Registry of Open Data on AWS

The Human Sleep Project

The Human Sleep Project (HSP) sleep physiology dataset is a growing collection of clinical polysomnography (PSG) recordings. Beginning with PSG recordings from from ~15K patients evaluated at the Massachusetts General Hospital, the HSP will grow over the coming years to include data from >200K patients, as well as people evaluated outside of the clinical setting. This data is being used to develop CAISR (Complete AI Sleep Report), a collection of deep neural networks, rule-based algorithms, and signal processing approaches designed to provide better-than-human detection of conventional PSG scoring metrics, including sleep stages, arousals, apnea and hypopnea events and their subtypes, and periodic limb movements. Beyond conventional scoring, the HSP dataset is intended to support research seeking to identify "hidden" information within the brain's activity during sleep that can be used to directly measure brain health. These brain health indicators include measures of risk for common neurologic diseases, including cerebrovascular disease, and Alzheimer's disease and related neurodegenerative diseases of aging; and indicators of response to therapies, including lifestyle interventions (e.g. diet, meditation, exercise) and pharmacologic interventions. These data are shared via the BDSP (Brain Data Science Platform, a resource developed by an international coalition of investigators that aggregates and harmonizes a wide range of large-scale human clinical neuroscience data to support research aimed at improving diagnosis, treatment, and prevention of neurologic disease, and promotion of brain health. The summary data provided here are released for the benefit of the wider scientific community without restriction on use.

Common Crawl

A corpus of web crawl data composed of over 300 billion web pages.

The Cancer Genome Atlas

The Cancer Genome Atlas (TCGA), a collaboration between the National Cancer Institute (NCI) and National Human Genome Research Institute (NHGRI), aims to generate comprehensive, multi-dimensional maps of the key genomic changes in major types and subtypes of cancer. TCGA has analyzed matched tumor and normal tissues from 11,000 patients, allowing for the comprehensive characterization of 33 cancer types and subtypes, including 10 rare cancers. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA Expression Quantification, Genotyping Array Copy Number Segment, Genotyping Array Masked Copy Number Segment, Genotyping Array Gene Level Copy Number Scores, and WXS Masked Somatic Mutation data from Genomic Data Commons (GDC). This dataset also contains controlled Whole Exome Sequencing (WXS), RNA-Seq, miRNA-Seq, ATAC-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and WXS Aggregated Somatic Mutation data from GDC. TCGA is made available on AWS via the NIH STRIDES Initiative.

CCRS MODIS albedo over Canada | Albédo MODIS du CCT couvrant le Canada

Times series of 10-day spectral and broadband albedo products derived at 250-m spatial resolution over Canadian territory and neighboring areas produced at the Canada Centre for Remote Sensing (CCRS) since February 2000 using MODIS L1B C6.1 swath imagery as input. The imagery for all spectral bands was downscaled and re-projected into the Lambert Conformal Conic (LCC) projection at 250-m spatial resolution. The area size is 5,700 km x 4,800 km (22,800 pixel x 19,200 lines). Séries temporelles de produits d’albédo spectral et à large bande générés à des intervalles de 10 jours avec une résolution spatiale de 250 m, couvrant le territoire canadien et les régions voisines. Ces produits sont élaborés par le Centre canadien de télédétection (CCT) depuis février 2000 à partir des images MODIS L1B C6.1. Les images de toutes les bandes spectrales ont été rééchantillonnées et reprojetées en projection conforme de Lambert (LCC) à une résolution spatiale de 250 m. La zone couverte est d’environ 5 700 km par 4 800 km (22 800 pixels par 19 200 lignes).

Foldingathome COVID-19 Datasets

Folding@home is a massively distributed computing project that uses biomolecular simulations to investigate the molecular origins of disease and accelerate the discovery of new therapies. Run by the Folding@home Consortium, a worldwide network of research laboratories focusing on a variety of different diseases, Folding@home seeks to address problems in human health on a scale that is infeasible by another other means, sharing the results of these large-scale studies with the research community through peer-reviewed publications and publicly shared datasets. During the COVID-19 epidemic, Folding@home focused its resources on understanding the vulnerabilities in SARS-CoV-2, the virus that causes COVID-19 disease, and working closely with a number of experimental collaborators to accelerate progress toward effective therapies for treating COVID-19 and ending the pandemic. In the process, it created the world's first exascale distributed computing resource, enabling it to generate valuable scientific datasets of unprecedented size. More information about Folding@home's COVID-19 research activities at the Folding@home COVID-19 page. In addition to working directly with experimental collaborators and rapidly sharing new research findings through preprint servers, Folding@home has joined other researchers in committing to rapidly share all COVID-19 research data, and has joined forces with AWS and the Molecular Sciences Software Institute (MolSSI) to share datasets of unprecedented side through the AWS Open Data Registry, indexing these massive datasets via the MolSSI COVID-19 Molecular Structure and Therapeutics Hub. The complete index of all Folding@home datasets can be found here. This repository contains several major datasets from this effort and comprises the single largest collection of molecular simulation data ever released.

Sentinel-2

Therapeutically Applicable Research to Generate Effective Treatments (TARGET)

Therapeutically Applicable Research to Generate Effective Treatments (TARGET) is the collaborative effort of a large, diverse consortium of extramural and NCI investigators. The goal of the effort is to accelerate molecular discoveries that drive the initiation and progression of hard-to-treat childhood cancers and facilitate rapid translation of those findings into the clinic. TARGET projects provide comprehensive molecular characterization to determine the genetic changes that drive the initiation and progression of childhood cancers.The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, miRNA-Seq miRNA Expression Quantification data from Genomic Data Commons (GDC), and open data from GDC Legacy Archive.

USGS Landsat

This joint NASA/USGS program provides the longest continuous space-based record of Earth’s land in existence. Every day, Landsat satellites provide essential information to help land managers and policy makers make wise decisions about our resources and our environment. Data is provided for Landsats 1, 2, 3, 4, 5, 7, 8, and 9 (excludes Landsat 6).As of June 28, 2023 (announcement), the previous single SNS topic arn:aws:sns:us-west-2:673253540267:public-c2-notify was replaced with three new SNS topics for different types of scenes.

Allen Cell Imaging Collections

This bucket contains multiple datasets (as Quilt packages) created by the Allen Institute for Cell Science. The types of data included in this bucket are listed below:

Field of view or cropped images of cells
Segmentations of structures in the images (e.g., boundaries of cells, DNA, other intracellular structures, etc.)
Processed versions of the above images and segmentations
Machine learning predictions and labels of the data listed above
Models trained on the previously listed data
Additional supporting non-image data related to the above listed data types (e.g., gene expression data, whole genome sequencing data, features derived from the images or model predictions, metadata)
Simulation, analysis, and visualization data of in silico cell structures, cells, and cell populations

External funding:The generation of some datasets was supported by the National Human Genome Research Institute of the National Institutes under Award Number UM1HG011593. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

Sudachi Language Resources

Japanese dictionaries and pre-trained models (word embeddings and language models) for natural language processing. SudachiDict is the dictionary for a Japanese tokenizer (morphological analyzer) Sudachi. chiVe is Japanese pretrained word embeddings (word vectors), trained using the ultra-large-scale web corpus NWJC by National Institute for Japanese Language and Linguistics, analyzed by Sudachi. chiTra is a library for using large-scale pre-trained language models with the Japanese tokenizer SudachiPy.

CELLxGENE Discover Census

CELLxGENE Discover (cellxgene.cziscience.com) is a free-to-use platform for the exploration, analysis, and retrieval of single-cell data. CELLxGENE Discover hosts the largest aggregation of standardized single-cell data from the major human and mouse tissues, with modalities that include gene expression, chromatin accessibility, DNA methylation, and spatial transcriptomics. This year, CELLxGENE Discover has made available all of its human and mouse RNA single-cell data through Census (https://chanzuckerberg.github.io/cellxgene-census/) – a free-to-use service with an API and data that allows for querying its single-cell data corpus directly from Python or R. The API uses a new technology, TileDB-SOMA, that allows for efficient and low-latency querying. The data are fully standardized and hosted publicly for free access, and they are composed by a count matrix of tens of millions of cells (observations) by >60 k genes (features) accompanied by standard cell metadata variables (e.g. cell type, tissue, sequencing technology, donor id, etc) and gene metadata that includes GENCODE-based IDs and gene names. While these data are built from hundreds of datasets, the APIs enable convenient cell- and gene-based filtering to obtain any slice of interest in a matter of seconds. All data can be quickly transformed to NumPy, Pandas, Anndata, Seurat, or R base objects. We created data loaders for the data to be directly used by PyTorch for modeling at scale. In addition, all the source dataset files in H5AD format are also available for retrieval.

Gabriella Miller Kids First Pediatric Research Program (Kids First)

The NIH Common Fund's Gabriella Miller Kids First Pediatric Research Program’s (“Kids First”) vision is to “alleviate suffering from childhood cancer and structural birth defects by fostering collaborative research to uncover the etiology of these diseases and by supporting data sharing within the pediatric research community.” The program continues to generate and share whole genome sequence data from thousands of children affected by these conditions, ranging from rare pediatric cancers, such as osteosarcoma, to more prevalent diagnoses, such as congenital heart defects. In 2018, Kids First launched the Gabriella Miller Kids First Data Resource Center, charged with building a large-scale data platform supporting clinical and genetic data from these patients and their families in order to accelerate discovery and ultimately clinical impact. Researchers can search, access, aggregate, and analyze these data through the Kids First Data Resource Portal. Additionally, by using cloud-based individual workspaces in CAVATICA, a data analysis and sharing computation platform, researchers can cross-analyze Kids First data with data from other efforts, such as NCI’s TARGET program and consortia-based datasets like the Children’s Brain Tumor Tissue Consortium (CBTTC). Kids First is made available on AWS via the NIH STRIDES Initiative.

NOAA Geostationary Operational Environmental Satellites (GOES) 16, 17, 18 & 19

NEW GOES-19 Data!! On April 4, 2025 at 1500 UTC, the GOES-19 satellite will be declared the Operational GOES-East satellite. All products and services, including NODD, for GOES-East will transition to GOES-19 data at that time. GOES-19 will operate out of the GOES-East location of 75.2°W starting on April 1, 2025 and through the operational transition. Until the transition time and during the final stretch of Post Launch Product Testing (PLPT), GOES-19 products are considered non-operational regardless of their validation maturity level. Shortly following the transition of GOES-19 to GOES-East, all data distribution from GOES-16 will be turned off. GOES-16 will drift to the storage location at 104.7°W. GOES-19 data should begin flowing again on April 4th once this maneuver is complete.

NEW GOES 16 Reprocess Data!! The reprocessed GOES-16 ABI L1b data mitigates systematic data issues (including data gaps and image artifacts) seen in the Operational products, and improves the stability of both the radiometric and geometric calibration over the course of the entire mission life. These data were produced by recomputing the L1b radiance products from input raw L0 data using improved calibration algorithms and look-up tables, derived from data analysis of the NIST-traceable, on-board sources. In addition, the reprocessed data products contain enhancements to the L1b file format, including limb pixels and pixel timestamps, while maintaining compatibility with the operational products. The datasets currently available span the operational life of GOES-16 ABI, from early 2018 through the end of 2024. The Reprocessed L1b dataset shows improvement over the Operational L1b products but may still contain data gaps or discrepancies. Please provide feedback to Dan Lindsey (dan.lindsey@noaa.gov) and Gary Lin (guoqing.lin-1@nasa.gov). More information can be found in the [GOES-R ABI Reprocess User Guide](https://github.com/NOAA-Big-Data-Program/nodd-data-docs/blob/main/GOES/GOES-R_ABI_Reprocessed_L1b_User_Guide-v1.1.pdf).

NOTICE: As of January 10th 2023, GOES-18 assumed the GOES-West position and all data files are deemed both operational and provisional, so no ‘preliminary, non-operational’ caveat is needed. GOES-17 is now offline, shifted approximately 105 degree West, where it will be in on-orbit storage. GOES-17 data will no longer flow into the GOES-17 bucket. Operational GOES-West products can be found in the GOES-18 bucket.

GOES satellites (GOES-16, GOES-17, GOES-18 & GOES-19) provide continuous weather imagery and monitoring of meteorological and space environment data across North America. GOES satellites provide the kind of continuous monitoring necessary for intensive data analysis. They hover continuously over one position on the surface. The satellites orbit high enough to allow for a full-disc view of the Earth. Because they stay above a fixed spot on the surface, they provide a constant vigil for the atmospheric "triggers" for severe weather conditions such as tornadoes, flash floods, hailstorms, and hurricanes. When these conditions develop, the GOES satellites are able to monitor storm development and track their movements. SUVI products available in both NetCDF and FITS.

Sentinel-2 Cloud-Optimized GeoTIFFs

The Sentinel-2 mission is a land monitoring constellation of two satellites that provide high resolution optical imagery and provide continuity for the current SPOT and Landsat missions. The mission provides a global coverage of the Earth's land surface every 5 days, making the data of great use in ongoing studies. This dataset is the same as the Sentinel-2 dataset, except the JP2K files were converted into Cloud-Optimized GeoTIFFs (COGs). Additionally, SpatioTemporal Asset Catalog metadata has were in a JSON file alongside the data, and a STAC API called Earth-search is freely available to search the archive. This dataset contains all of the scenes in the original Sentinel-2 Public Dataset and will grow as that does. L2A data are available from April 2017 over wider Europe region and globally since December 2018.

Terrain Tiles

A global dataset providing bare-earth terrain heights, tiled for easy usage and provided on S3.

NASA Prediction of Worldwide Energy Resources (POWER)

NASA's goal in Earth science is to observe, understand, and model the Earth system to discover how it is changing, to better predict change, and to understand the consequences for life on Earth. The Applied Sciences Program, within the Earth Science Division of the NASA Science Mission Directorate, serves individuals and organizations around the globe by expanding and accelerating societal and economic benefits derived from Earth science, information, and technology research and development.

The Prediction Of Worldwide Energy Resources (POWER) Project, funded through the Applied Sciences Program at NASA Langley Research Center, gathers NASA Earth observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in energy development, building energy efficiency, and supporting agriculture projects.

The POWER project contains over 380 satellite-derived meteorology and solar energy Analysis Ready Data (ARD) at four temporal levels: hourly, daily, monthly, and climatology. The POWER data archive provides data at the native resolution of the source products. The data is updated nightly to maintain near real time availability (2-3 days for meteorological parameters and 5-7 days for solar). The POWER services catalog consists of a series of RESTful Application Programming Interfaces, geospatial enabled image services, and web mapping Data Access Viewer. These three service offerings support data discovery, access, and distribution to the project’s user base as ARD and as direct application inputs to decision support tools.

The latest data version update includes hourly-based source ARD, in addition to enhanced daily, monthly, annual, and climatology data. The daily time series for meteorology is available from 1981, while solar-based parameters start in 1984. The hourly source data are from Clouds and the Earth's Radiant Energy System (CERES) and Global Modeling and Assimilation Office (GMAO), spanning from 1984 for meteorology and from 2001 for solar-based parameters. The hourly data equips users with the ARD needed to model building system energy performance, providing information directly amenable to decision support tools introducing the industry standard EnergyPlus Weather file format.

NEXRAD on AWS

Real-time and archival data from the Next Generation Weather Radar (NEXRAD) network.

Update

The NEXRAD Level II archive data is moving to a new bucket: unidata-nexrad-level2 and SNS topic: arn:aws:sns:us-east-1:684042711724:NewNEXRADLevel2Archive. The old bucket and SNS topic are now deprecated and will no longer be available starting September 1, 2025.

1000 Genomes Phase 3 Reanalysis with DRAGEN 3.5, 3.7, 4.0, 4.2, and 4.4

Overview

This dataset contains alignment files and small variant (includes single nucleotide variants (SNV) and indels), copy number variant (CNV), short tandem repeat (i.e., repeat expansion; STR), structural variant (SV) and other variant call files from the 1000 Genomes Project (1KGP) Phase 3 dataset (3,202 individuals, 602 trios) using Illumina DRAGEN v3.5.7b, v3.7.6, v4.0.3, v4.2.7, and v4.4.7 software. All DRAGEN analyses were performed in the cloud using the Illumina Connected Analytics bioinformatics platform powered by Amazon Web Services (see 'Data solution empowering population genomics' for more information). The v3.7.6, v4.2.7, and v4.4.7 datasets include results from trio small variant, de novo structural variant, and de novo copy number variant calls on 602 trio families comprised of members from the 1KGP Phase 3 dataset. Trio repeat expansion calling was included in the v3.7.6 dataset only. Joint cohort analysis was also performed on the entire 1KGP sample dataset for the v3.7.6, v4.0.3, v4.2.7, and v4.4.7 re-analyses using DRAGEN Iterative gVCF Genotyper v3.8.3, v4.2.0, v4.2.7, v4.4.7, respectively (see 'Genotyping variants at population scale using DRAGEN gVCF Genotyper' and 'Population Genotyping'). DRAGEN Versions

v3.7

User Guide | Release NotesImprovements and new features in the v3.7.6 individual samples analyses include CYP2D6 variant calling (see 'Overcoming high homology to detect variation in CYP21A2 with whole-genome sequencing in DRAGEN') and joint detection and use of graph-based hg19 and hg38 reference hash tables (see 'DRAGEN Wins at PrecisionFDA Truth Challenge V2 Showcase Accuracy Gains from Alt-aware Mapping and Graph Reference Genomes' and 'Demystifying the versions of GRCh38/hg38 reference genomes, how they are used in DRAGEN and their impact on accuracy' for details).

v4.0

User Guide | Release NotesThe DRAGEN v4.0.3 dataset features improved small variant calling accuracy due to utilization of a newly integrated machine learning functionality with an updated graph based reference for difficult to map regions (see 'DRAGEN Sets New Standard for Data Accuracy in PrecisionFDA Benchmark Data. Optimizing Variant Calling Performance with Illumina Machine Learning and DRAGEN Graph'); accuracy and runtime improvements in the SV caller; new targeted callers including CYP2B6, GBA, SMN and a Star Allele PGx caller; and an expanded catalog for use with Expansion Hunter STR caller.

v4.2

User Guide | Release NotesDRAGEN v4.2.7 offers significant accuracy improvements in small variant, CNV, and SV calling, includes new targeted callers (HBA, LPA, RH, CYP21A2, SMN silent carrier variant), and supports Star Allele calling for five additional pharmacogenes (BCHE, ABCG2, NAT2, F5, and UGT2B17). These are further improved by upgraded machine learning models. See DRAGEN 4.2: Enhanced machine learning, new targeted callers, and more for further details on these and other enchancements.

v4.4

User Guide | Release NotesDRAGEN v4.4.7 boosts the speed and accuracy of all callers via the official release of an optimized pangenome graph reference ('The quest for accuracy gains in the dark regions of the genomes: Presenting the DRAGEN multigenome mapper and pangenome reference updates in version 4.3'). Namely, SV calling accuracy is substantially increased via the implementation of a multigenome mapper capable of exploiting the power of a pangenome reference. Runtime is further reduced by supporting AWS F2 EC2 instances (Enabling Rapid Genomic and Multiomic Data Analysis with Illumina DRAGEN™ v4.4 on Amazon EC2 F2 Instances) Annotation

Starting with the v4.0.3 reanalysis, annotation using the Illumina Connected Annotations (also known as Illumina Annotation Engine or Nirvana) was included as part of the analysis (see Illumina Connected Annotations documentation for more information). For the v4.0.3, v4.2.7, and v4.4.7 datasets, annotation was performed on the merged small variant VCF generated by the DRAGEN Iterative gVCF Genotyper for the entire 1KGP cohort. For v4.2.7 and v4.4.7, annotation was also performed on the merged CNV, SV, and STR VCFs for the entire cohort.

Cell Painting Gallery

The Cell Painting Gallery is a collection of image datasets created using the Cell Painting assay. The images of cells are captured by microscopy imaging, and reveal the response of various labeled cell components to whatever treatments are tested, which can include genetic perturbations, chemicals or drugs, or different cell types. The datasets can be used for diverse applications in basic biology and pharmaceutical research, such as identifying disease-associated phenotypes, understanding disease mechanisms, and predicting a drug’s activity, toxicity, or mechanism of action (Chandrasekaran et al 2020). This collection is maintained by the Carpenter–Singh lab and the Cimini lab at the Broad Institute. A human-friendly listing of datasets, instructions for accessing them, and other documentation is at the corresponding GitHub page about the Gallery.

Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE)

The Inter-mission Time Series of Land Ice Velocity and Elevation (ITS_LIVE) project has a singular mission: to accelerate ice sheet and glacier research by producing globally comprehensive, high resolution, low latency, temporally dense, multi-sensor records of land ice and ice shelf change while minimizing barriers between the data and the user. ITS_LIVE data currently consists of NetCDF Level 2 scene-pair ice flow products posted to a standard 120 m grid derived from Landsat 4/5/7/8/9, Sentinel-2 optical scenes, and Sentinel-1 SAR scenes. We have processed all land-ice intersecting image pairs separated by ≤546 days for optical and ≤12 for SAR sensors, for all missions from each mission start to the end of 2022. The ITS_LIVE project will continue processing new optical and radar scene pairs as they come in, and will soon increase production to include SAR image pairs separated by more than 12 days. We also expect to extend processing to include ASTER, RADARSAT, and NISAR imagery. Further, all scene pair data are combined into a set of Zarr datacubes that are cloud-optimized for time-series analysis, with a suite of user tools available to query the data effectively. The ITS_LIVE project is also producing a suite of Level 3 products, including quarterly and annual ice-sheet and regional velocity mosaics as Cloud-Optimized GeoTIFFs, standardized records of elevation change rates for Greenland and Antarctica, and ice shelf and tidewater glacier front positions. For more information about the ITS_LIVE project, please see https://its-live.jpl.nasa.gov/.

MERRA-2 tavg1_2d_slv_Nx: 2d,1-Hourly,Time-Averaged,Single-Level,Assimilation,Single-Level Diagnostics 0.625 x 0.5 degree

M2T1NXSLV (or tavg1_2d_slv_Nx) is an hourly time-averaged 2-dimensional data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of meteorology diagnostics at popularly used vertical levels, such as air temperature at 2-meter (or at 10-meter, 850hPa, 500 hPa, 250hPa), wind components at 50-meter (or at 2-meter, 10-meter, 850 hPa, 500hPa, 250 hPa), sea level pressure, surface pressure, and total precipitable water vapor (or ice water, liquid water). The data field is time-stamped with the central time of an hour starting from 00:30 UTC, e.g.: 00:30, 01:30, … , 23:30 UTC.MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov). Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

ESA WorldCover

The European Space Agency (ESA) WorldCover product provides global land cover maps for 2020 & 2021 at 10 m resolution based on Copernicus Sentinel-1 and Sentinel-2 data. The WorldCover product comes with 11 land cover classes and has been generated in the framework of the ESA WorldCover project, part of the 5th Earth Observation Envelope Programme (EOEP-5) of the European Space Agency. A first version of the product (v100), containing the 2020 map was released in October 2021. The 2021 map was released in October 2022 using an improved algorithm (v200). The WorldCover 2020 and 2021 maps were generated with different algorithm versions and therefore changes between the maps should be treated with caution as these contain both real land cover changes as well as changes due to the used algorithms.

Genome Aggregation Database (gnomAD)

The Genome Aggregation Database (gnomAD) is a resource developed by an international coalition of investigators that aggregates and harmonizes both exome and genome data from a wide range of large-scale human sequencing projects. The summary data provided here are released for the benefit of the wider scientific community without restriction on use. The v4.1 data set (GRCh38) spans 730,947 exome sequences and 76,215 whole-genome sequences from unrelated individuals, of diverse ancestries, sequenced sequenced as part of various disease-specific and population genetic studies. The gnomAD Principal Investigators and team can be found here, and the groups that have contributed data to the current release are listed here. Sign up for the gnomAD mailing list here.

GeoNet Aotearoa New Zealand Data

GeoNet provides geological hazard information for Aotearoa New Zealand. This dataset contains data and products recorded by the GeoNet sensor network.

GNSS (Global Navigation Satellite System) data include raw data in proprietary and Receiver Independent Exchange Format (RINEX) and local tie-in survey conducted during equipment changes, more details can be found on the GeoNet geodetic page website.
Coastal gauge data include relative measurement of sea level measured by tsunami monitoring gauges. Raw and quality control data are provided in CREX format (Character Form for the Representtion and eXchange of metereological data), more details can be found on the GeoNet coastal tsunami monitoring gauges page.
Camera images data include webcam images from the GeoNet Volcano monitoring network and Built Environment Instrumentation Programme, more details can be found on the GeoNet camera page.
Waveform data include raw data from weak and strong motion instruments of the GeoNet seismic networks, more details can be found on the GeoNet seismic waveform page.
Seismic data products include strong motion derived data, more details can be found on the GeoNet Strong Motion products page.
Time Series data products include derived time series data from a subgroup of the GeoNet sensor network. Data are in compressed comma separated format (csv.gz), more details can be found on the GeoNet tilde website page.

MERRA-2 inst3_3d_aer_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Aerosol Mixing Ratio 0.625 x 0.5 degree

M2I3NVAER (or inst3_3d_aer_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of aerosol mixing ratio parameters at 72 model layers, such as dust, sulphur dioxide, sea salt, black carbon, and organic carbon. The data field is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov). Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

MERRA-2 inst3_3d_asm_Np: 3d,3-Hourly,Instantaneous,Pressure-Level,Assimilation,Assimilated Meteorological Fields

M2I3NPASM (or inst3_3d_asm_Np) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 42 pressure levels, such as temperature, wind components, vertical pressure velocity, water vapor, ozone mass mixing ratio, and layer height. The data field is available every three hours starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. The information on the pressure levels can be found in the section 4.2 of the MERRA-2 File Specification document. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov). Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

MERRA-2 inst3_3d_asm_Nv: 3d,3-Hourly,Instantaneous,Model-Level,Assimilation,Assimilated Meteorological Fields 0.625 x 0.5 degree

M2I3NVASM (or inst3_3d_asm_Nv) is an instantaneous 3-dimensional 3-hourly data collection in Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2). This collection consists of assimilations of meteorological parameters at 72 model layers, such as temperature, wind components, vertical pressure velocity, water vapor, and layer height. The data field is available every three hour starting from 00:00 UTC, e.g.: 00:00, 03:00, … , 21:00 UTC. Section 4.2 of the MERRA-2 File Specification document provides pressure values nominal for a 1000 hPa surface pressure and refers to the top edge of the layer. The lev=1 is for the top layer, and lev=72 is for the bottom (or surface) model layer. MERRA-2 is the latest version of global atmospheric reanalysis for the satellite era produced by NASA Global Modeling and Assimilation Office (GMAO) using the Goddard Earth Observing System Model (GEOS) version 5.12.4. The dataset covers the period of 1980-present with the latency of ~3 weeks after the end of a month. Data Reprocessing: Please check “Records of MERRA-2 Data Reprocessing and Service Changes” linked from the “Documentation” tab on this page. Note that a reprocessed data filename is different from the original file.MERRA-2 Mailing List: Sign up to receive information on reprocessing of data, changing of tools and services, as well as data announcements from GMAO. Contact the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov) to be added to the list.Questions: If you have a question, please read "MERRA-2 File Specification Document", “MERRA-2 Data Access – Quick Start Guide”, and FAQs linked from the ”Documentation” tab on this page. If that does not answer your question, you may post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov). Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

NOAA Joint Polar Satellite System (JPSS)

Near Real Time JPSS data is now flowing! See bucket information on the right side of this page to access products!
Satellites in the JPSS constellation gather global measurements of atmospheric, terrestrial and oceanic conditions, including sea and land surface temperatures, vegetation, clouds, rainfall, snow and ice cover, fire locations and smoke plumes, atmospheric temperature, water vapor and ozone. JPSS delivers key observations for the Nation's essential products and services, including forecasting severe weather like hurricanes, tornadoes and blizzards days in advance, and assessing environmental hazards such as droughts, forest fires, poor air quality and harmful coastal waters. Further, JPSS will provide continuity of critical, global observations of Earth’s atmosphere, oceans and land through 2038.

SpaceNet

SpaceNet, launched in August 2016 as an open innovation project offering a repository of freely available imagery with co-registered map features. Before SpaceNet, computer vision researchers had minimal options to obtain free, precision-labeled, and high-resolution satellite imagery. Today, SpaceNet hosts datasets developed by its own team, along with data sets from projects like IARPA’s Functional Map of the World (fMoW).

The Singapore Nanopore Expression Data Set

The Singapore Nanopore Expression (SG-NEx) project is an international collaboration to generate reference transcriptomes and a comprehensive benchmark data set for long read Nanopore RNA-Seq. Transcriptome profiling is done using PCR-cDNA sequencing (PCR-cDNA), amplification-free cDNA sequencing (direct cDNA), direct sequencing of native RNA (direct RNA), and short read RNA-Seq. The SG-NEx core data includes 5 of the most commonly used cell lines and it is extended with additional cell lines and samples that cover a broad range of human tissues. All core samples are sequenced with at least 3 high quality replicates. For a subset of samples spike-in RNAs are used and matched m6A profiling data is available.

2021 Amazon Last Mile Routing Research Challenge Dataset

The 2021 Amazon Last Mile Routing Research Challenge was an innovative research initiative led by Amazon.com and supported by the Massachusetts Institute of Technology’s Center for Transportation and Logistics. Over a period of 4 months, participants were challenged to develop innovative machine learning-based methods to enhance classic optimization-based approaches to solve the travelling salesperson problem, by learning from historical routes executed by Amazon delivery drivers. The primary goal of the Amazon Last Mile Routing Research Challenge was to foster innovative applied research in route planning, building on recent advances in predictive modeling, and using a real-world problem and data. The dataset released for the research challenge includes route-, stop-, and package-level features for 9,184 historical routes performed by Amazon drivers in 2018 in five metropolitan areas in the United States. This real-world dataset excludes any personally identifiable information (all route and package identifiers have been randomly regenerated and related location data have been obfuscated to ensure anonymity). Although multiple synthetic benchmark datasets are available in the literature, the dataset of the 2021 Amazon Last Mile Routing Research Challenge is the first large and publicly available dataset to include instances based on real-world operational routing data. The dataset is fully described and formally introduced in the following Transportation Science article: https://pubsonline.informs.org/doi/10.1287/trsc.2022.1173

Distributed Archives for Neurophysiology Data Integration (DANDI)

DANDI is a public archive of neurophysiology datasets, including raw and processed data, and associated software containers. Datasets are shared according to Creative Commons CC0 or CC-BY licenses. This US BRAIN Initiative supported archive provides a broad range of cellular neurophysiology data including intracellular and extracellular electrophysiology, optophysiology, calcium imaging, fiber photometry, behavioral time-series, and images from immunostaining experiments, from over 20 species.Data is organized using community standards: NWB - Neurodata Without Borders, BIDS - Brain Imaging Data Structure, NGFF - Next Generation File Format for Zarr-based imaging data, and NIDM - Neuro Imaging Data Model.The S3 bucket is organized as follows:

dandisets/ - Metadata and manifests for each Dandiset version; manifests reference keys under blobs/ or zarrs/ for actual data. See DANDI schema for manifest format specifications.
blobs/ - Deduplicated binary data (NWB files) indexed by content hash.
zarrs/ - Zarr arrays for large imaging datasets.

Development of DANDI is supported by the National Institute of Mental Health.

Digital Earth Africa Global Mangrove Watch

The Global Mangrove Watch (GMW) dataset is a result of the collaboration between Aberystwyth University (U.K.), solo Earth Observation (soloEO; Japan), Wetlands International the World Conservation Monitoring Centre (UNEP-WCMC) and the Japan Aerospace Exploration Agency (JAXA). The primary objective of producing this dataset is to provide countries lacking a national mangrove monitoring system with first cut mangrove extent and change maps, to help safeguard against further mangrove forest loss and degradation. The Global Mangrove Watch dataset (version 2) consists of a global baseline map of mangroves for 2010 and changes from this baseline for six epochs i.e. 1996, 2007, 2008, 2009, 2015 and 2016. Annual maps are planned from 2018 and onwards. The dataset can be used to identify mangrove ecosystems and monitor changes in mangrove extent. This is important in applications such as quantifying ‘blue carbon’, mitigating risks from natural disasters, and prioritising restoration activities. For more information on the Global Watch Mangrove product see the Global Mangrove Watch website.

Digital Earth Africa Landsat Collection 2 Level 2

Digital Earth Africa (DE Africa) provides free and open access to a copy of Landsat Collection 2 Level-2 products over Africa. These products are produced and provided by the United States Geological Survey (USGS). The Landsat series of Earth Observation satellites, jointly led by USGS and NASA, have been continuously acquiring images of the Earth’s land surface since 1972. DE Africa provides data from Landsat 5, 7 and 8 satellites, including historical observations dating back to late 1980s and regularly updated new acquisitions. New Level-2 Landsat 7 and Landsat 8 data are available after 15 to 27 days from acquisition. See Landsat Collection 2 Generation Timeline for details. USGS Landsat Collection 2 was released early 2021 and offers improved processing, geometric accuracy, and radiometric calibration compared to previous Collection 1 products. The Level-2 products are endorsed by the Committee on Earth Observation Satellites (CEOS) to be Analysis Ready Data for Land (CARD4L)-compliant. This internationally recognized certification ensures these products have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets.

Fly Brain Anatomy: FlyLight Gen1 and Split-GAL4 Imagery

This data set, made available by Janelia's FlyLight project, consists of fluorescence images of Drosophila melanogaster driver lines, aligned to standard templates, and stored in formats suitable for rapid searching in the cloud. Additional data will be added as it is published.

RADIANT Public Data

The Real-time Analysis and Discovery in Integrated And Networked Technologies (RADIANT) initiative seeks to develop an extensible, federated framework for rapid exchange of multimodal clinical and research data on behalf of accelerated discovery and patient impact. Coordination and implementation of initial RADIANT deployments will leverage a network of more than 35 partnered health care systems and participating patient families within the Children’s Brain Tumor Network (CBTN) and the Pediatric Neuro-Oncology Consortium (PNOC). This data set is composed of public multi-modal data provisioned by RADIANT. The initial bolus of data is from CBTN and consists of clinical data extracted/abstracted from electronic medical records, omic data such as genomics, transcriptomics and proteomics and radiology and pathology imaging data. Data are collected or generated as part of consent-based, IRB-approved observational or interventional studies with the goal of making it available globally to researchers across a broad number of disciplines.

Digital Earth Africa - Copernicus Global Land Service - Lake Water Quality

The Copernicus Global Land Service – Lake Water Quality products offer a comprehensive, satellite-derived monitoring system for assessing key water quality indicators in major large lakes, typically those greater than 50 hectares. These datasets are generated using optical satellite sensors, primarily Sentinel-2 MSI and Sentinel-3 OLCI, with earlier archives derived from Envisat MERIS. Spanning multiple spatial resolutions (100 m and 300 m) and temporal scales (10-day composites), they support both near-real-time and retrospective assessments of inland water quality.Key parameters include surface reflectance, turbidity, total suspended matter (TSM), chlorophyll-a concentration, trophic state index, and floating cyanobacteria risk—all essential for monitoring eutrophication, ecological health, and harmful algal blooms (HABs). The datasets cover the period from 2002 to the present, providing long-term continuity for environmental monitoring and scientific research, with focused coverage in Europe and Africa.All products are delivered using standardized geospatial grids (EPSG:4326) and include quality flags, detailed metadata, and validation against in situ observations to ensure reliability. Continuous improvements across product versions—such as enhanced atmospheric correction and updated retrieval algorithms—have significantly improved accuracy and usability. In addition, comprehensive user manuals, technical documentation, and support materials are available, making the data highly accessible to researchers, policymakers, and environmental managers.Digital Earth Africa (DE Africa) hosts these datasets for the African region, providing free and open access to both the data and associated tools.

Digital Earth Africa CHIRPS Rainfall

Digital Earth Africa (DE Africa) provides free and open access to a copy of the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) monthly and daily products over Africa. The CHIRPS rainfall maps are produced and provided by the Climate Hazards Center in collaboration with the US Geological Survey, and use both rain gauge and satellite observations. The CHIRPS-2.0 Africa Monthly dataset is regularly indexed to DE Africa from the CHIRPS monthly data. The CHIRPS-2.0 Africa Daily dataset is likewise indexed from the CHIRPS daily data. Both products have been converted to cloud-opitmized GeoTIFFs, and can be accessed through DE Africa’s Open Data Cube. This means the full archive of CHIRPS daily and monthly rainfall can be easily used for inspection or analysis across DE Africa platforms, including the user-interactive DE Africa Map. For more information on the dataset, see the CHIRPS website.

Digital Earth Africa Coastlines

Africa's long and dynamic coastline is subject to a wide range of pressures, including extreme weather and climate, sea level rise and human development. Understanding how the coastline responds to these pressures is crucial to managing this region, from social, environmental and economic perspectives. The Digital Earth Africa Coastlines (provisional) is a continental dataset that includes annual shorelines and rates of coastal change along the entire African coastline from 2000 to the present. The product combines satellite data from the Digital Earth Africa program with tidal modelling to map the typical location of the coastline at mean sea level for each year. The product enables trends of coastal erosion and growth to be examined annually at both a local and continental scale, and for patterns of coastal change to be mapped historically and updated regularly as data continues to be acquired. This allows current rates of coastal change to be compared with that observed in previous years or decades. The ability to map shoreline positions for each year provides valuable insights into whether changes to the coastline are the result of particular events or actions, or a process of more gradual change over time. This information can enable scientists, managers and policy makers to assess impacts from the range of drivers impacting the coastlines and potentially assist planning and forecasting for future scenarios.

Digital Earth Africa GeoMAD

GeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes. The geomedian component combines measurements collected over the specified timeframe to produce one representative, multispectral measurement for every pixel unit of the African continent. The end result is a comprehensive dataset that can be used to generate true-colour images for visual inspection of anthropogenic or natural landmarks. The full spectral dataset can be used to develop more complex algorithms. For each pixel, invalid data is discarded, and remaining observations are mathematically summarised using the geomedian statistic. Flyover coverage provided by collecting data over a period of time also helps scope intermittently cloudy areas. Variations between the geomedian and the individual measurements are captured by the three Median Absolute Deviation (MAD) layers. These are higher-order statistical measurements calculating variation relative to the geomedian. The MAD layers can be used on their own or together with geomedian to gain insights about the land surface, and understand change over time. Calculating GeoMAD over different timeframes and sensors provides a range of insights to the environment. An annual timeframe allows better correction for cloud cover and reduces artifacts for comparison over multiple years. A semiannual timeframe, for example six-month blocks, better captures seasonal variation within one year, but can also be used to compare equivalent periods from different years. Likewise, Landsat sensors allows full utility of the surface reflectance archive dating back to 1984, while more recent Sentinel-2 data provides higher-frequency flyovers and better resolution. The Digital Earth Africa GeoMAD service currently provides annual and six-month semiannual datasets, with separate services for Landsat and Sentinel-2 sensors.

Digital Earth Africa Sentinel-2 Level-2A Surface Reflectance Collection 1

The Sentinel-2 mission is part of the European Union Copernicus programme for Earth observations. Sentinel-2 consists of twin satellites, Sentinel-2A (launched 23 June 2015) and Sentinel-2B (launched 7 March 2017). The two satellites have the same orbit, but 180° apart for optimal coverage and data delivery. Their combined data is used in the Digital Earth Africa Sentinel-2 product. Together, they cover all Earth’s land surfaces, large islands, inland and coastal waters every 3-5 days. Sentinel-2 data is tiered by level of pre-processing. Level-0, Level-1A and Level-1B data contain raw data from the satellites, with little to no pre-processing. Level-1C data is surface reflectance measured at the top of the atmosphere. This is processed using the Sen2Cor algorithm to give Level-2A, the bottom-of-atmosphere reflectance (Obregón et al, 2019). Level-2A data is the most ideal for research activities as it allows further analysis without applying additional atmospheric corrections. Digital Earth Africa Sentinel-2 Level-2A Surface Reflectance Collection 1 is the Sentinel-2 product processed for enhanced calibration and consistent time series between Sentinel-2A and Sentinel-2B. Digital Earth Africa does not host any lower-level Sentinel-2 data.Note that this data is a subset of the Sentinel-2 COGs dataset.

Digital Earth Africa Water Observations from Space

Water Observations from Space (WOfS) is a service that draws on satellite imagery to provide historical surface water observations of the whole African continent. WOfS allows users to understand the location and movement of inland and coastal water present in the African landscape. It shows where water is usually present; where it is seldom observed; and where inundation of the surface has been observed by satellite. They are generated using the WOfS classification algorithm on Landsat satellite data. There are several WOfS products available for the African continent including scene-level data and annual or all time summaries.

International Neuroimaging Data-Sharing Initiative (INDI)

This bucket contains multiple neuroimaging datasets that are part of the International Neuroimaging Data-Sharing Initiative. Raw human and non-human primate neuroimaging data include 1) Structural MRI; 2) Functional MRI; 3) Diffusion Tensor Imaging; 4) Electroencephalogram (EEG) In addition to the raw data, preprocessed data is also included for some datasets. A complete list of the available datasets can be seen in the documentation lonk provided below.

Low Altitude Disaster Imagery (LADI) Dataset

The Low Altitude Disaster Imagery (LADI) Dataset consists of human and machine annotated airborne images collected by the Civil Air Patrol in support of various disaster responses from 2015-2023. Two key distinctions are the low altitude, oblique perspective of the imagery and disaster-related features, which are rarely featured in computer vision benchmarks and datasets.

Maxar Open Data Program

Pre and post event high-resolution satellite imagery in support of emergency planning, risk assessment, monitoring of staging areas and emergency response, damage assessment, and recovery. These images are generated using the Maxar ARD pipeline, tiled on an organized grid in analysis-ready cloud-optimized formats.

NOAA Operational Forecast System (OFS)

ANNOUNCEMENTS: [NOS OFS Version Updates and Implementation of Upgraded Oceanographic Forecast Modeling Systems for Lakes Superior and Ontario; Effective October 25, 2022}(https://www.weather.gov/media/notification/pdf2/scn22-91_nos_loofs_lsofs_v3.pdf)

For decades, mariners in the United States have depended on NOAA's Tide Tables for the best estimate of expected water levels. These tables provide accurate predictions of the astronomical tide (i.e., the change in water level due to the gravitational effects of the moon and sun and the rotation of the Earth); however, they cannot predict water-level changes due to wind, atmospheric pressure, and river flow, which are often significant.

The National Ocean Service (NOS) has the mission and mandate to provide guidance and information to support navigation and coastal needs. To support this mission, NOS has been developing and implementing hydrodynamic model-based Operational Forecast Systems.

This forecast guidance provides oceanographic information that helps mariners safely navigate their local waters. This national network of hydrodynamic models provides users with operational nowcast and forecast guidance (out to 48 – 120 hours) on parameters such as water levels, water temperature, salinity, and currents. These forecast systems are implemented in critical ports, harbors, estuaries, Great Lakes and coastal waters of the United States, and form a national backbone of real-time data, tidal predictions, data management and operational modeling.

Nowcasts and forecasts are scientific predictions about the present and future states of water levels (and possibly currents and other relevant oceanographic variables, such as salinity and temperature) in a coastal area. These predictions rely on either observed data or forecasts from a numerical model. A nowcast incorporates recent (and often near real-time) observed meteorological, oceanographic, and/or river flow rate data. A nowcast covers the period from the recent past (e.g., the past few days) to the present, and it can make predictions for locations where observational data are not available. A forecast incorporates meteorological, oceanographic, and/or river flow rate forecasts and makes predictions for times where observational data will not be available. A forecast is usually initiated by the results of a nowcast.

OFS generally runs four times per day (every 6 hours) on NOAA's Weather and Climate Operational Supercomputing Systems (WCOSS) in a standard Coastal Ocean Modeling Framework (COMF) developed by the Center for Operational Oceanographic Products and Services (CO-OPS). COMF is a set of standards and tools for developing and maintaining NOS’s hydrodynamic model–based operational forecast systems. The goal of COMF is to provide a standard and comprehensive software infrastructure to enhance ease of use, performance, portability, and interoperability of NOS’s operational forecast systems.

Open Targets

The Open Targets Platform is a comprehensive data integration tool that supports systematic identification and prioritisation of potential therapeutic drug targets. By integrating publicly available datasets including data generated by the Open Targets experimental and informatics research programmes, the Platform provides data and services to assist in the task of therapeutic hypothesis building.

The Cancer Dependency Map (DepMap) Cancer Cell Line Encyclopedia (CCLE) Dataset

This dataset consists of whole genome sequencing (WGS), whole exome sequencing (WES), and RNA sequencing files generated from ~1000 cancer cell lines described in Ghandi et al., 2019.

Alliance of Genome Resources

The Alliance of Genome Resources is a consortium that integrates genomic, genetic, and molecular data from leading model organism databases including Drosophila melanogaster, Caenorhabditis elegans, Danio rerio (zebrafish), Mus musculus (mouse), Rattus norvegicus (rat), Saccharomyces cerevisiae (yeast), Xenopus laevis and Xenopus tropicalis (frogs), and human reference data. The Alliance provides comprehensive datasets including gene annotations, disease associations, expression data (bulk and single-cell RNA-Seq), protein and genetic interactions, orthology relationships, variants and alleles, and complete genome sequences with annotations. Data is organized into Alliance-wide integrated datasets and organism-specific collections, supporting comparative genomics, disease modeling, and functional genomics research.

CBERS on AWS

Imagery acquired by the China-Brazil Earth Resources Satellite (CBERS), 4 and 4A. The image files are recorded and processed by Instituto Nacional de Pesquisas Espaciais (INPE) and are converted to Cloud Optimized Geotiff format in order to optimize its use for cloud based applications. Contains all CBERS-4 MUX, AWFI, PAN5M and PAN10M scenes acquired since the start of the satellite mission and is daily updated with new scenes. CBERS-4A MUX Level 4 (Orthorectified) scenes are being ingested starting from 04-13-2021. CBERS-4A WFI Level 4 (Orthorectified) scenes are being ingested starting from 10-12-2022. CBERS-4A WPM Level 4 (Orthorectified) scenes are being ingested starting from 03-27-2022.

Digital Earth Africa Sentinel-1 Radiometrically Terrain Corrected

DE Africa’s Sentinel-1 backscatter product is developed to be compliant with the CEOS Analysis Ready Data for Land (CARD4L) specifications. The Sentinel-1 mission, composed of a constellation of two C-band Synthetic Aperture Radar (SAR) satellites, are operated by European Space Agency (ESA) as part of the Copernicus Programme. The mission currently collects data every 12 days over Africa at a spatial resolution of approximately 20 m. Radar backscatter measures the amount of microwave radiation reflected back to the sensor from the ground surface. This measurement is sensitive to surface roughness, moisture content and viewing geometry. DE Africa provides Sentinel-1 backscatter as Radiometrically Terrain Corrected (RTC) gamma-0 (γ0) where variation due to changing observation geometries has been mitigated. The dual polarisation backcastter time series can be used in applications for forests, agriculture, wetlands and land cover classification. SAR’s ability to ‘see through’ clouds makes it critical for mapping and monitoring land cover changes in the wet tropics.

Digital Earth Africa Sentinel-2 Level-2A

The Sentinel-2 mission is part of the European Union Copernicus programme for Earth observations. Sentinel-2 consists of twin satellites, Sentinel-2A (launched 23 June 2015) and Sentinel-2B (launched 7 March 2017). The two satellites have the same orbit, but 180° apart for optimal coverage and data delivery. Their combined data is used in the Digital Earth Africa Sentinel-2 product. Together, they cover all Earth’s land surfaces, large islands, inland and coastal waters every 3-5 days. Sentinel-2 data is tiered by level of pre-processing. Level-0, Level-1A and Level-1B data contain raw data from the satellites, with little to no pre-processing. Level-1C data is surface reflectance measured at the top of the atmosphere. This is processed using the Sen2Cor algorithm to give Level-2A, the bottom-of-atmosphere reflectance (Obregón et al, 2019). Level-2A data is the most ideal for research activities as it allows further analysis without applying additional atmospheric corrections. The Digital Earth Africa Sentinel-2 dataset contains Level-2A data of the African continent. Digital Earth Africa does not host any lower-level Sentinel-2 data. Note that this data is a subset of the Sentinel-2 COGs dataset.

Garvan Institute Long Read Sequencing Benchmark Data

The dataset contains reference samples that will be useful for benchmarking and comparing bioinformatics tools for genome analysis. Examples include: NA12878 (HG001) and NA24385 (HG002) sequenced on an Oxford Nanopore Technologies (ONT) PromethION using the latest R10.4.1 flowcells; and, UHR RNA (direct-RNA) on an ONT PromethION using the latest RNA004 flowcells. Raw signal data output by the sequencer is provided for these datasets in BLOW5 format, and can be rebasecalled when basecalling software updates bring accuracy and feature improvements over the years. Raw signal data is not only for rebasecalling, but also can be used for emerging bioinformatics tools that directly analyse raw signal data. We also provide the basecalled data alongside the raw signal data.

IBL Neuropixels Brainwide Map on AWS

Electrophysiological recordings of mouse brain activity acquired during a decision making task.

Multi-Scale Ultra High Resolution (MUR) Sea Surface Temperature (SST)

A global, gap-free, gridded, daily 1 km Sea Surface Temperature (SST) dataset created by merging multiple Level-2 satellite SST datasets. Those input datasets include the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 (AMSR-2) on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. Data are available from 2002 to present in Zarr format. The original source of the MUR data is the NASA JPL Physical Oceanography DAAC.

NHGRI AnVIL Project

The NHGRI Analysis, Visualization, and Informatics Lab-space (AnVIL) Project (https://anvilproject.org/) is the National Human Genome Research Institute's cloud-based platform for genomic data sharing and analysis. AnVIL hosts widely used human genome reference datasets generated through NHGRI-funded research. AnVIL on Open Data on AWS provides public access to open-access datasets available through AnVIL. The project is a collaborative effort involving NHGRI, the Broad Institute, Johns Hopkins University, the University of California Santa Cruz, Vanderbilt University Medical Center, Brigham and Women's Hospital, the Carnegie Institution for Science, the City University of New York, the Fred Hutchinson Cancer Research Center, Harvard University, Oregon Health & Science University, Massachusetts General Hospital, Moffitt Cancer Center, Penn State University, and Washington University.

New Zealand Imagery

The New Zealand Imagery dataset consists of New Zealand's publicly owned aerial and satellite imagery, which is freely available to use under an open licence. The dataset ranges from the latest high-resolution aerial imagery down to 5cm in some urban areas to lower resolution satellite imagery that provides full coverage of mainland New Zealand, Chathams and other offshore islands. It also includes historical imagery that has been scanned from film, orthorectified (removing distortions) and georeferenced (correctly positioned) to create a unique and crucial record of changes to the New Zealand landscape.
All of the imagery files are Cloud Optimised GeoTIFFs using lossless WEBP compression for the main image and lossy WEBP compression for the overviews. These image files are accompanied by STAC metadata. The imagery is organised by region and survey.

RADARSAT-1

Developed and operated by the Canadian Space Agency, it is Canada's first commercial Earth observation satellite

Développé et exploité par l'Agence spatiale canadienne, il s'agit du premier satellite commercial d'observation de la Terre au Canada.

Catalina Sky Survey (CSS) subset data on AWS

Raw data that discovers Near Earth Objects (NEOs) which potentially could impact Earth

Department of Energy's Open Energy Data Initiative (OEDI)

Data released under the Department of Energy's (DOE) Open Energy Data Initiative (OEDI). The Open Energy Data Initiative aims to improve and automate access of high-value energy data sets across the U.S. Department of Energy’s programs, offices, and national laboratories. OEDI aims to make data actionable and discoverable by researchers and industry to accelerate analysis and advance innovation.

Digital Earth Africa ALOS PALSAR, ALOS-2 PALSAR-2 and JERS-1

The ALOS/PALSAR annual mosaic is a global 25 m resolution dataset that combines data from many images captured by JAXA’s PALSAR and PALSAR-2 sensors on ALOS-1 and ALOS-2 satellites respectively. This product contains radar measurement in L-band and in HH and HV polarizations. It has a spatial resolution of 25 m and is available annually for 2007 to 2010 (ALOS/PALSAR) and 2015 to 2020 (ALOS-2/PALSAR-2). The JERS annual mosaic is generated from images acquired by the SAR sensor on the Japanese Earth Resources Satellite-1 (JERS-1) satellite. This product contains radar measurement in L-band and HH polarization. It has a spatial resolution of 25 m and is available for 1996. This mosaic data is part of a global dataset provided by the Japan Aerospace Exploration Agency (JAXA) Earth Observation Research Center.

Digital Earth Africa Cropland Extent Map (2019)

Digital Earth Africa's cropland extent map (2019) shows the estimated location of croplands in Africa for the period January to December 2019. Cropland is defined as: "a piece of land of minimum 0.01 ha (a single 10m x 10m pixel) that is sowed/planted and harvest-able at least once within the 12 months after the sowing/planting date." This definition will exclude non-planted grazing lands and perennial crops which can be difficult for satellite imagery to differentiate from natural vegetation. This provisional cropland extent map has a resolution of 10m, and was built using Copernicus Sentinel-2 satellite images from 2019. The cropland extent map was produced using extensive training data from regions across Africa, coupled with a Random Forest machine learning model. The continental service contains maps built separately for eight Agro-Ecological Zones (AEZs). For a detailed exploration of the methods used to produce the cropland extent map, read the Jupyter Notebooks in DE Africa’s crop-mask GitHub repository.

Digital Earth Africa Fractional Cover

Fractional cover (FC) describes the landscape in terms of coverage by green vegetation, non-green vegetation (including deciduous trees during autumn, dry grass, etc.) and bare soil. It provides insight into how areas of dry vegetation and/or bare soil and green vegetation are changing over time. The product is derived from Landsat satellite data, using an algorithm developed by the Joint Remote Sensing Research Program. Digital Earth Africa's FC service has two components. Fractional Cover is estimated from each Landsat scene, providing measurements from individual days. Fractional Cover Annual Summary (Percentiles) provides 10th, 50th, and 90th percentiles estimated independently for the green vegetation, non-green vegetation, and bare soil fractions observed in each calendar year (1st of January - 31st December). While the scene based Fractional Cover can be used to study dynamic processes, the annual summaries make it easier to analyse year to year changes. The percentiles provide robust estimates of the low, median and high proportion values observed for each cover type in a year, which can be used to characterise the land cover.

Digital Earth Africa Monthly Normalised Difference Vegetation Index (NDVI) Anomaly

Digital Earth Africa’s Monthly NDVI Anomaly service provides estimate of vegetation condition, for each caldendar month, against the long-term baseline condition measured for the month from 1984 to 2020 in the NDVI Climatology. A standardised anomaly is calculated by subtracting the long-term mean from an observation of interest and then dividing the result by the long-term standard deviation. Positive NDVI anomaly values indicate vegetation is greener than average conditions, and are usually due to increased rainfall in a region. Negative values indicate additional plant stress relative to the long-term average. The NDVI anomaly service is therefore effective for understanding the extent, intensity and impact of a drought.Abrupt and significant negative anomalies may also be caused by fire disturbance.

KyFromAbove on AWS

The KyFromAbove initiative is focused on building and maintaining a current basemap for Kentucky that can meet the needs of its users at the state, federal, local, and regional level. A common basemap, including current color leaf-off aerial photography and elevation data (LiDAR), reduces the cost of developing GIS applications, promotes data sharing, and add efficiencies to many business processes. All basemap data acquired through this effort is made available in the public domain. KyFromAbove acquires aerial imagery and LiDAR during leaf-off conditions in the Commonwealth. The imagery typically ranges from 6-inches to 3-inches in resolution and is available from the kyfromabove Amazon S3 bucket in a Cloud Optimized GeoTiff format. LiDAR data acquired by the program is USGS Quality Level 2 (QL2). Digital Elevation Models (DEMs) at a 2-foot resolution, point cloud data in an LAS format, spot elevations in a geopackage format, and contours in a geopackage format are also available from the kyfromabove Amazon S3 bucket. KyFromAbove LiDAR and imagery data resources are managed in a Kentucky-specific 5000x5000 foot grid (FIPS:1600) (EPSG:3089). More details about the program can be found at (https://kyfromabove.ky.gov/).

Louisiana Watershed Initiative (LWI) Model Data

Geographic (land cover, land elevation, etc.), meteorologic (pluvial, wind, etc.), hydrologic (fluvial, tidal, etc.), hydrodynamic (water surface elevations, flow velocities), and built environment (structures, levees, floodgates, culverts) data used as inputs to and outputs from numerical modeling software for the prediction of flood risk in stochastic and probabilistic frameworks. This data was collected from open sources, such as from the National Oceanographic and Atmospheric Administration (NOAA) or the United States Geological Survey (USGS). The format of these data is modified to suit the needs of the modeling program and software, and then used to predict flooding in Louisiana across a range of scenarios. The modeling software used to predict flooding which utilizes and creates this data is freely available from the United States Army Corps of Engineers Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) and River Analysis System (HEC-RAS). All data is made public by the State of Louisiana for the benefit of its citizens. This flood prediction data can be used by federal, state, and local decision makers as well as private citizens to assess the flood risk they face and make sound science-based decisions for response and adaptation.

NREL Wind Integration National Dataset

Released to the public as part of the Department of Energy's Open Energy Data Initiative, the Wind Integration National Dataset (WIND) is an update and expansion of the Eastern Wind Integration Data Set and Western Wind Integration Data Set. It supports the next generation of wind integration studies.

Open NeuroData

This bucket contains multiple neuroimaging datasets (as Neuroglancer Precomputed Volumes) across multiple modalities and scales, ranging from nanoscale (electron microscopy), to microscale (cleared lightsheet microscopy and array tomography), and mesoscale (structural and functional magnetic resonance imaging). Additionally, many of the datasets include segmentations and meshes.

PubSeq - Public Sequence Resource

COVID-19 PubSeq is a free and open online bioinformatics public sequence resource with on-the-fly analysis of sequenced SARS-CoV-2 samples that allows for a quick turnaround in identification of new virus strains. PubSeq allows anyone to upload sequence material in the form of FASTA or FASTQ files with accompanying metadata through the web interface or REST API.

Southern California Earthquake Data

This dataset contains ground motion velocity and acceleration seismic waveforms recorded by the Southern California Seismic Network (SCSN) and archived at the Southern California Earthquake Data Center (SCEDC). A Distributed Acousting Sensing (DAS) dataset is included.

Steinegger Lab Datasets

The Steinegger Lab Dataset comprises biological databases and resources critical for protein sequence and structure analysis, developed to support ColabFold, MMseqs2, and Foldseek/Foldcomp—three high-performance computational tools widely used in bioinformatics.The MMseqs2 dataset serves as the backbone for our fast structure prediction tool, ColabFold, and includes UniRef30, BFD, and the ColabFold environmental databases. These datasets are specifically designed for the rapid generation of multiple sequence alignments (MSAs), which are essential for high-accuracy structure prediction. Beyond MSA generation, these resources allow for fast taxonomy annotations and functional annotation, supporting a wide range of bioinformatics applications.The Foldseek dataset includes preprocessed databases such as the AlphaFold Database (AFDB), PDB, SwissProt, and CATH, specifically designed for protein structure similarity searches. These datasets encompass the majority of both experimental and predicted structural resources, supporting analyses for monomers and multimers alike.

USGS 3DEP LiDAR Point Clouds

The goal of the USGS 3D Elevation Program (3DEP) is to collect elevation data in the form of light detection and ranging (LiDAR) data over the conterminous United States, Hawaii, and the U.S. territories, with data acquired over an 8-year period. This dataset provides two realizations of the 3DEP point cloud data. The first resource is a public access organization provided in Entwine Point Tiles format, which a lossless, full-density, streamable octree based on LASzip (LAZ) encoding. The second resource is a Requester Pays of the original, Raw LAZ (Compressed LAS) 1.4 3DEP format, and more complete in coverage, as sources with incomplete or missing CRS, will not have an ETP tile generated. Resource names in both buckets correspond to the USGS project names.

World Bank - Light Every Night

Light Every Night - World Bank Nighttime Light Data – provides open access to all nightly imagery and data from the Visible Infrared Imaging Radiometer Suite Day-Night Band (VIIRS DNB) from 2012-2020 and the Defense Meteorological Satellite Program Operational Linescan System (DMSP-OLS) from 1992-2013. The underlying data are sourced from the NOAA National Centers for Environmental Information (NCEI) archive. Additional processing by the University of Michigan enables access in Cloud Optimized GeoTIFF format (COG) and search using the Spatial Temporal Asset Catalog (STAC) standard. The data is published and openly available under the terms of the World Bank’s open data license.

nuScenes

Public large-scale dataset for autonomous driving. It enables researchers to study challenging urban driving situations using the full sensor suite of a real self-driving car.

ArcticDEM

ArcticDEM - 2m GSD Digital Elevation Models (DEMs) and mosaics from 2007 to the present. The ArcticDEM project seeks to fill the need for high-resolution time-series elevation data in the Arctic. The time-dependent nature of the strip DEM files allows users to perform change detection analysis and to compare observations of topography data acquired in different seasons or years. The mosaic DEM tiles are assembled from multiple strip DEMs with the intention of providing a more consistent and comprehensive product over large areas. ArcticDEM data is constructed from in-track and cross-track high-resolution (~0.5 meter) imagery acquired by the Maxar constellation of optical imaging satellites.

Boreas Autonomous Driving Dataset

This autonomous driving dataset includes data from a 128-beam Velodyne Alpha-Prime lidar, a 5MP Blackfly camera, a 360-degree Navtech radar, and post-processed Applanix POS LV GNSS data. This dataset was collect in various weather conditions (sun, rain, snow) over the course of a year. The intended purpose of this dataset is to enable benchmarking of long-term all-weather odometry and metric localization across various sensor types. In the future, we hope to also support an object detection benchmark.

CHAMMI-75

Quantifying cell morphology using images and machine learning models has proven to be a powerful tool to study the response of cells to treatments. However, the models used to quantify cellular morphology are typically trained with a single microscopy imaging type and under controlled experimental conditions. This results in specialized models that cannot be reused across biological studies because the technical specifications do not match (e.g., different number of channels), or because the target experimental conditions are out of distribution. We have created CHAMMI-75, a large-scale dataset containing 2.8 million multi-channel, high-resolution images curated from 75 diverse, publicly available biological studies. This dataset is useful to investigate and develop channel-adaptive models, which could process microscopy images of varying technical specifications and regardless of the number of channels. By breaking the limitations of existing models, CHAMMI-75 is an invaluable resource for creating the next generation of foundation models for image-based biological research.

CMIP6 GCMs downscaled using WRF

High-resolution historical and future climate simulations from 1980-2100

Cancer Cell Line Encyclopedia (CCLE)

The Cancer Cell Line Encyclopedia (CCLE) project is an effort to conduct a detailed genetic characterization of a large panel of human cancer cell lines. The CCLE provides public access to genomic data, visualization and analysis for over 1100 cancer cell lines. This dataset contains RNA-Seq Aligned Reads, WXS Aligned Reads, and WGS Aligned Reads data.

Co-Produced Climate Data to Support California's Resilience Investments

Downscaled future and historical climate projections for California and her environs in support of California's Fifth Climate Assessment

DOE's Water Power Technology Office's (WPTO) US Wave dataset

Released to the public as part of the Department of Energy's Open Energy Data Initiative, this is the highest resolution publicly available long-term wave hindcast dataset that – when complete – will cover the entire U.S. Exclusive Economic Zone (EEZ).

Digital Earth Africa Normalised Difference Vegetation Index (NDVI) Climatology

Digital Earth Africa’s NDVI climatology product represents the long-term average baseline condition of vegetation for every Landsat pixel over the African continent. Both mean and standard deviation NDVI climatologies are available for each calender month.Some key features of the product are:

NDVI climatologies were developed using harmonized Landsat 5,7,and 8 satellite imagery.
Mean and standard deviation NDVI climatologies are produced for each calender month, using a temporal baseline period from 1984-2020 (inclusive)
Datasets have a spatial resolution of 30 metres

Logan Unitigs and Contigs of the Sequence Read Archive (SRA) on AWS

This repository is a re-analysis of the NCBI Sequence Read Archive (SRA), December 2023 freeze, to make it more accessible. The SRA is an open access database of biological sequences, containing raw data from high-throughput DNA and RNA sequencing platforms. It is the largest database of public DNA sequences worldwide, containing a wealth of genomic diversity across all living organisms. This repository contains Logan, a set of compressed FASTA files for all individual SRA accessions, in the form of unitigs and contigs. Borrowing methods from the realm of genome assembly, unitigs preserve nearly all the information present in the original sample, whereas contigs get rid of variations to increase sequence lengths. Altogether, Logan recapitulates the information present in the SRA while making it an order of magnitude more accessible due to 20-100x smaller size and higher quality genomic content.

NIH Roadmap Epigenomics

The NIH Roadmap Epigenomics Mapping Consortium was launched with the goal of producing a public resource of human epigenomic data to catalyze basic biology and disease-oriented research. The project has generated high-quality, genome-wide maps of several key histone modifications, chromatin accessibility, DNA methylation and mRNA expression across 100s of human cell types and tissues. To see what data is available, please check the directory listing: https://roadmapepigenomics.s3.us-west-2.amazonaws.com/index.html.

NOAA Water-Column Sonar Data Archive

Water-column sonar data archived at the NOAA National Centers for Environmental Information.

New Zealand Elevation

The New Zealand Elevation dataset consists of New Zealand's publicly owned digital elevation models and digital surface models, which are freely available to use under an open licence. The dataset contains 1m resolution grids derived from LiDAR data. Point clouds are not included in the initial release.All of the elevation files are Cloud Optimised GeoTIFFs using LERC compression for the main grid and LERC compression with lower max_z_error for the overviews. These elevation files are accompanied by STAC metadata. The elevation data is organised by region and survey.

Northern California Earthquake Data

This dataset contains various types of digital data relating to earthquakes in central and northern California. Time series data come from broadband, short period, and strong motion seismic sensors, GPS, and other geophysical sensors.

Open CEDA by Watershed

CEDA is a multi-regional Environmentally-Extended Input-Output (EEIO) model developed to support a wide range of environmental systems analyses—including corporate carbon accounting and sustainable spend analysis. CEDA provides unparalleled global coverage and granularity, representing 95% of the world's GDP across 148 countries and 400 sectors, enabling robust and geographically comprehensive Scope 3 greenhouse gas (GHG) measurement. Open CEDA is the publicly avaialable version of CEDA, now easy to download and available for free for all use cases. For more information please visit our website at openceda.org. This data registry entry contains CEDA 2025 and CEDA 2024 in two separate files. CEDA 2025, the latest version of CEDA, uses 2023 as its base year, ensuring that emissions factors and economic data reflect the most recent global economic landscape available. To maintain accuracy and relevance, CEDA is updated annually with the latest data releases. At its core, CEDA connects economic exchanges to GHG emissions by quantifying the life-cycle emissions of products and services. This is achieved through the integration of input-output tables, which represent the full supply-chain network of the global economy, with GHG emissions data. As a result, CEDA provides users with a powerful tool to assess the environmental impacts embedded in corporate value chains.

Radiant MLHub

Radiant MLHub is an open library for geospatial training data that hosts datasets generated by Radiant Earth Foundation's team as well as other training data catalogs contributed by Radiant Earth’s partners. Radiant MLHub is open to anyone to access, store, register and/or share their training datasets for high-quality Earth observations. All of the training datasets are stored using a SpatioTemporal Asset Catalog (STAC) compliant catalog and exposed through a common API. Training datasets include pairs of imagery and labels for different types of machine learning problems including image classification, object detection, and semantic segmentation. Labels are generated from ground reference data and/or image annotation.

Reference Elevation Model of Antarctica (REMA)

The Reference Elevation Model of Antarctica - 2m GSD Digital Elevation Models (DEMs) and mosaics from 2009 to the present. The REMA project seeks to fill the need for high-resolution time-series elevation data in the Antarctic. The time-dependent nature of the strip DEM files allows users to perform change detection analysis and to compare observations of topography data acquired in different seasons or years. The mosaic DEM tiles are assembled from multiple strip DEMs with the intention of providing a more consistent and comprehensive product over large areas. REMA data is constructed from in-track and cross-track high-resolution (~0.5 meter) imagery acquired by the Maxar constellation of optical imaging satellites.

Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription (TaRGET)

The TaRGET (Toxicant Exposures and Responses by Genomic and Epigenomic Regulators of Transcription) Program is a research consortium funded by the National Institute of Environmental Health Sciences (NIEHS). The goal of the collaboration is to address the role of environmental exposures in disease pathogenesis as a function of epigenome perturbation, including understanding the environmental control of epigenetic mechanisms and assessing the utility of surrogate tissue analysis in mouse models of disease-relevant environmental exposures.

U.S. Environmental Protection Agency (EPA) Center for Computational Toxicology and Exposure High Throughput Transcriptomics Data

High-throughput transcriptomics (HTTr) data generated by US EPA Office of Research and Development, Center for Computational Toxicology and Exposure (CCTE), Biomolecular and Computational Toxicology Division. All data is generated using TempO-Seq targeted RNA-seq technology from in vitro cell culture systems.

ASTER L1T Cloud-Optimized GeoTIFFs

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B (AST_L1B), that has been geometrically corrected, and rotated to a north-up UTM projection. The AST_L1T is created from a single resampling of the corresponding ASTER L1A (AST_L1A) product.The precision terrain correction process incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes where correlation statistics reach a minimum threshold. Alternate levels of correction are possible (systematic terrain, systematic, or precision) for scenes acquired at night or that otherwise represent a reduced quality ground image (e.g., cloud cover).Each AST_L1T granule is converted into three different COG files based on the sensor and spatial resolution, VNIR at 15m, SWIR at 30m and TIR at 90m. The metadata required to transform the digital numbers (DN) to radiance and reflectance values are also incorporated as metadata in the TIFF files. The filenaming convention and the organization of bands are described here.

BossDB Open Neuroimagery Datasets

This data ecosystem, Brain Observatory Storage Service & Database (BossDB), contains several neuro-imaging datasets across multiple modalities and scales, ranging from nanoscale (electron microscopy), to microscale (cleared lightsheet microscopy and array tomography), and mesoscale (structural and functional magnetic resonance imaging). Additionally, many of the datasets include dense segmentation and meshes.

CIViC (Clinical Interpretation of Variants in Cancer)

Precision medicine refers to the use of prevention and treatment strategies that are tailored to the unique features of each individual and their disease. In the context of cancer this might involve the identification of specific mutations shown to predict response to a targeted therapy. The biomedical literature describing these associations is large and growing rapidly. Currently these interpretations exist largely in private or encumbered databases resulting in extensive repetition of effort. Realizing precision medicine will require this information to be centralized, debated and interpreted for application in the clinic. CIViC is an open access, open source, community-driven web resource for Clinical Interpretation of Variants in Cancer. Our goal is to enable precision medicine by providing an educational forum for dissemination of knowledge and active discussion of the clinical significance of cancer genome alterations.

Clinical Proteomic Tumor Analysis Consortium 2 (CPTAC-2)

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. CPTAC-2 is the Phase II of the CPTAC Initiative (2011-2016). Datasets contain open RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, and miRNA Expression Quantification data.

Coupled Model Intercomparison Project 6

The sixth phase of global coupled ocean-atmosphere general circulation model ensemble.

Earth Observation Data Cubes for Brazil

Earth observation (EO) data cubes produced from analysis-ready data (ARD) of CBERS-4, Sentinel-2 A/B and Landsat-8 satellite images for Brazil. The datacubes are regular in time and use a hierarchical tiling system. Further details are described in Ferreira et al. (2020).

GEOS-Chem Input Data

Input data for the GEOS-Chem Chemical Transport Model, includes NASA/GMAO MERRA-2 and GEOS-FP meteorological products, chemistry input data, emissions input data, and other smaller datasets such as model initial conditions.

GEOS-Chem Nested Input Data

Input data for nested-grid simulations using the GEOS-Chem Chemical Transport Model. This includes the NASA/GMAO MERRA-2 and GEOS-FP meteorological products, the HEMCO emission inventories, and other small data such as model initial conditions.

Global Database of Events, Language and Tone (GDELT)

This project monitors the world's broadcast, print, and web news from nearly every corner of every country in over 100 languages and identifies the people, locations, organizations, counts, themes, sources, emotions, quotes, images and events driving our global society every second of every day.

IBL Neuropixels Reproducible Ephys Data on AWS

Electrophysiological recordings acquired using Neuropixels probes in different mice and labs, targeting the same brain locations (including posterior parietal cortex, hippocampus, and thalamus).

ICGC on AWS

The International Cancer Genome Consortium (ICGC) coordinates projects with the common aim of accelerating research into the causes and control of cancer. The PanCancer Analysis of Whole Genomes (PCAWG) study is an international collaboration to identify common patterns of mutation in whole genomes from ICGC. More than 2,400 consistently analyzed genomes corresponding to over 1,100 unique ICGC donors are now freely available on Amazon S3 to credentialed researchers subject to ICGC data sharing policies.

Materials Project Data

Materials Project is an open database of computed materials properties aiming to accelerate materials science research. The resources in this OpenData dataset contain the raw, parsed, and build data products.

NOAA National Water Model CONUS Retrospective Dataset

The NOAA National Water Model Retrospective dataset contains input and output from multi-decade CONUS retrospective simulations. These simulations used meteorological input fields from meteorological retrospective datasets. The output frequency and fields available in this historical NWM dataset differ from those contained in the real-time operational NWM forecast model. Additionally, note that no streamflow or other data assimilation is performed within any of the NWM retrospective simulations

One application of this dataset is to provide historical context to current near real-time streamflow, soil moisture and snowpack conditions. The retrospective data can be used to infer flow frequencies and perform temporal analyses with hourly streamflow output and 3-hourly land surface output. This dataset can also be used in the development of end user applications which require a long baseline of data for system training or verification purposes.

Details for Each Version of the NWM Retrospective Output

CONUS Domain - CONUS retrospective output is provided by all four versions of the NWM

Version 3.0 - A 44-year (February 1979 through January 2023) retrospective simulation using version 3.0 of the National Water Model.
Version 2.1 - A 42-year (February 1979 through December 2020) retrospective simulation using version 2.1 of the National Water Model.
Version 2.0 - A 26-year (January 1993 through December 2018) retrospective simulation using version 2.0 of the National Water Model.
Version 1.2 - A 25-year (January 1993 through December 2017) retrospective simulation using version 1.2 of the National Water Model.

oCOUNS Domains - Only v3.0 of the NWM Retrospective data set provides coverage of the NWM Alaska, Hawaii and Puerto Rico / US Virgin Island domains.

CONUS Meteorological Forcing

Versions 3.0 and 2.1: NWM Retrospective simulations used forcing from the Office of Water Prediction Analysis of Record for Calibration (AORC) dataset. NWM v2.1 used AORC v1.0 for 1979-2006 and AORC v1.1 for 2007-2020, while NWM v3.0 used AORC v1.1 for the full v3.0 (1979-2023 period)

Important Warning - While the metadata tag in the NWM v3.0 forcing files label the files as “v2.1”, the files are in fact v3.0 forcing files. 2) Versions 2.0 and 1.2 - NWM Retrospective simulation uses forcing from the North American Land Data Assimilation System (NLDAS) dataset
oCONUS Meteorological Forcing

Version 3.0 - AORC Alaska forcing was used to drive the NWM Alaska simulation, while North American Regional Reanalysis (NARR) data along with precipitation from the Alaska Pacific River Forecast Center (APRFC) was used to drive the Hawaii retrospective simulation. Similarly, the Puerto Rico / US Virgin Island retrospective simulation was driven by NARR data along with precipitation from the Southeast River Forecast Center.

Formats - NWM Retrospective data is available in two formats, NetCDF and Zarr. The NetCDF files contain the full set of NWM output data, while the Zarr files contain a subset of NWM output fields that vary with model version.

NWM V3.0: All model output and forcing input fields are available in the NetCDF format. All model output fields along with the precipitation forcing field are available in the Zarr format.
NWM V2.1: All model output and forcing input fields are available in the NetCDF format. Many of the model output fields along with the precipitation forcing field are available in the Zarr format
NWM V2.0: All model output fields are available in NetCDF format. Model channel output including streamflow and related fields are available in Zarr format.
NWM V1.2: All model output fields are available in NetCDF format.

A table listing the data available within each NetCDF and Zarr file is located in the '[documentation page](https://github.com/NOAA-Big-Data-Program/bdp-data-docs/blob/main/nwm/README.md)'. This data includes meteorological NWM forcing inputs along with NWM hydrologic and land surface outputs, and varies by version number.

OpenAQ

Global, aggregated physical air quality data from public data sources provided by government, research-grade and other sources. These awesome groups do the hard work of measuring these data and publicly sharing them, and our community makes them more universally-accessible to both humans and machines.

OpenAerialMap on AWS

OpenAerialMap is a collection of high-resolution openly licensed satellite and aerial imagery.

Scottish Public Sector LiDAR Dataset

This dataset is Lidar data that has been collected by the Scottish public sector and made available under the Open Government Licence. The data are available as point cloud (LAS format or in LAZ compressed format), along with the derived Digital Terrain Model (DTM) and Digital Surface Model (DSM) products as Cloud optimized GeoTIFFs (COG) or standard GeoTIFF. The dataset contains multiple subsets of data which were each commissioned and flown in response to different organisational requirements. The details of each can be found at https://remotesensingdata.gov.scot/data#/list

SnpEff & SnpSift Genomic Variant Annotation Databases

SnpEff is a variant annotation and effect prediction tool that annotates and predicts the effects of genetic variants on genes and proteins (such as amino acid changes). It supports over 38,000 genomes and provides comprehensive genomic databases for variant annotation. The databases include reference genomes, gene annotations, protein sequences, and regulatory elements from trusted sources like ENSEMBL, RefSeq, and UCSC. SnpSift complements SnpEff by providing tools to annotate genomic variants using databases, filter large genomic datasets, and manipulate annotated variants. Together, these tools provide a complete solution for genomic variant analysis, supporting research in human genetics, cancer genomics, pharmacogenomics, and model organism studies.

nuPlan

nuPlan is the world's first large-scale planning benchmark for autonomous driving.

10m Annual Land Use Land Cover (9-class)

This dataset, produced by Impact Observatory, Microsoft, and Esri, displays a global map of land use and land cover (LULC) derived from ESA Sentinel-2 imagery at 10 meter resolution for the years 2017 - 2023. Each map is a composite of LULC predictions for 9 classes throughout the year in order to generate a representative snapshot of each year. This dataset was generated by Impact Observatory, which used billions of human-labeled pixels (curated by the National Geographic Society) to train a deep learning model for land classification. Each global map was produced by applying this model to the Sentinel-2 annual scene collections from the Mircosoft Planetary Computer. Each of the maps has an assessed average accuracy of over 75%. These maps have been improved from Impact Observatory’s previous release and provide a relative reduction in the amount of anomalous change between classes, particularly between “Bare” and any of the vegetative classes “Trees,” “Crops,” “Flooded Vegetation,” and “Rangeland”. This updated time series of annual global maps is also re-aligned to match the ESA UTM tiling grid for Sentinel-2 imagery. Data can be accessed directly from the Registry of Open Data on AWS, from the STAC 1.0.0 endpoint, or from the IO Store for a specific Area of Interest (AOI).

AIRS/Aqua L1C Infrared (IR) resampled and corrected radiances V6.7 (AIRICRAD) at GES DISC

The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1C data set contains AIRS infrared calibrated and geolocated radiances in W/m2/micron/ster. This data set is generated from AIRS level 1B data. The spectral coverage of L1C data is from 3.74 to 15.4 mm. The nominal spectral resolution lambda / delta lambda = 1200. The spectrum is sampled twice per spectral resolution element in a total of 2645 spectral channels. A day of AIRS data is divided into 240 granules (scenes) each of 6-minute duration. For the AIRS IR measurements, an individual granule contains 135 pixels across-track and 90 along-track pixels; there are total of 135 x 90 = 12,150 pixels per granule. AIRS employs a 49.5 degree crosstrack scanning with a 1.1 degree instantaneous field of view (IFOV) to provide twice daily coverage of essentially the entire globe in a 1:30 PM sun synchronous orbit with the 13.5 x 13.5 km2 spatial resolution at nadir. The L1C swath products are derived from the L1B swath products. The primary purpose of the level 1C is to generate the spectra of radiances without spectral gaps caused by the instrument design and bad spectral points. The AIRS L1C data can be used for comparative (with other IR measurements) studies and for weather-climate research.This is the latest version of this collection. The DOIs assigned to previous versions, which are no longer available, now direct to this page. For this collection the switchover occurred on June 1, 2020. Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

Argoverse

Home of the Argoverse datasets.Public datasets supported by detailed maps to test, experiment, and teach self-driving vehicles how to understand the world around them.This bucket includes the following datasets:

Argoverse 1 (AV1)

Motion Forecasting
Tracking

Argoverse 2 (AV2)

Motion Forecasting
Lidar
Sensor

Trust, but Verify (TbV)

Map Change Detection

Capella Space Synthetic Aperture Radar (SAR) Open Dataset

Open Synthetic Aperture Radar (SAR) data from Capella Space. Capella Space is an information services company that provides on-demand, industry-leading, high-resolution synthetic aperture radar (SAR) Earth observation imagery. Through a constellation of small satellites, Capella provides easy access to frequent, timely, and flexible information affecting dozens of industries worldwide. Capella's high-resolution SAR satellites are matched with unparalleled infrastructure to deliver reliable global insights that sharpen our understanding of the changing world – improving decisions about commerce, conservation, and security on Earth. Learn more at www.capellaspace.com.

Clinical Proteomic Tumor Analysis Consortium 3 (CPTAC-3)

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) is a national effort to accelerate the understanding of the molecular basis of cancer through the application of large-scale proteome and genome analysis, or proteogenomics. CPTAC-3 is the Phase III of the CPTAC Initiative. The dataset contains open RNA-Seq Gene Expression Quantification data.

CryoET Data Portal

Cryo-electron tomography (cryoET) is a powerful technique for visualizing 3D structures of cellular macromolecules at near atomic resolution in their native environment. Observing the inner workings of cells in context enables better understanding about the function of healthy cells and the changes associated with disease. However, the analysis of cryoET data remains a significant bottleneck, particularly the annotation of macromolecules within a set of tomograms, which often requires a laborious and time-consuming process of manual labelling that can take months to complete. Given the current success of machine learning (ML) methods for image analysis, it seems likely that ML will have a significant impact on resolving this bottleneck. The scientific community has expressed the need to encourage further ML algorithm development by providing large training sets of annotated cryoET data in standardized formats. In response to this, we (Biohub) have established the CryoET Data Portal (https://cryoetdataportal.czscience.com/) to provide biologists and developers open access to high-quality, standardized, annotated data that can be readily used to retrain or develop new annotation models and algorithms.

DCR Office of Resilience Planning – Public File Repository

The Virginia Department of Conservation and Recreation’s Office of Resilience Planning maintains this public file repository to provide access to flood resilience open data products. The repository is designed to house public data produced for the Virginia Coastal Resilience Master Plan (CRMP), Virginia Flood Protection Master Plan (VFPMP), and other purposes. At present, the repository hosts only data products produced for the CRMP Phase II (2025) and Phase I (2021).

DE Africa Waterbodies Monitoring Service

The Digital Earth Africa continental Waterbodies Monitoring Service identifies more than 700,000 water bodies from over three decades of satellite observations. This service maps persistent and seasonal water bodies and the change in their water surface area over time. Mapped water bodies may include, but are not limited to, lakes, ponds, man-made reservoirs, wetlands, and segments of some river systems.On a local, regional, and continental scale, this service helps improve our understanding of surface water dynamics and water availability and can be used for monitoring water bodies such as wetlands, lakes and dams in remote and/or inaccessible locations.

ESM Atlas — Protein Features and Structures

The ESM Atlas is a large-scale public dataset of computational outputs generated by ESMC and ESMFold2, derived from a deduplicated set of over 6.8 billion publicly available protein sequences spanning all domains of life — including viral proteins and previously unannotated sequences representing metagenomic dark matter sampled from a wide range of biomes. The dataset includes two primary components. A sparse autoencoder (SAE) features for ~6.8 billion proteins, capturing interpretable biological representations from the ESMC 6B model, and predicted three-dimensional protein structures for ~1.1 billion proteins generated using ESMFold2. Proteins are organized into 7.7 million clusters based on SAE feature similarity, enabling functional grouping across the protein universe. The dataset is accessible via AWS CLI. A companion data explorer is available at https://biohub.ai/esmc/atlas.

EarthDEM

EarthDEM - 2m GSD Digital Elevation Models (DEMs) and mosaics from 2002 to the present. The EarthDEM project seeks to fill the need for high-resolution time-series elevation data in non-polar regions. The time-dependent nature of the strip DEM files allows users to perform change detection analysis and to compare observations of topography data acquired in different seasons or years. The mosaic DEM tiles are assembled from multiple strip DEMs with the intention of providing a more consistent and comprehensive product over large areas. EarthDEM data is constructed from in-track and cross-track high-resolution (~0.5 meter) imagery acquired by the Maxar constellation of optical imaging satellites. Only a portion of the worldwide EarthDEM dataset is available publicly. Federally-funded researchers may access the entire dataset via NASA CSDA's Smallsat Data Explorer (see Data at Work section).

GeoJSON Files for Geo-TIDE

GeoJSON files for the MIT Climate & Sustainability Consortium's Geospatial Trucking Industry Decarbonization Explorer

HYCOM-OceanTrack Integrated HYCOM Eulerian Fields and Lagrangian Trajectories Dataset

A combined dataset of simulated ocean sea surface height, near-surface velocities, and particle trajectories from a global 1/25th degree HYbrid Coordinate Ocean Model (HYCOM) 1-year run.

HYbrid Coordinate Ocean Model Global Ocean Forecast System Reanalysis

Global Ocean Forecasting System (GOFS) 3.1 output on the GLBv0.08 grid. The resolution is 0.08° resolution between 40°S and 40°N, 0.04° poleward of these latitudes. The temportal frequenct is 3 hourly. This data was created by the Naval Research Laboratory: Ocean Dynamics and Prediction Branch.

IBL Behavioral Data on AWS

Behavioral data of mice performing a decision-making task, associated with 2020 publication of the IBL.

NOAA Rapid Refresh Forecast System (RRFS) [Prototype]

The Rapid Refresh Forecast System (RRFS) is the National Oceanic and Atmospheric Administration’s (NOAA) next generation convection-allowing, rapidly-updated ensemble prediction system, currently scheduled for operational implementation in 2026. The operational configuration will feature a 3 km grid covering North America and include deterministic forecasts every hour out to 18 hours, with deterministic and ensemble forecasts to 60 hours four times per day at 00, 06, 12, and 18 UTC.The RRFS will provide guidance to support forecast interests including, but not limited to, aviation, severe convective weather, renewable energy, heavy precipitation, and winter weather on timescales where rapidly-updated guidance is particularly useful.

The RRFS is underpinned by the Unified Forecast System (UFS), a community-based Earth modeling initiative, and benefits from collaborative development efforts across NOAA, academia, and research institutions.

This bucket provides access to a real time, pre-implementation RRFS prototype. It also hosts some final retrospective parallel output used in the evaluation of RRFSv1

The real-time RRFS output is not under 24x7 monitoring and is not operational. Output may be delayed or missing. Outputs will change. When significant changes to output take place, this description will be updated.

We currently provide hourly deterministic forecasts at 3 km grid spacing out to 84 hours at 00, 06, 12, and 18 UTC, and out to 18 hours for other cycles. Output is organized by cycle date and cycle hour.We are now dividing what is provided in this bucket into a fuller set of products (intended primarily for model developers of RRFSv2 using this data to initialize their forecasts), and a more limited set of products that will approximate what we'll be able to provide operationally over NOMADS. The fuller set of data is under the rrfs_a/ subdirectory:For example, rrfs_a/rrfs.20260114/12 contains the deterministic forecast initialized at 12 UTC on 14 January 2026. Users will find two primary types of output in GRIB2 format. The first is:

rrfs.t12z.natlev.3km.f018.na.grib2

Meaning that this is the RRFS initialized at 12 UTC, covers the full North America domain, and is the native level post-processed gridded data at hour 18. This output is on a rotated latitude-longitude grid at 3 km grid spacing.

A second output file in grib2 format is:

rrfs.t12z.prslev.3km.f018.conus.grib2

The “prslev” descriptor indicates that this post-processed gridded data is output on pressure levels. The “conus” descriptor indicates that it covers the contiguous United StatesFor users interested in other domains, output is provided on the full 3-km North American grid and also for subset over Alaska, Hawaii, and Puerto Rico. The files are identified as follows:

North America: rrfs.t00z.prslev.3km.f002.na.grib2 Alaska: rrfs.t00z.prslev.3km.f002.ak.grib2 Hawaii: rrfs.t00z.prslev.2p5km.f002.hi.grib2 Puerto Rico: rrfs.t00z.prslev.2p5km.f002.pr.grib2

We also provide prototype RRFSv1 ensemble output and products. Output is available for 00, 06, 12, and 18 UTC cycles, and is organized by cycle date and cycle hour. For example, rrfs_a/rrfsens.20260115/00/m001 contains the forecast from member 1, and rrfs_a/refs.20260115/00/enspost_timelag contains the combined ensemble products.The set of files that will be available operationally over NOMADS (and which most users should use rather than the rrfs_a/ products) is under the rrfs_public/ subdirectory:Most naming conventions listed above for rrfs_a apply here as well. However, it will not include full North America domain output, and only provides hourly output to 60 h (with 3 h resolution beyond that). Also, only the 00/03/06/09/12/15/18/21 UTC cycles are provided. Individual ensemble forecast members are not included, but the combined ensemble products are. However, the combined ensemble products are under rrfs_public/refs.20260304/12/ensprod/ types of directories, which will better match the operational directory structure name. Final retrospective parallel output is under the retro_output_final/ directory, with spring (May 2024), summer (July 2023), and winter (~Jan 8 to Feb 8, 2024) output available.

NYU Langone & FAIR FastMRI Dataset

This dataset contains deidentified raw k-space data and DICOM image files of over 1,500 knees and 6,970 brains.

New York City Taxi and Limousine Commission (TLC) Trip Record Data

Data of trips taken by taxis and for-hire vehicles in New York City. Note: access to this dataset is free, however direct S3 access does require an AWS account. Anonymous downloads are accessible from the dataset's documentation webpage listed below.

OME-Zarr Open SciVis Datasets

This project provides the Open SciVis Datasets in a chunked, highly-compressed, multi-scale format, encodes metadata in JSON according to the OME-Zarr specification, and hosts the datasets on AWS S3 through the AWS Open Data Program, aiming to serve as a web-based resource for the scientific visualization community to enhance reproducibility and facilitate testing and development of OME-Zarr tools.

Open Bioinformatics Reference Data for Galaxy

This dataset provides genomic reference data and software packages for use with Galaxy and Bioconductor applications. The reference data is available for hundreds of reference genomes and has been formatted for use with a variety of tools. The available configuration files make this data easily incorporable with a local Galaxy server without additional data preparation. Additionally, Bioconductor's AnnotationHub and ExperimentHub data are provided for use via R packages through which the data can be downloaded and installed into any R environment.

OpenEEW

Grillo has developed an IoT-based earthquake early-warning system, with sensors currently deployed in Mexico, Chile, Puerto Rico and Costa Rica, and is now opening its entire archive of unprocessed accelerometer data to the world to encourage the development of new algorithms capable of rapidly detecting and characterizing earthquakes in real time.

Pacific Ocean Sound Recordings

This project offers passive acoustic data (sound recordings) from a deep-ocean environment off central California. Recording began in July 2015, has been nearly continuous, and is ongoing. These resources are intended for applications in ocean soundscape research, education, and the arts.

Planette C3S Seasonal Forecast Data

The C3S seasonal forecast dataset provides global, daily, probabilistic forecasts of the Earth system, enabling users to assess the likelihood of future climate states. These forecasts are particularly valuable for studying slowly evolving climate patterns such as El Niño, La Niña, and the North Atlantic Oscillation (NAO), which can be predicted with greater skill than the chaotic atmosphere. This dataset is derived from the Copernicus Climate Change Service (C3S) archive and includes SEAS5 hindcasts (1981-2016) and forecasts (2017-present) at 1°x1° global resolution. More models from the C3S archive will be updated as they are processed into cloud native format. The planette C3S archive stores this data in cloud native format for easy access and analysis.

Planette ERA5 Archive

The ERA5 archive provides a comprehensive record of global weather and climate from 1940 to present, with multiple temporal aggregations for flexible analysis. This dataset is derived from the ECMWF/Copernicus ERA5 reanalysis and includes daily means, 7-day rolling means, and monthly/seasonal aggregations at 0.25°×0.25° global resolution. The Planette ERA5 archive stores this data in cloud-native format (Zarr with icechunk) for efficient access and analysis.The dataset includes essential atmospheric variables at both surface and pressure levels, enabling a wide range of climate analyses, from daily weather patterns to long-term climate trends. Daily means are computed by averaging hourly ERA5 data, while longer temporal aggregations are derived from these daily means.

PoroTomo

Released to the public as part of the Department of Energy's Open Energy Data Initiative, these data represent vertical and horizontal distributed acoustic sensing (DAS) data collected as part of the Poroelastic Tomography (PoroTomo) project funded in part by the Office of Energy Efficiency and Renewable Energy (EERE), U.S. Department of Energy.

Public Utility Data Liberation Project

The Public Utility Data Liberation Project (PUDL) provides analysis-ready U.S. energy system data in bulk for programmatic use. Sources include the U.S. Energy Information Administration (EIA), the Environmental Protection Agency (EPA), the Federal Energy Regulatory Commission (FERC), the Pipeline and Hazardous Materials Safety Administration (PHMSA), the Securities and Exchange Commission (SEC). The primary focus is on the electricity sector, with additional data on the natural gas system and energy company financial reporting.

RCM CEOS Analysis Ready Data | Données prêtes à l'analyse du CEOS pour le MCR

The RADARSAT Constellation Mission (RCM) is Canada's third generation of Earth observation satellites. Launched on June 12, 2019, the three identical satellites work together to bring solutions to key challenges for Canadians. As part of ongoing Open Government efforts, NRCan produces a CEOS analysis ready data (ARD) of Canada landmass using a 30M Compact-Polarization standard coverage, every 12 days. RCM CEOS-ARD (POL) is the first ever polarimetric dataset approved by the CEOS committee. Previously, users were stuck ordering, downloading and processing RCM images (level 1) on their own, often with expensive software. This new dataset aims to remove these burdens with a new STAC catalog for discovery and direct download links.

La mission de la Constellation RADARSAT (MCR) est la troisième génération de satellites d'observation de la Terre du Canada. Lancés le 12 juin 2019, les trois satellites identiques travaillent ensemble pour apporter des solutions aux principaux défis des Canadiens. Dans le cadre des efforts continus pour un gouvernement ouvert, RNCan produit des données prêtes à l'analyse CEOS (ARD) de la masse terrestre du Canada en utilisant une couverture standard de 30 m en polarisation compacte, tous les 12 jours. Les CEOS-ARD (POL) du MCR constituent le premier ensemble de données polarimétriques jamais approuvé par le comité CEOS. Auparavant, les utilisateurs étaient obligés de commander, de télécharger et de traiter eux-mêmes les images RCM (niveau 1), souvent à l'aide de logiciels coûteux. Ce nouvel ensemble de données vise à supprimer ces fardeaux avec un nouveau catalogue STAC à découvrir et à télécharger directement depuis S3.

RarePlanes

RarePlanes is a unique open-source machine learning dataset from CosmiQ Works and AI.Reverie that incorporates both real and synthetically generated satellite imagery. The RarePlanes dataset specifically focuses on the value of AI.Reverie synthetic data to aid computer vision algorithms in their ability to automatically detect aircraft and their attributes in satellite imagery. Although other synthetic/real combination datasets exist, RarePlanes is the largest openly-available very high resolution dataset built to test the value of synthetic data from an overhead perspective. The real portion of the dataset consists of 253 Maxar WorldView-3 satellite scenes spanning 112 locations and 2,142 km^2 with 14,700 hand-annotated aircraft. The accompanying synthetic dataset is generated via AI.Reverie’s novel simulation platform and features 50,000 synthetic satellite images with ~630,000 aircraft annotations.

Sentinel-1 Monthly Mosaic

Synthetic Aperture Radar (SAR) sensor have the advantage of operating at wavelengths not impeded by cloud cover and can acquire data over a site during the day or night. The Sentinel-1 mission, part of the Copernicus joint initiative by the European Commission (EC) and the European Space Agency (ESA), provides reliable and repeated wide-area monitoring using its SAR instrument.Sentinel-1 Monthly Mosaics are analysis-ready product of individual Sentinel-1 acquisitions. Sentinel-1 monthly mosaics are generated from Radiometric Terrain Corrected (RTC) backscatter data, with variations from changing observation geometries mitigated. RTC images acquired within a calendar month are combined using a multitemporal compositing algorithm. This algorithm calculates a weighted average of valid pixels, assigning higher weights to pixels with higher local resolution (e.g., slopes facing away from the sensor). This local resolution weighting approach minimizes noise and improves spatial homogeneity in the composites. Sinergise (Planet Labs) processed and indexed the product on the DE Africa platform

Serratus: Ultra-deep Search for Novel Viruses - Versioned Data Release

Serratus is a collaborative open science project for ultra-rapid discovery of known and unknown coronaviruses in response to the COVID-19 pandemic through re-analysis of publicly available genomic data. Our resulting vertebrate viral alignment data is explorable via the Serratus Explorer and directly accessible on Amazon S3.

Solar Dynamics Observatory (SDO) Machine Learning Dataset

The v1 dataset includes AIA/HMI observations 2010-2018 and v2 includes AIA/HMI observations 2010-2020 in all 10 wavebands (94A, 131A, 171A, 193A, 211A, 304A, 335A, 1600A, 1700A, 4500A), with 512x512 resolution and 6 minutes cadence; HMI vector magnetic field observations in Bx, By, and Bz components, with 512x512 resolution and 12 minutes cadence; The EVE observations in 39 wavelengths from 2010-05-01 to 2014-05-26, with 10 seconds cadence.

The MIT Supercloud Dataset

Collection of parsed datacenter logs and time series data of hardware utilization from the MIT Supercloud system.

Wildfire Projections to Support Climate Resilience

Wildfire projections for California and her environs in support of California's Fifth Climate Assessment supported with historical weather observations and renewable energy capacity profiles for grid operations.

2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File

The 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File (2023-06-30) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9 , and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official “production settings,” the final set of algorithmic parameters and privacy-loss budget allocations, that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File. The NMF consists of the full set of privacy-protected statistical queries (counts of individuals or housing units with particular combinations of characteristics) of confidential 2010 Census data relating to the 2010 Demonstration Data Products Suite – Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File – Production Settings (2023-04-03). These statistical queries, called “noisy measurements” were produced under the zero-Concentrated Differential Privacy framework (Bun, M. and Steinke, T [2016] https://arxiv.org/abs/1605.02065; see also Dwork C. and Roth, A. [2014] https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] https://arxiv.org/abs/2004.00010), which added positive or negative integer-valued noise to each of the resulting counts. The noisy measurements are an intermediate stage of the TDA prior to the post-processing the TDA then performs to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these 2010 Census demonstration data to enable data users to evaluate the expected impact of disclosure avoidance variability on 2020 Census data. The 2010 Census Production Settings Demographic and Housing Characteristics (DHC) Demonstration Noisy Measurement File (2023-04-03) has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004).

The 2010 Census Production Settings Demographic and Housing Characteristics Demonstration Noisy Measurement File includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2010 Census Edited File (CEF), which includes confidential data initially collected in the 2010 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) (https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product-planning/2010-demonstration-data-products/04-Demonstration_Data_Products_Suite/2023-04-03/). As these 2010 Census demonstration data are intended to support study of the design and expected impacts of the 2020 Disclosure Avoidance System, the 2010 CEF records were pre-processed before application of the zCDP framework. This pre-processing converted the 2010 CEF records into the input-file format, response codes, and tabulation categories used for the 2020 Census, which differ in substantive ways from the format, response codes, and tabulation categories originally used for the 2010 Census.

The NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints—information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) —are provided.

2020 Census Demographic and Housing Characteristics (DHC) Noisy Measurement File

The 2020 Census Demographic and Housing Characteristics Noisy Measurement File is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022], and implemented in primitives.py). The 2020 Census Demographic and Housing Characteristics Noisy Measurement File includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] ), which added positive or negative integer-valued noise to each of the resulting counts. These are estimated counts of individuals and housing units included in the 2020 Census Edited File (CEF), which includes confidential data collected in the 2020 Census of Population and Housing.

The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the Census Demographic and Housing Characteristics Summary File. In addition to the noisy measurements, constraints based on invariant calculations --- counts computed without noise --- are also included (with the exception of the state-level total populations, which can be sourced separately from data.census.gov).

The Noisy Measurement File was produced using the official “production settings,” the final set of algorithmic parameters and privacy-loss budget allocations that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File.

The noisy measurements are produced in an early stage of the TDA. Afterward, these noisy measurements are post-processed to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these noisy measurements to enable data users to evaluate the impact of disclosure avoidance variability on 2020 Census data. The 2020 Census Demographic and Housing Characteristics (DHC) Noisy Measurement File has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004).

3000 Rice Genomes Project

The 3000 Rice Genome Project is an international effort to sequence the genomes of 3,024 rice varieties from 89 countries.

AWS Public Blockchain Data

The AWS Public Blockchain Data initiative provides free access to blockchain datasets through collaboration with data providers. The data is optimized for analytics by being transformed into compressed Parquet files, partitioned by date for efficient querying.

Datasets

Blockchain dataset - Maintained by - Path:
- Bitcoin - AWS - s3://aws-public-blockchain/v1.0/btc/
- Ethereum - AWS - s3://aws-public-blockchain/v1.0/eth/
- Aptos - SonarX - s3://aws-public-blockchain/v1.1/sonarx/aptos/
- Arbitrum - SonarX - s3://aws-public-blockchain/v1.1/sonarx/arbitrum/
- Base - SonarX - s3://aws-public-blockchain/v1.1/sonarx/base/
- BNB Chain - BNB Chain - s3://aws-public-blockchain/v1.1/bnb/
- Cronos - Cronos - s3://aws-public-blockchain/v1.1/cronos/
- Provenance - SonarX - s3://aws-public-blockchain/v1.1/sonarx/provenance/
- Stellar(XDR files) - Stellar - s3://aws-public-blockchain/v1.1/stellar/
- The Open Network (TON) - TON - s3://aws-public-blockchain/v1.1/ton/
- XRP Ledger - SonarX - s3://aws-public-blockchain/v1.1/sonarx/xrp/

Become a Data Provider

We welcome additional blockchain data providers to join this initiative. If you're interested in contributing datasets to the AWS Public Blockchain Data program, please contact our team at aws-public-blockchain@amazon.com.

Amazonia EO satellite on AWS

Imagery acquired by Amazonia-1 satellite. The image files are recorded and processed by Instituto Nacional de Pesquisas Espaciais (INPE) and are converted to Cloud Optimized Geotiff format in order to optimize its use for cloud based applications. WFI Level 4 (Orthorectified) scenes are being ingested daily starting from 08-29-2022, the complete Level 4 archive will be ingested by the end of October 2022.

Argo marine floats data and metadata from Global Data Assembly Centre (Argo GDAC)

Argo is an international program to observe the interior of the ocean with a fleet of profiling floats drifting in the deep ocean currents (https://argo.ucsd.edu). Argo GDAC is a dataset of 5 billion in situ ocean observations from 18.000 profiling floats (4.000 active) which started 20 years ago. Argo GDAC dataset is a collection of 18.000 NetCDF files. It is a major asset for ocean and climate science, a contributor to IOCCP reports.

Automated Segmentation of Intracellular Substructures in Electron Microscopy (ASEM) on AWS

The Automated Segmentation of intracellular substructures in Electron Microscopy (ASEM) project provides deep learning models trained to segment structures in 3D images of cells acquired by Focused Ion Beam Scanning Electron Microscopy (FIB-SEM). Each model is trained to detect a single type of structure (mitochondria, endoplasmic reticulum, golgi apparatus, nuclear pores, clathrin-coated pits) in cells prepared via chemically-fixation (CF) or high-pressure freezing and freeze substitution (HPFS). You can use our open source pipeline to load a model and predict a class of sub-cellular structures in naive FIB-SEM cells images. If required, a fine-tuning procedure allows a model to be trained on a small amount of additional ground truth annotations to improve a prediction on a naive dataset. Together with the trained models, we also provide the training, validation and test datasets.

CAM6 Data Assimilation Research Testbed (DART) Reanalysis: Cloud-Optimized Dataset

This is a cloud-hosted subset of the CAM6+DART (Community Atmosphere Model version 6 Data Assimilation Research Testbed) Reanalysis dataset. These data products are designed to facilitate a broad variety of research using the NCAR CESM 2.1 (National Center for Atmospheric Research's Community Earth System Model version 2.1), including model evaluation, ensemble hindcasting, data assimilation experiments, and sensitivity studies. They come from an 80 member ensemble reanalysis of the global troposphere and stratosphere using DART and CAM6. The data products represent states of the atmosphere consistent with observations from 2011 through 2019 at 1 degree horizontal resolution and weekly frequency. Each ensemble member is an equally likely description of the atmosphere, and is also consistent with dynamics and physics of CAM6. The dataset also contains corresponding land surface values at 6-hourly frequency. This dataset is a reformatting, with no change to numerical values, of data from the "CAM6 Data Assimilation Research Testbed (DART) Reanalysis", DOI:10.5065/JG1E-8525.

CAncer MEtastases in LYmph nOdes challeNge (CAMELYON) Dataset

"This dataset contains the all data for the CAncer MEtastases in LYmph nOdes challeNge or CAMELYON. CAMELYON was the first challenge using whole-slide images in computational pathology and aimed to help pathologists identify breast cancer metastases in sentinel lymph nodes. Lymph node metastases are extremely important to find, as they indicate that the cancer is no longer localized and systemic treatment might be warranted. Searching for these metastases in H&E-stained tissue is difficult and time-consuming and AI algorithms can play a role in helping make this faster and more accurate.

CESM-HR

This dataset provides several global fields describing the state of atmosphere, ocean, land and ice from a high-resolution (0.1o for the ocean/ice models 0.25o for the land/atmosphere models) numerical earth system model, the Community Earth System Model (CESM, https://www.cesm.ucar.edu/). Texas A&M University (TAMU) and National Center for Atmospheric Research together with international partners collaboratively carried out a large set of high-resolution climate simulations, including a 500-year long preindustrial control simulation (PI-CTRL) described here. The CESM uses dynamic equations with a climatological (observations, long-term averaged) initial state of the earth system and the preindustrial greenhouse gas forcing (kept constant at the 1850 conditions) for making this PI-CTRL. Compared to standard low-resolution (1o) CMIP-class simulations, this high-resolution simulation dataset enables researchers to explore the contribution from processes at a wide range of spatial scales, from mesoscales to global scales, in shaping various observed climate phenomena with better accuracy.

CMAS Data Warehouse

CMAS Data Warehouse on AWS collects and disseminates meteorology, emissions and air quality model input and output for Community Multiscale Air Quality (CMAQ) Model Applications. This dataset is available as part of the AWS Open Data Program, therefore egress fees are not charged to either the host or the person downloading the data. This S3 bucket is maintained as a public service by the University of North Carolina's CMAS Center, the US EPA’s Office of Research and Development, and the US EPA’s Office of Air and Radiation. Metadata and DOIs for datasets included in the CMAS Data Warehouse are available from the CMAS Dataverse site: https://dataverse.unc.edu/dataverse/cmascenter

Caenorabditis Diversity Natural Resource

The Caenorhabditis Natural Diversity Resource (CaeNDR) is a data repository and analysis hub of wild strains of selfing Caenhorabditis species C. elegans, C. briggsae, and C. tropicalis from around the world to facilitate discovery of genetic variation across all three species through genome-wide association mappings to correlate genotype with phenotype and identify genetic variation underlying quantitative traits.

CoMMpass from the Multiple Myeloma Research Foundation

The Relating Clinical Outcomes in Multiple Myeloma to Personal Assessment of Genetic Profile study is the Multiple Myeloma Research Foundation (MMRF)’s landmark personalized medicine initiative. CoMMpass is a longitudinal observation study of around 1000 newly diagnosed myeloma patients receiving various standard approved treatments. The MMRF’s vision is to track the treatment and results for each CoMMpass patient so that someday the information can be used to guide decisions for newly diagnosed patients. CoMMpass checked on patients every 6 months for 8 years, collecting tissue samples, genetic, information, quality of life and various disease and clinical outcomes. The study has produced one of the largest genomic and clinical datasets of a single disease.

Community Earth System Model Large Ensemble (CESM LENS)

The Community Earth System Model (CESM) Large Ensemble Numerical Simulation (LENS) dataset includes a 40-member ensemble of climate simulations for the period 1920-2100 using historical data (1920-2005) or assuming the RCP8.5 greenhouse gas concentration scenario (2006-2100), as well as longer control runs based on pre-industrial conditions. The data comprise both surface (2D) and volumetric (3D) variables in the atmosphere, ocean, land, and ice domains. The total data volume of the original dataset is ~500TB, which has traditionally been stored as ~150,000 individual CF/NetCDF files on disk or magnetic tape made available through the NCAR Climate Data Gateway for download or via web services. NCAR has copied a subset (currently ~70 TB) of CESM LENS data to Amazon S3 as part of the AWS Public Datasets Program. To optimize for large-scale analytics we have represented the data as ~275 Zarr stores format accessible through the Python Xarray library. Each Zarr store contains a single physical variable for a given model run type and temporal frequency (monthly, daily, 6-hourly).

Daylight Map Distribution of OpenStreetMap

Daylight is a complete distribution of global, open map data that’s freely available with support from community and professional mapmakers. Meta combines the work of global contributors to projects like OpenStreetMap with quality and consistency checks from Daylight mapping partners to create a free, stable, and easy-to-use street-scale global map.

The Daylight Map Distribution contains a validated subset of the OpenStreetMap database. In addition to the standard OpenStreetMap PBF format, Daylight is available in two parquet formats that are optimized for AWS Athena including geometries (Points, LineStrings, Polygons, or MultiPolygons). First, Daylight OSM Features contains the nearly 1B renderable OSM features. Second, Daylight OSM Elements contains all of OSM, including all 7B nodes without attributes, and relations that do not contain geometries, such as turn restrictions.

Daylight Earth Table is a new data schema that classifies OpenStreetMap-style tags into a 3-level ontology (theme, class, subclass) and is the result of running the earth table classification over the latest release (v1.18) of the Daylight Map Distribution. The Daylight Earth Table is available as parquet files on Amazon S3.

EEGDash on AWS

The EEG-DaSh (EEG Data Sharing) data archive is a large-scale data-sharing resource for magnetoencephalography and electroencephalography (MEEG) data hosted at the Swartz Center for Computational Neuroscience (SCCN), UC San Diego. It provides curated, BIDS-formatted datasets for neuroscience research, machine learning, and deep learning applications. The archive spans three S3 buckets: (1) the EEGDash bucket for data served through the EEGDash platform, (2) the NEMAR archive containing datasets contributed through the NEMAR (Neuroelectromagnetic Data Archive and Tools Resource) platform, which serves as the upstream data source for EEGDash, and (3) a competition-specific collection of datasets adapted and preprocessed for machine learning benchmarks and competitions.

ESA WorldCover Sentinel-1 and Sentinel-2 10m Annual Composites

The WorldCover 10m Annual Composites were produced, as part of the European Space Agency (ESA) WorldCover project, from the yearly Copernicus Sentinel-1 and Sentinel-2 archives for both years 2020 and 2021. These global mosaics consists of four products composites. A Sentinel-2 RGBNIR yearly median composite for bands B02, B03, B04, B08. A Sentinel-2 SWIR yearly median composite for bands B11 and B12. A Sentinel-2 NDVI yearly percentiles composite (NDVI 90th, NDVI 50th NDVI 10th percentiles). A Sentinel-1 GAMMA0 yearly median composite for bands VV, VH and VH/VV (power scaled). Each product is delivered as a series of Cloud-Optimized GeoTIFFs (COGs) in WSG84 projection in a grid of 1 by 1 degrees and at 0.3 arc seconds resolution (approx. 10m), except for the SWIR composite which is delivered at 0.6 arc seconds (approx. 20m). The Sentinel-2 composites were produced from the L2A archive. The GAMMA0 composite was produced by pre-processing the Sentinel-1 GRD products using GAMMA software.

End-Use Load Profiles for the U.S. Building Stock

The U.S. Department of Energy (DOE) funded a three-year project, End-Use Load Profiles for the U.S. Building Stock, that culminated in this publicly available dataset of calibrated and validated 15-minute resolution load profiles for all major residential and commercial building types and end uses, across all climate regions in the United States. These EULPs were created by calibrating the ResStock and ComStock physics-based building stock models using many different measured datasets, as described here. This dataset includes load profiles for both the baseline building stock and the building stock with various energy efficiency, electrification, and demand flexibility upgrades applied.

GPM IMERG Early Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHE) at GES DISC

Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases.The half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme.The KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the “forecast” and the IR estimates as the “observations”, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about ±90 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR.The IMERG system is run twice in near-real time:"Early" multi-satellite product ~4 hr after observation time using only forward morphing and "Late" multi-satellite product ~14 hr after observation time, using both forward and backward morphing and once after the monthly gauge analysis is received:"Final", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.In V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users.Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

GPM IMERG Final Precipitation L3 1 month 0.1 degree x 0.1 degree V07 (GPM_3IMERGM) at GES DISC

Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases.The half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme.The KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the “forecast” and the IR estimates as the “observations”, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about ±90 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR.The IMERG system is run twice in near-real time:"Early" multi-satellite product ~4 hr after observation time using only forward morphing and "Late" multi-satellite product ~14 hr after observation time, using both forward and backward morphing and once after the monthly gauge analysis is received:"Final", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.In V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users.Briefly describing the Final Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the CORRA product (because it is presumed to be the best snapshot TRMM/GPM estimate after adjustment to the monthly GPCP SG), then "forward/backward morphed" and combined with microwave precipitation-calibrated geo-IR fields, and adjusted with seasonal GPCP SG surface precipitation data to provide half-hourly and monthly precipitation estimates on a 0.1°x0.1° (roughly 10x10 km) grid over the globe. Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. The current period of record is June 2000 to the present (delayed by about 4 months). Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHH) at GES DISC

Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases.The half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme.The KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the “forecast” and the IR estimates as the “observations”, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about ±90 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR.The IMERG system is run twice in near-real time:"Early" multi-satellite product ~4 hr after observation time using only forward morphing and "Late" multi-satellite product ~14 hr after observation time, using both forward and backward morphing and once after the monthly gauge analysis is received:"Final", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.In V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users.Briefly describing the Final Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the CORRA product (because it is presumed to be the best snapshot TRMM/GPM estimate after adjustment to the monthly GPCP SG), then "forward/backward morphed" and combined with microwave precipitation-calibrated geo-IR fields, and adjusted with seasonal GPCP SG surface precipitation data to provide half-hourly and monthly precipitation estimates on a 0.1°x0.1° (roughly 10x10 km) grid over the globe. Precipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. The current period of record is June 2000 to the present (delayed by about 4 months). Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

GPM IMERG Late Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHL) at GES DISC

Version 07B is the current version of the IMERG data sets. Older versions will no longer be available and have been superseded by Version 07.\n\nThe Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the multi-satellite precipitation product for the U.S. GPM team.\n\nThe precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2021 version of the Goddard Profiling Algorithm (GPROF2021), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Note that CORRA is adjusted to the monthly Global Precipitation Climatology Project (GPCP) Satellite-Gauge (SG) product over high-latitude ocean to correct known biases.\n\nThe half-hourly intercalibrated merged PMW estimates are then input to both a Morphing-Kalman Filter (KF) Lagrangian time interpolation scheme based on work by the Climate Prediction Center (CPC) and the Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Dynamic Infrared–Rain Rate (PDIR) re-calibration scheme. In parallel, CPC assembles the zenith-angle-corrected, intercalibrated merged geo-IR fields and forwards them to PPS for input to the PERSIANN-CCS algorithm (supported by an asynchronous re-calibration cycle) which are then input to the KF morphing (quasi-Lagrangian time interpolation) scheme.\n\nThe KF morphing (supported by an asynchronous KF weights updating cycle) uses the PMW and IR estimates to create half-hourly estimates. Motion vectors for the morphing are computed by maximizing the pattern correlation of successive hours within each of the precipitation (PRECTOT), total precipitable liquid water (TQL), and vertically integrated vapor (TQV) data fields provided by the Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) and Goddard Earth Observing System model Version 5 (GEOS-5) Forward Processing (FP) for the post-real-time (Final) Run and the near-real-time (Early and Late) Runs, respectively. The vectors from PRECTOT are chosen if available, else from TQL, if available, else from TQV. The KF uses the morphed data as the “forecast” and the IR estimates as the “observations”, with weighting that depends on the time interval(s) away from the microwave overpass time. The IR becomes important after about ±90 minutes away from the overpass time. Variable averaging in the KF is accounted for in a routine (Scheme for Histogram Adjustment with Ranked Precipitation Estimates in the Neighborhood, or SHARPEN) that compares the local histogram of KF morphed precipitation to the local histogram of forward- and backward-morphed microwave data and the IR.\n\nThe IMERG system is run twice in near-real time:\n\n"Early" multi-satellite product ~4 hr after observation time using only forward morphing and\n"Late" multi-satellite product ~14 hr after observation time, using both forward and backward morphing\nand once after the monthly gauge analysis is received:\n\n"Final", satellite-gauge product ~4 months after the observation month, using both forward and backward morphing and including monthly gauge analyses.\n\nIn V07, the near-real-time Early and Late half-hourly estimates have a monthly climatological concluding calibration based on averaging the concluding calibrations computed in the Final, while in the post-real-time Final Run the multi-satellite half-hourly estimates are adjusted so that they sum to the Final Run monthly satellite-gauge combination. In all cases the output contains multiple fields that provide information on the input data, selected intermediate fields, and estimation quality. In general, the complete calibrated precipitation, precipitation, is the data field of choice for most users.\n\nPrecipitation phase is a diagnostic variable computed using analyses of surface temperature, humidity, and pressure. \n\n\n\nRead our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

Global 30m Height Above Nearest Drainage (HAND)

Height Above Nearest Drainage (HAND) is a terrain model that normalizes topography to the relative heights along the drainage network and is used to describe the relative soil gravitational potentials or the local drainage potentials. Each pixel value represents the vertical distance to the nearest drainage. The HAND data provides near-worldwide land coverage at 30 meters and was produced from the 2021 release of the Copernicus GLO-30 Public DEM as distributed in the Registry of Open Data on AWS.

Global Seasonal Sentinel-1 Interferometric Coherence and Backscatter Data Set

This data set is the first-of-its-kind spatial representation of multi-seasonal, global SAR repeat-pass interferometric coherence and backscatter signatures. Global coverage comprises all land masses and ice sheets from 82 degrees northern to 79 degrees southern latitude. The data set is derived from high-resolution multi-temporal repeat-pass interferometric processing of about 205,000 Sentinel-1 Single-Look-Complex data acquired in Interferometric Wide-Swath mode (Sentinel-1 IW mode) from 1-Dec-2019 to 30-Nov-2020. The data set was developed by Earth Big Data LLC and Gamma Remote Sensing AG, under contract for NASA's Jet Propulsion Laboratory. The data set covers four sets of seasonal (DJF/MAM/JJA/SON) metrics: 1) Median 6-, 12-, 18-, 24-, 36-, and 48-day repeat coherence estimates for C-band VV and HH polarized data, 2) Mean backscatter (gamma naught) for VV, VH, HH, and HV polarizations, 3) Seasonal coherence decay model parameters rho, tau, and rmse, 4) Local incidence and layover/shadow regions for all relative orbits (175 orbits). Note that in the data set filenames the seasons were referred to as northern hemisphere winter (DJF), spring (MAM), summer (JJA), and fall (SON). The data set is available in two main components: 1) 1x1 degree tiles. Each tile contains GeoTiffs at 3 arcsec pixel spacing of all metrics available in the tile. (s3://sentinel-1-global-coherence-earthbigdata/data/tiles/), 2) Global mosaicked tiles as cloud optimized GeoTIFFs (COG) at 0.01 degree pixel spacing (s3://sentinel-1-global-coherence-earthbigdata/data/mosaics/) for each of the computed metrics.

High resolution, annual cropland and landcover maps for selected African countries

High resolution, annual cropland and landcover maps for selected African countries developed by Clark University's Agricultural Impacts Research Group using various machine learning approaches applied to Planet imagery, including field boundary and cultivated frequency maps, as well as multi-class land cover.

IBL Neuropixels Brainwide Map on AWS

Electrophysiological recordings of mouse brain activity acquired during a decision making task in multiple autism mice models.

JMA Himawari-8/9

Himawari-9, stationed at 140.7E, owned and operated by the Japan Meteorological Agency (JMA), is a geostationary meteorological satellite, with Himawari-8 as on-orbit back-up, that provides constant and uniform coverage of east Asia, and the west and central Pacific regions from around 35,800 km above the equator with an orbit corresponding to the period of the earth’s rotation. This allows JMA weather offices to perform uninterrupted observation of environmental phenomena such as typhoons, volcanoes, and general weather systems. Archive data back to July 2015 is available for Full Disk (AHI-L1b-FLDK) products in the bucket. For questions regarding Himawari-9 imagery specifications, visit the JMA site at https://www.data.jma.go.jp/mscweb/en/himawari89/space_segment/spsg_ahi.html. For examples of Full Disk Himawari-9 imagery coverage, visit the NOAA Himawari-9 data page at https://www.ospo.noaa.gov/Products/imagery/himawari.html.

MONKEY

This dataset contains the training data for the Machine learning for Optimal detection of iNflammatory cells in the KidnEY or MONKEY challenge. The MONKEY challenge focuses on the automated detection and classification of inflammatory cells, specifically monocytes and lymphocytes, in kidney transplant biopsies using Periodic acid-Schiff (PAS) stained whole-slide images (WSI). It contains 80 WSI, collected from 4 different pathology institutes, with annotated regions of interest. For each WSI up to 3 different PAS scans and one IHC slide scan are available. This dataset and challenge support the development of AI models that can aid in the diagnostic process, reduce pathologists’ workload, and improve patient outcomes in renal transplantation.

Meta-Organized Stimuli And fMRI Imaging data for Computational modeling (MOSAIC)

This extensible dataset, MOSAIC, aggregates individual functional magnetic resonance imaging (fMRI) datasets by leveraging a shared preprocessing pipeline and stimulus curation procedure. This dataset aggregation procedure achieves the scale necessary for neural network training and the diversity needed for generalizable results.

NASA / USGS Lunar Orbiter Laser Altimeter Cloud Optimized Point Cloud

The lunar orbiter laser altimeter (LOLA) has collected and released almost 7 billion individual laser altimeter returns from the lunar surface. This dataset includes individual altimetry returns scraped from the Planetary Data System (PDS) LOLA Reduced Data Record (RDR) Query Tool, V2.0. Data are organized in 15˚ x 15˚ (longitude/latitude) sections, compressed and encoded into the Cloud Optimized Point Cloud (COPC) file format, and collected into a Spatio-Temporal Asset Catalog (STAC) collection for query and analysis. The data are in latitude, longitude, and radius (X, Y, Z) format with the proper IAU 2015 30100 well-known text projection string. These data are in the -180 to 180, center longitude 0 domain. Users of this data set are encouraged to use the Point Data Abstract Library (PDAL) STAC driver to query at the collection level or the COPC driver to query individual COPC tiles within the dataset. Queries of these data using bounding boxes, buffered points, or other geometries should use the -180 to 180 longitude domain (converting from 0-360, clone 180 as needed).

NASA Earth Exchange (NEX) Data Collection

A collection of downscaled climate change projections, derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 5 (CMIP5) [Taylor et al. 2012] and across the four greenhouse gas emissions scenarios known as Representative Concentration Pathways (RCPs) [Meinshausen et al. 2011]. The NASA Earth Exchange group maintains the NEX-DCP30 (CMIP5), NEX-GDDP (CMIP5), and LOCA (CMIP5).NOTE: The S3 Bucket location for this dataset changed on 5/6/2026

NIH NCBI Sequence Read Archive (SRA) on AWS

The Sequence Read Archive (SRA), produced by the National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM) at the National Institutes of Health (NIH), stores raw DNA sequencing data and alignment information from high-throughput sequencing platforms. The SRA provides open access to these biological sequence data to support the research community's efforts to enhance reproducibility and make new discoveries by comparing data sets. Buckets in this registry contain public SRA data in the original (user submitted) format from select high value and newly-released studies as well as all public-access SRA formatted ETL+BQS data. Also included is all SRA metadata that can be leveraged for attribute-based data discovery.

NOAA National Air Quality Forecast Capability (NAQFC) Regional Model Guidance

The National Air Quality Forecasting Capability (NAQFC) dataset contains model-generated air quality (AQ) forecast guidance from three different prediction systems. The first system is a coupled weather and atmospheric chemistry numerical forecast model, known as the Air Quality Model (AQM). It is used to produce forecast guidance for ozone (O3) and particulate matter that is less than or equal to 2.5 micrometers in diameter (PM2.5). Prior to May 14, 2024, AQM predictions were derived using the EPA’s Community Multiscale Air Quality (CMAQ) model, driven by meteorological fields from NCEP’s operational weather forecast models, specifically the North American Mesoscale Model (NAM; prior to 20 July 2021) and the Global Forecast System (GFS; beginning 20 July 2021). Since May 14, 2024, AQM guidance has been produced by a unique application within the community-based Unified Forecast System (UFS). The core model components in this application are derived directly from the fully online-coupled UFS-based weather and CMAQ-based chemistry models. In addition, it incorporates information related to chemical and particle source emissions as it integrates forward in time, including anthropogenic chemical emissions provided by the EPA, fire emissions from NOAA/NESDIS, and airborne particles generated by human activities and those predicted to be generated by wind-driven erosion and biosphere at ground level. The NCEP NAQFC AQM output fields in this archive include model raw and bias-corrected predictions dating back to 1 January 2020, all generated by the contemporaneous operational AQM, beginning with AQMv5 in 2020, transitioning to AQMv6 on 20 July 2021, and to AQMv7 on 14 May 2024. The length of each forecast was 48 hours prior to the implementation of AQMv6, and has been 72 hours ever since. The history of AQM upgrades is documented here

The second prediction is known as the Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT). It is a widely used atmospheric transport and dispersion model containing an internal dust-generation module. It provides forecast guidance for atmospheric dust concentration and, prior to 28 June 2022, it also provided the NAQFC forecast guidance for smoke. Starting on that date, the third prediction system, a regional numerical weather prediction (NWP) model known as the Rapid Refresh (RAP) model, subsumed HYSPLIT for operational smoke guidance, simulating the emission, transport, and deposition of smoke particles that originate from biomass burning (fires) and anthropogenic sources.

The output from each of these modeling systems is generated over three separate domains, one covering CONUS, another over Alaska, and the other over Hawaii. Currently, for this archive, the O3, PM2.5, and smoke output is available over all three domains, while dust products are available only over the CONUS domain. The predicted concentrations of all species in the lowest model layer (i.e., the layer in contact with the surface) are available, as are vertically integrated values of smoke and dust. The data is gridded horizontally within each domain, with a grid spacing of approximately 5 km over CONUS, 6 km over Alaska, and 2.5 km over Hawaii. O3 concentrations are provided in parts per billion (PPB), while the concentrations of all other species are quantified in units of micrograms per cubic meter (ug/m3), except for the column-integrated smoke values which are expressed in units of milligrams per square meter (mg/m2).

Temporally, O3 and PM2.5 are available as maximum and/or averaged values over various time periods, selected in part for consistency with the EPA’s National Ambient Air Quality Standards. Specifically, O3 is available in both 1-hour and 8-hour (backward calculated) averages, as well as preceding 1-hour and 8-hour maximum values. Similarly, PM2.5 is available in 1-hour and 24-hour average values and 24-hour maximum values. In addition, all O3 and PM2.5 fields are available with bias-corrected magnitudes, based on derived historical model biases relative to observations.

The AQM produces hourly forecast guidance for O3 and PM2.5 up to 72 hours twice per day. Smoke guidance is available up to 51 hours from once-per-day RAP forecasts, while dust guidance from HYSPLIT is available up to 48 hours.

New Zealand Coastal Elevation

The New Zealand Coastal Elevation dataset consists of New Zealand's publicly owned coastal digital elevation models, which are freely available to use under an open licence. The data consists of bare earth (DEM) data that traverses the coastal zone, including the seabed down to approximately 25m in depth. Data is provided as nationally consistent 1m resolution tiles derived from LiDAR surveys.All of the coastal elevation files are Cloud Optimised GeoTIFFs using LERC compression for the main grid and LERC compression with lower max_z_error for the overviews. These elevation files are accompanied by STAC metadata. The coastal elevation data is organised by region and survey.

Normalized Difference Urban Index (NDUI)

NDUI is combined with cloud shadow-free Landsat Normalized Difference Vegetation Index (NDVI) composite and DMSP/OLS Night Time Light (NTL) to characterize global urban areas at a 30 m resolution,and it can greatly enhance urban areas, which can then be easily distinguished from bare lands including fallows and deserts. With the capability to delineate urban boundaries and, at the same time, to present sufficient spatial details within urban areas, the NDUI has the potential for urbanization studies at regional and global scales.

OPERA Dynamic Surface Water Extent from Harmonized Landsat Sentinel-2 product (Version 1)

This dataset contains Level-3 Dynamic OPERA surface water extent product version 1. The data are validated surface water extent observations beginning April 2023. Known issues and caveats on usage are described under Documentation. The input dataset for generating each product is the Harmonized Landsat-8 and Sentinel-2A/B/C (HLS) product version 2.0. HLS products provide surface reflectance (SR) data from the Operational Land Imager (OLI) aboard the Landsat 8 satellite and the MultiSpectral Instrument (MSI) aboard the Sentinel-2A/B/C satellite. The surface water extent products are distributed over projected map coordinates using the Universal Transverse Mercator (UTM) projection. Each UTM tile covers an area of 109.8 km × 109.8 km. This area is divided into 3,660 rows and 3,660 columns at 30-m pixel spacing. Each product is distributed as a set of 10 GeoTIFF (Geographic Tagged Image File Format) files including water classification, associated confidence, land cover classification, terrain shadow layer, cloud/cloud-shadow classification, Digital elevation model (DEM), and Diagnostic layer.

The digital elevation model (DEM) provided as a layer of the DSWx-HLS product (band 10) was generated using the Copernicus DEM 30-m and Copernicus DEM 90-m models provided by the European Space Agency. The Copernicus DEM 30-m and Copernicus DEM 90-m were produced using Copernicus WorldDEM-30 © DLR e.V. 2010-2014 and © Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA; all rights reserved. The organizations in charge of the OPERA project, the Copernicus programme, and Airbus Defence and Space GmbH by law or by delegation do not assume any legal responsibility or liability, whether express or implied, arising from the use of this DEM.

The OPERA DSWx-HLS product contains modified Copernicus Sentinel data (2023-2025).

To access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov Read our doc on how to get AWS Credentials to retrieve this data: https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME

Open-Meteo Weather API Database

Open-Meteo integrates weather models from reputable national weather services, offering a swift and efficient weather API. Real-time weather forecasts are unified into a time-series database that provides historical and future weather data for any location worldwide.Through Open-Meteo on AWS Open Data, you can download the Open-Meteo weather database and analysis weather data locally. Docker images are provided to download data and to expose an HTTP API endpoint. Using Open-Meteo SDKs, you can seamlessly integrate weather data into your Python, Typescript, Swift, Kotlin, or Java applications.The entire source code is open-source, and contributions are welcome!To get started, familiarize yourself with the available weather models and explore tutorials on downloading 80 years of historical weather data from ERA5 or set up your own real-time weather API with high-resolution models.If you have any questions, feel free to reach out on GitHub discussions.

OpenStreetMap on AWS

OSM is a free, editable map of the world, created and maintained by volunteers. Regular OSM data archives are made available in Amazon S3 in both standard formats (OSM PBF, XML) and cloud-native formats optimized for analytics workloads.

Overture Maps Foundation Open Map Data

Overture is a collaboratively built, global, open map data project for developers who build map services or use geospatial data. Overture Open Map Data contains data that are standardized under the themes of Admins, Base, Buildings, Places, and Transportation. Overture also includes a Global Entity Reference System (GERS) which encodes map data to a shared universal reference. Beginning with the Overture 2023-11-14-alpha.0 release, the data is available as cloud-native GeoParquet files.

Ozone Monitoring Instrument (OMI) / Aura NO2 Tropospheric Column Density

NO2 tropospheric column density, screened for CloudFraction < 30% global daily composite at 0.25 degree resolution for the temporal range of 2004 to May 2020. Original archive data in HDF5 has been processed into a Cloud-Optimized GeoTiff (COG) format. Quality Assurance - This data has been validated by the NASA Science Team at Goddard Space Flight Center.Cautionary Note: https://airquality.gsfc.nasa.gov/caution-interpretation.

Prefeitura Municipal de São Paulo (PMSP) LiDAR Point Cloud

The objective of the Mapa 3D Digital da Cidade (M3DC) of the São Paulo City Hall is to publish LiDAR point cloud data. The initial data was acquired in 2017 by aerial surveying and future data will be added. This publicly accessible dataset is provided in the Entwine Point Tiles format as a lossless octree, full density, based on LASzip (LAZ) encoding.

Protein Data Bank 3D Structural Biology Data

The "Protein Data Bank (PDB) archive" was established in 1971 as the first open-access digital data archive in biology. It is a collection of three-dimensional (3D) atomic-level structures of biological macromolecules (i.e., proteins, DNA, and RNA) and their complexes with one another and various small-molecule ligands (e.g., US FDA approved drugs, enzyme co-factors). For each PDB entry (unique identifier: 1abc or PDB_0000001abc) multiple data files contain information about the 3D atomic coordinates, sequences of biological macromolecules, information about any small molecules/ligands present in the entry, details about the structure-determination experiment, authors and publication information, experimental data, and the wwPDB validation report. Additional content stored in the archive includes documentation, summary reports, and software (among others). The PDB is a jointly-managed core archive of the Worldwide Protein Data Bank partnership [RCSB Protein Data Bank (RCSB PDB, rcsb.org); Protein Data Bank in Europe (PDBe, pdbe.org); Protein Data Bank Japan (PDBj, pdbj.org); Electron Microscopy Data Bank (EMDB, emdb-empiar.org); and Biological Magnetic Resonance Bank (BMRB, bmrb.io)]. RCSB PDB serves as the wwPDB-designated Archive Keeper for the Protein Data Bank. Additional wwPDB Core Archives are as follows: Electron Microscopy Data Bank (wwPDB-designated Archive Keeper: EMDB) Biological Magnetic Resonance Bank (wwPDB-designated Archive Keeper: BMRB)

RACECAR Dataset

The RACECAR dataset is the first open dataset for full-scale and high-speed autonomous racing. Multi-modal sensor data has been collected from fully autonomous Indy race cars operating at speeds of up to 170 mph (273 kph). Six teams who raced in the Indy Autonomous Challenge during 2021-22 have contributed to this dataset. The dataset spans 11 interesting racing scenarios across two race tracks which include solo laps, multi-agent laps, overtaking situations, high-accelerations, banked tracks, obstacle avoidance, pit entry and exit at different speeds. The data is organized and released in both ROS2 and nuScenes format. We have also developed the ROS2-to-nuScenes conversion library to achieve this. The RACECAR data is unique because of the high-speed environment of autonomous racing and is suitable to explore issues regarding localization, object detection and tracking (LiDAR, Radar, and Camera), and mapping that arise at the limits of operation of the autonomous vehicle.

SPARC: Datasets bridging the body and the brain

The SPARC Datasets comprise a collection of scientific data that is focused on bridging the body and the brain. The datasets focus on neural connectivity, organ innervation and detailed anatomical mapping of the peripheral nervous system. SPARC datasets distinguish themselves from other data resources through its multi-modal approach to scientific data and integrates molecular, imaging, timeseries and other datatypes associated with the interaction between the peripheral nervous system and organs. SPARC data provides a unique integrated effort to develop next generation mapping of anatomical and functional connectivity of the nervous system.

SPARTAN Data

SPARTAN (Surface PARTiculate mAtter Network) measures and provides surface ambient particulate matter (PM2.5 and PM10) concentration and the chemical composition around the world, with the purpose of connecting ground-based PM2.5 and satellite remote sensing.

Sanborn Maps Data Package

The dataset contains metadata records for 50,600 maps from the Sanborn Fire Insurance Maps collection and their corresponding 440,048 JPEG images. The Sanborn collection at Library of Congress includes over fifty thousand editions of fire insurance maps comprising almost seven hundred thousand individual sheets. The Library of Congress holdings represent the largest extant collection of maps produced by the Sanborn Map Company.

Sea Around Us Global Fisheries Catch Data

The project presents Sea Around Us Global Fisheries Catch Data aggregated at EEZ level. The data are computed from reconstructed catches from various official fisheries statistics, scientific, technical and policy reports about the fisheries, and includes estimation of discards, unreported and illegal catch data from all maritime countries and major territories of the world.This project was the result of a work between Sea Around Us and the CIC programme, a collaborative programme between the University of British Columbia (UBC) and AWS.

Sofar Spotter Archive

This dataset includes archival hourly data from the [Sofar Spotter buoy global network] (https://weather.sofarocean.com/) from 2019 to March 2022.

SondeHub Radiosonde Telemetry

SondeHub Radiosonde telemetry contains global radiosonde (weather balloon) data captured by SondeHub from our participating radiosonde_auto_rx receiving stations. radiosonde_auto_rx is a open source project aimed at receiving and decoding telemetry from airborne radiosondes using software-defined-radio techniques, enabling study of the telemetry and sometimes recovery of the radiosonde itself. Currently 313 receiver stations are providing data for an average of 384 radiosondes a day. The data within this repository contains received telemetry frames, including radiosonde type, gps position, and for some radiosondes atmospheric sensor data (temperature, humidity, pressure). As the downlinked telemetry does not always contain calibration information, any atmospheric sensor data should be considered to be uncalibrated. Note that radiosonde_auto_rx does not have sensor data support for all radiosonde types.

The Human Connectome Project

The Human Connectome Project (HCP Young Adult, HCP-YA) is mapping the healthy human connectome by collecting and freely distributing neuroimaging and behavioral data on 1,200 normal young adults, aged 22-35.

Vermont Open Geospatial on AWS

The State of Vermont has partnered with Amazon's Open Data Initative to make a wide range of geospatial data available in the public domain. Vermont acquires aerial imagery and LiDAR during leaf-off conditions. The imagery typically ranges from 30-centimeter to 15-centimeter in resolution and is available from Vermont's Amazon S3 bucket in a Cloud Optimized GeoTiff (COG) format. LiDAR data has been acquired and is available as USGS Quality Level-1 (QL1) and Level-2 (QL2) compliant datasets in COG format. Geospatial datasets derived from imagery and/or lidar are also available as COGs, including high resolution landcover.

A region-wide, multi-year set of crop field boundary labels for Africa

Crop field boundaries digitized in Planet imagery collected across Africa between 2017 and 2023, developed by Farmerline, Spatial Collective, and the Agricultural Impacts Research Group at Clark University, with support from the Lacuna Fund (Estes et al, 2024; Wussah et al. (2023)). This dataset has been further supplemented by additional labels collected primarily for for 2018 over a subset of countries, which provide an example of their application in training and validating a CNN-based cropland mapping model (Khallaghi et al. 2025).

APEX-CONNECTS

The BRAIN Initiative Connectivity Across Scales (CONNECTS) program is working to create detailed maps of brain wiring across different species and scales, using advanced imaging technologies. APEX supports this effort by serving as a central hub that brings together and coordinates data and tools from research focused on brain connectivity in humans and animals. Together, these efforts aim to improve our understanding of how the brain is structured and functions.

ASKAP Radio Telescope

ASKAP is the CSIRO’s newest radio telescope. It is situated at the Inyarrimanha Ilgari Bundara, the CSIRO Murchison Radio-astronomy Observatory on Wajarri Yamaji Country in the Murchison region of Western Australia, about 800 km north of Perth. ASKAP consists of 36 12m dishes, spread-out as far as 6km apart. It uses a new technology called Phased Array Feeds (PAFs), which allows it to see more of the sky at once. This novel technology allows ASKAP to achieve extremely high survey speed, making it one of the best instruments in the world for mapping the sky at radio wavelengths. Initial dataset available - The Rapid ASKAP Continuum Survey (RACS)RACS is the first large-area survey completed with ASKAP. This survey is revolutionary as the entire sky was observed in a matter of weeks, doing what previously took telescopes years to do. RACS initially covered the whole sky at 890 MHz (RACS-Low), and has since expanded to ASKAP’s other bands (1.4 and 1.7 GHz). RACS also covers the sky in multiple epochs, with a second epoch of RACS-Low and RACS-Mid obtained and processed. RACS provides astronomers with a unique opportunity to study the radio sky and radio populations, in particular supermassive blackholes (active galactic nuclei) and their role in galaxy evolution. The multi-epoch approach also allows a study of the transient sky and testing and verification of calibration methods. The large area allows for cosmological studies, such as a search for anisotropy in the galaxy population, or cosmic dipole.

BUSCO Datasets

Lineage datasets for use with BUSCO software package. Each dataset contains HMM profiles for clade specific, universal, single-copy marker genes. Datasets are available across archaea, bacteria, eukaryota and virus domains. The repository also includes necessary data files for phylogenetic placement of an input assembly.

Basic Local Alignment Sequences Tool (BLAST) Databases

A centralized repository of pre-formatted BLAST databases created by the National Center for Biotechnology Information (NCBI).

Chalmers Cloud Ice Climatology

The Chalmers Cloud Ice Climatology (CCIC) is a novel, deep-learning-based climate record of ice-particle concentrations in the atmosphere. CCIC results are available at high spatial and temporal resolution (0.07° / 3 h from 1983, 0.036° / 30 min from 2000) and thus ideally suited for evaluating high-resolution weather and climate models or studying individual weather systems.

Community Earth System Model v2 Large Ensemble (CESM2 LENS)

The US National Center for Atmospheric Research partnered with the IBS Center for Climate Physics in South Korea to generate the CESM2 Large Ensemble which consists of 100 ensemble members at 1 degree spatial resolution covering the period 1850-2100 under CMIP6 historical and SSP370 future radiative forcing scenarios. Data sets from this ensemble were made downloadable via the Climate Data Gateway on June 14th, 2021. NCAR has copied a subset (currently ~500 TB) of CESM2 LENS data to Amazon S3 as part of the AWS Public Datasets Program. To optimize for large-scale analytics we have represented the data as ~275 Zarr stores format accessible through the Python Xarray library. Each Zarr store contains a single physical variable for a given model run type and temporal frequency (monthly, daily).

Community coral reef image classification training data

Community-sourced repository of coral reef image classification training data, including continually updated confirmed annotations from MERMAID

Data to Science Catalog

A user-generated geospatial data collection maintained by the Data to Science platform. Contributions vary by project, but typically include cloud-optimized datasets such as Cloud-Optimized GeoTIFFs (COGs) and Cloud-Optimized Point Clouds (COPCs), designed for efficient streaming, visualization, and analysis in modern geospatial applications.

Demand-Side Grid (dsgrid) Toolkit

Projects that use the dsgrid toolkit assemble bottom-up descriptions of electricity demand and related data that are highly resolved geographically, temporally, and sectorally. Typically modelers describe multiple scenarios of future energy use at hourly resolution, suitable for inclusion in long-term power system planning models, i.e., capacity expansion and production cost models.

ECMWF real-time forecasts

These products are a subset of the ECMWF real-time forecast data and are made available to the public free of charge. They are based on the medium-range (high-resolution and ensembles) forecast models. Note: The ECMWF Open Data Portal provides a rolling archive (most recent forecast runs), while the AWS replica bucket is updated as new data are published and may retain older data conventions/versions over time.

Earth Radio Occultation

This is an updating archive of radio occultation (RO) data using the transmitters of the Global Navigation Satellite Systems (GNSS) as generated and processed by the COSMIC DAAC (ucar), the Jet Propulsion Laboratory (jpl) of the California Institute of Technology, and the Radio Occultation Meteorology Satellite Application Facility (romsaf). The contributions for ucar and romsaf are currently active.

This dataset is funded by the NASA Earth Science Data Systems and the Advancing Collaborative Connections for Earth System Science (ACCESS) 2019 program.

Encyclopedia of DNA Elements (ENCODE)

The Encyclopedia of DNA Elements (ENCODE) Consortium is an international collaboration of research groups funded by the National Human Genome Research Institute (NHGRI). The goal of ENCODE is to build a comprehensive parts list of functional elements in the human genome, including elements that act at the protein and RNA levels, and regulatory elements that control cells and circumstances in which a gene is active. ENCODE investigators employ a variety of assays and methods to identify functional elements. The discovery and annotation of gene elements is accomplished primarily by sequencing a diverse range of RNA sources, comparative genomics, integrative bioinformatic methods, and human curation. Regulatory elements are typically investigated through DNA hypersensitivity assays, assays of DNA methylation, and immunoprecipitation (IP) of proteins that interact with DNA and RNA, i.e., modified histones, transcription factors, chromatin regulators, and RNA-binding proteins, followed by sequencing.

Epigenomes of the Human Pangenome Reference Consortium (HPRC) Release 2

The Human Pangenome Reference Consortium (HPRC) Release 2 represents a landmark achievement in genomics, providing high-quality phased genome assemblies from over 200 individuals with comprehensive functional genomics data. The HPRC Epigenome Browser provides researchers a way to explore all epigenomics data generated by release 2. The HPRC Epigenome Browser (HPRCEB) is a modern, interactive web portal that democratizes access to HPRC Release 2 epigenomics data through an intuitive interface supporting genome selection, data visualization, and bulk download capabilities. The portal integrates genome assemblies, DNA methylation profiles, gene expression data, and chromatin accessibility measurements across diverse populations, enabling researchers to efficiently identify and retrieve datasets matching their specific research needs.

Epilepsy.Science

Epilepsy.Science comprise a set of datasets focused on Epilepsy Research that span both Clinical Data and Pre-clinical data. Datasets are contributed by the Epilepsy Research community and published using a standardized structure and metadata. Clinical datasets include de-identified subject information, EEG, and clinical imaging.

FoMo - A Multi-Season Dataset for Robot Navigation in Forêt Montmorency

The FoMo dataset is a multi-season collection recorded in a boreal forest environment, featuring deep snow, off-road terrain, steep slopes, and highly variable weather. It provides synchronized multi-modal sensor data—including two lidars (RoboSense and Leishen), an FMCW radar (Navtech), stereo and monocular cameras, dual IMUs, wheel odometry, power data, calibration sequences, and precise ground-truth trajectories via GNSS-PPK fusion. Designed to support research on robust robot autonomy under adverse conditions, FoMo includes repeated traversals of six trajectories of varying complexity for long-term SLAM and odometry evaluation, as well as rich metadata such as one-minute weather station measurements. The dataset is intended to challenge state-of-the-art SLAM, localization, traversability analysis, and multi-season robotics research.

GEOGLOWS Hydrological Model Version 2

GEOGLOWS is the Group on Earth Observation's Global Water Sustainability Program. It coordinates efforts from public and private entities to make application ready river data more accessible and sustainably available to underdeveloped regions. The GEOGLOWS Hydrological Model provides a retrospective and daily forecast of global river discharge at 7 million river sub-basins. The stream network is a hydrologically conditioned subset of the TDX-Hydro streams and basins data produced by the United State's National Geospatial Intelligence Agency. The daily forecast provides 3 hourly average discharge in a 51 member ensemble and 15 day lead time derived from the ECMWF Integrated Forecast System (IFS). The retrospective simulation is derived from ERA5 climate reanalysis data and provides daily average streamflow beginning on 1 January 1940. New forecasts are uploaded daily and the retrospective simulation is updated weekly on Sundays to keep the lag time between 5 and 12 days.

The geoglows-v2 bucket contains: (1) model configuration files used to generate the simulations, (2) the GIS streams datasets used by the model, (3) the GIS streams datasets optimized for visualizations used by Esri's Living Atlas layer, (4) several supporting table of metadata including country names, river names, hydrological properties used for modeling.

The geoglows-v2-forecasts bucket contains: (1) daily 15 forecasts in zarr format optimized for time series queries of all ensemble members in the prediction, (2) CSV formatted summary files optimized for producing time series animated web maps for the entire global streams dataset.

The geoglows-v2-retrospective bucket contains: (1) the model retrospective outputs in (1a) zarr format optimized for time series queries of up to a few hundred rivers on demand as well as (1b) in netCDF format best for bulk downloading the dataset, (2) estimated return period flows for all 7 million million rivers (2a) in zarr format optimized for reading subsets of the dataset as well as (2b) in netCDF format best for bulk downloading. (3) The initialization files produced at the end of each incremental simulation useful for restarting the model from a specific date.

GPM IMERG Early Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDE) at GES DISC

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.The Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques. IMERG can be used for global-scale applications as well as over regions with sparse or no reliable surface observations. The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of the application for increased skill. IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency). While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones. As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.This dataset is the GPM Level 3 IMERG Early Daily 10 x 10 km (GPM_3IMERGDE) derived from the half-hourly GPM_3IMERGHHE. The derived result represents an early (expedited) estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24. This minimizes the possible dry bias in versions before "07", in the simple daily totals for cells where less than 48 half-hourly observations are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared (and rain gauge in the final) dataset, variable "precipitation", and appears in higher latitudes. Thus, in most cases users of global "precipitation" data will not notice any difference. This correction, however, is noticeable in the high-quality microwave retrieval, variable "MWprecipitation", where the occurrence of less than 48 valid half-hourly samples per day is very common. The counts of the valid half-hourly samples per day have always been provided as a separate variable, and users of daily data were advised to pay close attention to that variable and use it to calculate the correct precipitation daily rates. Starting with version "07", this is done in production to minimize possible misinterpretations of the data. The counts are still provided in the data, but they are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. The latency of the derived Early daily product is a couple of minutes after the last granule of GPM_3IMERGHHE for the UTC data day is received at GES DISC. Since the target latency of GPM_3IMERGHHE is 4 hours, the daily should appear about 4 hours after the closure of the UTC day. For information on the original data (GPM_3IMERGHHE), please see the Documentation (Related URL). The daily mean rate (mm/day) is derived by first computing the mean precipitation rate (mm/hour) in a grid cell for the data day, and then multiplying the result by 24. Thus, for every grid cell we have Pdaily_mean = SUM{Pi * 1[Pi valid]} / Pdaily_cnt * 24, i=[1,Nf]Where: Pdaily_cnt = SUM{1[Pi valid]}Pi - half-hourly input, in (mm/hr) Nf - Number of half-hourly files per day, Nf=48 1[.] - Indicator function; 1 when Pi is valid, 0 otherwise Pdaily_cnt - Number of valid retrievals in a grid cell per day.Grid cells for which Pdaily_cnt=0, are set to fill value in the Daily files. Note that Pi=0 is a valid value.Pdaily_cnt are provided in the data files as variables "precipitation_cnt" and "MWprecipitation_cnt", for correspondingly the microwave-IR-gauge and microwave-only retrievals. They are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. There are various ways the daily error could be estimated from the source half-hourly random error (variable "randomError"). The daily error provided in the data files is calculated in a fashion similar to the daily mean precipitation rate. First, the mean of the squared half-hourly "randomError" for the day is computed, and the resulting (mm^2/hr) is converted to (mm^2/day). Finally, square root is taken to get the result in (mm/day):Perr_daily = { SUM{ (Perr_i)^2 * 1[Perr_i valid] ) } / Ncnt_err * 24}^0.5, i=[1,Nf] Ncnt_err = SUM( 1[Perr_i valid] )where: Perr_i - half-hourly input, "randomError", (mm/hr) Perr_daily - Magnitude of the daily error, (mm/day) Ncnt_err - Number of valid half-hour error estimatesAgain, the sum of squared "randomError" can be reconstructed, and other estimates can be derived using the available counts in the Daily files.Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDF) at GES DISC

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.The Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques. IMERG can be used for global-scale applications as well as over regions with sparse or no reliable surface observations. The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of the application for increased skill. IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency). While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones. As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.This dataset is the GPM Level 3 IMERG Final Daily 10 x 10 km (GPM_3IMERGDF) derived from the half-hourly GPM_3IMERGHH. The derived result represents the Final estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24. This minimizes the possible dry bias in versions before "07", in the simple daily totals for cells where less than 48 half-hourly observations are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared and rain gauge dataset, variable "precipitation", and appears in higher latitudes. Thus, in most cases users of global "precipitation" data will not notice any difference. This correction, however, is noticeable in the high-quality microwave retrieval, variable "MWprecipitation", where the occurrence of less than 48 valid half-hourly samples per day is very common. The counts of the valid half-hourly samples per day have always been provided as a separate variable, and users of daily data were advised to pay close attention to that variable and use it to calculate the correct precipitation daily rates. Starting with version "07", this is done in production to minimize possible misinterpretations of the data. The counts are still provided in the data, but they are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. The latency of the derived Final Daily product depends on the delivery of the IMERG Final Half-Hourly product GPM_IMERGHH. Since the latter are delivered in a batch, once per month for the entire month, with up to 4 months latency, so will be the latency for the Final Daily, plus about 24 hours. Thus, e.g. the Dailies for January can be expected to appear no earlier than April 2. The daily mean rate (mm/day) is derived by first computing the mean precipitation rate (mm/hour) in a grid cell for the data day, and then multiplying the result by 24. Thus, for every grid cell we have Pdaily_mean = SUM{Pi * 1[Pi valid]} / Pdaily_cnt * 24, i=[1,Nf]Where: Pdaily_cnt = SUM{1[Pi valid]}Pi - half-hourly input, in (mm/hr) Nf - Number of half-hourly files per day, Nf=48 1[.] - Indicator function; 1 when Pi is valid, 0 otherwise Pdaily_cnt - Number of valid retrievals in a grid cell per day.Grid cells for which Pdaily_cnt=0, are set to fill value in the Daily files. Note that Pi=0 is a valid value.Pdaily_cnt are provided in the data files as variables "precipitation_cnt" and "MWprecipitation_cnt", for correspondingly the microwave-IR-gauge and microwave-only retrievals. They are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. There are various ways the daily error could be estimated from the source half-hourly random error (variable "randomError"). The daily error provided in the data files is calculated in a fashion similar to the daily mean precipitation rate. First, the mean of the squared half-hourly "randomError" for the day is computed, and the resulting (mm^2/hr) is converted to (mm^2/day). Finally, square root is taken to get the result in (mm/day):Perr_daily = { SUM{ (Perr_i)^2 * 1[Perr_i valid] ) } / Ncnt_err * 24}^0.5, i=[1,Nf] Ncnt_err = SUM( 1[Perr_i valid] )where: Perr_i - half-hourly input, "randomError", (mm/hr) Perr_daily - Magnitude of the daily error, (mm/day) Ncnt_err - Number of valid half-hour error estimatesAgain, the sum of squared "randomError" can be reconstructed, and other estimates can be derived using the available counts in the Daily files.Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

GPM IMERG Late Precipitation L3 1 day 0.1 degree x 0.1 degree V07 (GPM_3IMERGDL) at GES DISC

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07.The Integrated Multi-satellitE Retrievals for GPM (IMERG) IMERG is a NASA product estimating global surface precipitation rates at a high resolution of 0.1° every half-hour beginning 2000. It is part of the joint NASA-JAXA Global Precipitation Measurement (GPM) mission, using the GPM Core Observatory satellite as the standard to combine precipitation observations from an international constellation of satellites using advanced techniques. IMERG can be used for global-scale applications as well as over regions with sparse or no reliable surface observations. The fine spatial and temporal resolution of IMERG data allows them to be accumulated to the scale of the application for increased skill. IMERG has three Runs with varying latencies in response to a range of application needs: rapid-response applications (Early Run, 4-h latency), same/next-day applications (Late Run, 14-h latency), and post-real-time research (Final Run, 3.5-month latency). While IMERG strives for consistency and accuracy, satellite estimates of precipitation are expected to have lower skill over frozen surfaces, complex terrain, and coastal zones. As well, the changing GPM satellite constellation over time may introduce artifacts that affect studies focusing on multi-year changes.This dataset is the GPM Level 3 IMERG Late Daily 10 x 10 km (GPM_3IMERGDL) derived from the half-hourly GPM_3IMERGHHL. The derived result represents a Late expedited estimate of the daily mean precipitation rate in mm/day. The dataset is produced by first computing the mean precipitation rate in (mm/hour) in every grid cell, and then multiplying the result by 24. This minimizes the possible dry bias in versions before "07", in the simple daily totals for cells where less than 48 half-hourly observations are valid for the day. The latter under-sampling is very rare in the combined microwave-infrared (and rain gauge in the final) dataset, variable "precipitation", and appears in higher latitudes. Thus, in most cases users of global "precipitation" data will not notice any difference. This correction, however, is noticeable in the high-quality microwave retrieval, variable "MWprecipitation", where the occurrence of less than 48 valid half-hourly samples per day is very common. The counts of the valid half-hourly samples per day have always been provided as a separate variable, and users of daily data were advised to pay close attention to that variable and use it to calculate the correct precipitation daily rates. Starting with version "07", this is done in production to minimize possible misinterpretations of the data. The counts are still provided in the data, but they are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. The latency of the derived Late daily product is a couple of minutes after the last granule of GPM_3IMERGHHL for the UTC data day is received at GES DISC. Since the target latency of GPM_3IMERGHHL is 14 hours, the daily should appear no earlier than 14 hours after the closure of the UTC day. For information on the original data (GPM_3IMERGHHL), please see the Documentation (Related URL). The daily mean rate (mm/day) is derived by first computing the mean precipitation rate (mm/hour) in a grid cell for the data day, and then multiplying the result by 24. Thus, for every grid cell we have Pdaily_mean = SUM{Pi * 1[Pi valid]} / Pdaily_cnt * 24, i=[1,Nf]Where: Pdaily_cnt = SUM{1[Pi valid]}Pi - half-hourly input, in (mm/hr) Nf - Number of half-hourly files per day, Nf=48 1[.] - Indicator function; 1 when Pi is valid, 0 otherwise Pdaily_cnt - Number of valid retrievals in a grid cell per day.Grid cells for which Pdaily_cnt=0, are set to fill value in the Daily files. Note that Pi=0 is a valid value.Pdaily_cnt are provided in the data files as variables "precipitation_cnt" and "MWprecipitation_cnt", for correspondingly the microwave-IR-gauge and microwave-only retrievals. They are only given to gauge the significance of the daily rates, and reconstruct the simple totals if someone wishes to do so. There are various ways the daily error could be estimated from the source half-hourly random error (variable "randomError"). The daily error provided in the data files is calculated in a fashion similar to the daily mean precipitation rate. First, the mean of the squared half-hourly "randomError" for the day is computed, and the resulting (mm^2/hr) is converted to (mm^2/day). Finally, square root is taken to get the result in (mm/day):Perr_daily = { SUM{ (Perr_i)^2 * 1[Perr_i valid] ) } / Ncnt_err * 24}^0.5, i=[1,Nf] Ncnt_err = SUM( 1[Perr_i valid] )where: Perr_i - half-hourly input, "randomError", (mm/hr) Perr_daily - Magnitude of the daily error, (mm/day) Ncnt_err - Number of valid half-hour error estimatesAgain, the sum of squared "randomError" can be reconstructed, and other estimates can be derived using the available counts in the Daily files.Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

Genome in a Bottle on AWS

Several reference genomes to enable translation of whole human genome sequencing to clinical practice. On 11/12/2020 these data were updated to reflect the most up to date GIAB release.

High Resolution Canopy Height Maps by WRI and Meta

Global and regional Canopy Height Maps (CHM). Created using machine learning models on high-resolution worldwide Maxar satellite imagery.

IDEAM - Colombian Radar Network

Historical and one-day delay data from the IDEAM radar network.

International Skin Imaging Collaboration (ISIC) Archive

A public-access archive of skin lesion images, supporting teaching, research, and the development and evaluation of diagnostic algorithms.

JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Digital Terrain Models

The Japan Aerospace EXploration Agency (JAXA) SELenological and ENgineering Explorer (SELENE) mission’s Kaguya spacecraft was launched on September 14, 2007 and science operations around the Moon started October 20, 2007. The primary mission in a circular polar orbit 100-km above the surface lasted from October 20, 2007 until October 31, 2008. An extended mission was then conducted in lower orbits (averaging 50km above the surface) from November 1, 2008 until the SELENE mission ended with Kaguya impacting the Moon on June 10, 2009. These data are digital terrain models derived using the NASA Ames Stereo Pipeline (ASP) and the Kaguya stereoscopic data. Digital terrain models (DTMs) in this data set were bundle adjusted and aligned to Lunar Orbiter Laser Altimeter (LOLA) shot data. The sensor model intrinsics used for these data have been re-estimated to reduce inter-DTM horizontal and vertical errors. Data are controlled to LOLA using the ASP pc_align program. Data co-register at orthoimage resolution (11-37 meters per pixel). At image resolution horizontal offsets are measurable. Horizontal precision is measures to be better than 30 meters per pixel on average. Vertical errors are mean centered to zero. An assessment of overlapping DTMs showed vertical precision on the order of 4 meters. Spacecraft jitter and unmodelled lense distortion at the observation edges is believed to be the major contributor to the vertical errors. To create these analysis ready data, we have taken the ASP genreated data, map projected the data to a stereopair centered orthographic projection and converted to a Cloud Optimized GeoTiff (COG) for online streaming. These data use a priori spacecraft ephemerides (for the nominal mission) and improved, but not controlled spacecraft ephemerides for the extended mission. These data co-register with other LOLA controlled data to the aforementioned horizontal and vertical accuracies.

JAXA / USGS / NASA Kaguya/SELENE Terrain Camera Observations

Met Office Blended Probabilistic Forecast – Global gridded percentiles

This product provides percentile weather forecasts. The grid resolution is approximately 20km and covers the whole globe. It is produced by the Met Office IMPROVER Blended Probabilistic Forecast system. It is available in NetCDF format.

Blended Probabilistic Forecast data is derived from the Met Office's operational NWP (Numerical Weather Prediction) ensembles and nowcasts. To give more reliable predictions, these are then blended and calibrated using the IMPROVER pipeline, and verified using spread–skill and reliability checks.

This is 1 of 8 Blended Probabilistic Forecast products published by the Met Office on the Registry of Open Data on AWS. Data is available for the Global and UK domains, as gridded and spot (site-specific), and represented as percentiles and probabilities.

This info is correct as of April 2026, but some things (like the number of sites, parameters and timesteps) may change in future.

How percentiles work
Ensemble forecasts show a range of possible weather outcomes. However, some users may find it more useful to see ensemble forecasts presented as percentiles, particularly when they want to see where each member of an ensemble sits within the full range of possible outcomes.

Percentiles are generated from an ensemble forecast by first sorting all the members of that ensemble, for example from the coldest temperature to the warmest. To then identify a particular percentile forecast (e.g. the 10th percentile), we find the temperature at which 10% of the ensemble members predict colder conditions. As only 10% of the ensemble members are predicting a lower temperature than the 10th percentile forecast, this indicates that it is giving a relatively low forecast of temperature. Similarly, 90% of ensemble members would predict a lower temperature than the 90th percentile forecast, indicating that it is giving a relatively high forecast of temperature.

Precipitation percentiles should be used cautiously
Precipitation percentiles are potentially useful for experts. But we don't recommend that most people use them, as they are hard to interpret.

Precipitation is spatially noisy (i.e. it can vary a lot over small distances), especially when it is showery. This means that the individual ensemble forecasts that the percentiles are generated from are likely to have their heaviest precipitation positioned in different places. As a result, a high percentile (e.g. 95th) will pick up the high values from all those different locations and make it appear that heavy rain could occur over a wider area than is physically plausible. In other words, the spatial extent of the precipitation when using a high percentile is not physically realistic. High percentiles can be very useful for finding out what the values at the higher end may be, but not how they are spatially organised.

Likewise, the spatial extent of the precipitation will be greatly reduced for the lower percentiles. If there are differences between the ensemble forecasts about where it will rain or not, it is possible that a low percentile precipitation field may show zero precipitation everywhere. Again, that is potentially misleading because it suggests it might be dry everywhere, which is not what the individual forecasts are necessarily saying. It is better to view different percentile maps together (5th, 50th, 95th) to get a more informative impression.

Even the 50th percentile can be misleading as, by definition, it will never include the highest predicted values. Nor is there any guarantee that the peaks in the 50th percentile grid will align with the peaks in the 95th percentile grid.

About the grid
The grid resolution for Best Probabilistic Forecast Global gridded probabilities is approximately 20km and covers the whole globe.

Numerical weather prediction (NWP) models generate forecasts for each grid point within a geographical area of interest. Each of these gridded forecasts corresponds to a particular diagnostic (e.g. precipitation rate) at a particular time. IMPROVER then takes an ensemble of these gridded forecasts and applies post-processing techniques to enhance them and represent them probabilistically. The resulting grid of values represents probabilities of exceeding or falling below a particular threshold.

Aspect	Values
Projection	Equirectangular Latitude-Longitude
Standard parallel	48.16° N
Reference datum	earth_radius = 6371229.0 m
Nominal resolution	20 km or N640
North-South spacing	0.1875° (20.85 km)
East-West spacing	0.28125° (~20.86 km - UK, 31.27 km - equator)
North-West corner	89.90625°N, 179.859375°W
South-West corner	89.90625°S, 179.859375°W
South-East corner	89.90625°S, 179.859375°E
North-East corner	89.90625°N, 179.859375°E
East-West points	1280
North-South points	960
Grid type	Arakawa A

Parameters and timesteps
There are 38 weather parameters available including:

Cloud
Temperature
Pressure
Humidity
Visibility
Precipitation rate and accumulations (see note above about care required with use)
UV
Wind

For most parameters, the following timesteps are available:

Every hour from 0 to 120 hours
Every 3 hours from 123 to 192 hours

However, timesteps vary significantly for some parameters. Check the parameter documentation for more details.

Latency
Data is made available shortly after the blend time.

Archive length
Data is available for the past 30 days.

Business needs
This product supports risk-based decision-making by providing uncertainty ranges rather than single deterministic values. Typical uses include:

assessing uncertainty for operational planning
evaluating weather-related risk thresholds
deriving deterministic products (e.g. 50th percentile) from probabilistic outputs

Gridded forecasts show how a diagnostic varies spatially across a domain at a given time. By using a time series of gridded fields, you can determine how a weather diagnostic is expected to evolve across a geographic area.

If you need a forecast for a specific location, a spot forecast may suit your needs better than gridded data. Spot Blended Probabilistic Forecasts are also available as percentiles and probabilities for both the UK and Global domains.

Met Office Blended Probabilistic Forecast – Global gridded probabilities

This product provides gridded probabilistic weather forecasts. The grid resolution is approximately 20km and covers the whole globe. It is produced by the Met Office IMPROVER Blended Probabilistic Forecast system. It is available in NetCDF format.

Blended Probabilistic Forecast data is derived from the Met Office's operational NWP (Numerical Weather Prediction) ensembles and nowcasts. To give more reliable predictions, these are then blended and calibrated using the IMPROVER pipeline, and verified using spread–skill and reliability checks.

This is 1 of 8 Blended Probabilistic Forecast products published by the Met Office on the Registry of Open Data on AWS. Data is available for the Global and UK domains, as gridded and spot (site-specific), and represented as percentiles and probabilities.

This info is correct as of April 2026, but some things (like the number of sites, parameters and timesteps) may change in future.

How probabilities work
Ensemble forecasts show a range of possible weather outcomes. However, some users may find it more useful to see ensemble forecasts presented as probabilities, particularly when they're interested in specific thresholds.

Probabilities are generated from an ensemble forecast by counting how many members of that ensemble exceed a particular threshold value. For example, if the threshold for screen temperature is 5°C, and 9 out of 18 ensemble members show a screen temperature above 5°C, there is a 50% chance of temperatures exceeding 5°C.

About the grid
The grid resolution for Blended Probabilistic Forecast Global gridded probabilities is approximately 20km and covers the whole globe.

Numerical weather prediction (NWP) models generate forecasts for each grid point within a geographical area of interest. Each of these gridded forecasts corresponds to a particular diagnostic (e.g. precipitation rate) at a particular time. IMPROVER then takes an ensemble of these gridded forecasts and applies post-processing techniques to enhance them and represent them probabilistically. The resulting grid of values represents probabilities of exceeding or falling below a particular threshold.

Aspect	Values
Projection	Equirectangular Latitude-Longitude
Standard parallel	48.16° N
Reference datum	earth_radius = 6371229.0 m
Nominal resolution	20 km or N640
North-South spacing	0.1875° (20.85 km)
East-West spacing	0.28125° (~20.86 km - UK, 31.27 km - equator)
North-West corner	89.90625°N, 179.859375°W
South-West corner	89.90625°S, 179.859375°W
South-East corner	89.90625°S, 179.859375°E
North-East corner	89.90625°N, 179.859375°E
East-West points	1280
North-South points	960
Grid type	Arakawa A

Parameters and timesteps
There are 31 weather parameters available including:

Cloud
Lightning
Humidity
Visibility
Precipitation rate and accumulations
UV
Wind

For most parameters, the following timesteps are available:
Every hour from 0 to 120 hours
Every 3 hours from 123 to 192 hours

However, timesteps vary significantly for some parameters. Check the parameter documentation for more details.

Latency
Data is made available shortly after the blend time.

Archive length
Data is available for the past 30 days.

Business needs
This product supports risk-based decision-making by providing uncertainty ranges rather than single deterministic values. Typical uses include:

assessing uncertainty for operational planning
evaluating weather-related risk thresholds
deriving deterministic products (e.g. 50th percentile) from probabilistic outputs

Met Office Blended Probabilistic Forecast – Global spot percentiles

This product provides percentile weather forecasts for 5,956 sites (or spots) across the globe. It is produced by the Met Office IMPROVER Blended Probabilistic Forecast system. It is available in NetCDF format.

Blended Probabilistic Forecast data is derived from the Met Office's operational NWP (Numerical Weather Prediction) ensembles and nowcasts. To give more reliable predictions, these are then blended and calibrated using the IMPROVER pipeline, and verified using spread–skill and reliability checks.

This is 1 of 8 Blended Probabilistic Forecast products published by the Met Office on the Registry of Open Data on AWS. Data is available for the Global and UK domains, as gridded and spot (site-specific), and represented as percentiles and probabilities.

This info is correct as of April 2026, but some things (like the number of sites, parameters and timesteps) may change in future.

How percentiles work
Ensemble forecasts show a range of possible weather outcomes. However, some users may find it more useful to see ensemble forecasts presented as percentiles, particularly when they want to see where each member of an ensemble sits within the full range of possible outcomes.

Percentiles are generated from an ensemble forecast by first sorting all the members of that ensemble, for example from the coldest temperature to the warmest. To then identify a particular percentile forecast (e.g. the 10th percentile), we find the temperature at which 10% of the ensemble members predict colder conditions. As only 10% of the ensemble members are predicting a lower temperature than the 10th percentile forecast, this indicates that it is giving a relatively low forecast of temperature. Similarly, 90% of ensemble members would predict a lower temperature than the 90th percentile forecast, indicating that it is giving a relatively high forecast of temperature.

Precipitation percentiles should be used cautiously
Precipitation percentiles are potentially useful for experts. But we don't recommend that most people use them, as they are hard to interpret.

Precipitation is spatially noisy (i.e. it can vary a lot over small distances), especially when it is showery. This means that the individual ensemble forecasts that the percentiles are generated from are likely to have their heaviest precipitation positioned in different places. As a result, a high percentile (e.g. 95th) will pick up the high values from all those different locations and make it appear that heavy rain could occur over a wider area than is physically plausible. In other words, the spatial extent of the precipitation when using a high percentile is not physically realistic. High percentiles can be very useful for finding out what the values at the higher end may be, but not how they are spatially organised.

Likewise, the spatial extent of the precipitation will be greatly reduced for the lower percentiles. If there are differences between the ensemble forecasts about where it will rain or not, it is possible that a low percentile precipitation field may show zero precipitation everywhere. Again, that is potentially misleading because it suggests it might be dry everywhere, which is not what the individual forecasts are necessarily saying. It is better to view different percentile maps together (5th, 50th, 95th) to get a more informative impression.

Even the 50th percentile can be misleading as, by definition, it will never include the highest predicted values. Nor is there any guarantee that the peaks in the 50th percentile grid will align with the peaks in the 95th percentile grid.

How spot data works
Spot data is derived by extracting site‑specific forecasts from post‑processed gridded forecasts.

IMPROVER operates on two domains:

the UK domain, which primarily covers the region around the United Kingdom, Ireland and parts of Western Europe
the Global domain, which covers the whole world

Within each domain, IMPROVER post-processes most forecasts on a grid, though the resolution of this grid differs between the two domains. Site-specific forecasts are drawn from these grids at the end of the post-processing chains. Sites within the UK domain draw forecasts from the high-resolution UK domain grid, as this should provide the best possible forecast, whereas sites outside of this domain draw from the lower resolution Global grid. The two domains are represented in two separate datasets.

Global domain sites
There are 5,956 sites in the Global domain. (The Global domain excludes the British Isles and parts of Western Europe. For sites in the UK, Ireland and the parts of Western Europe not included in the Global domain, please see the UK Spot Percentiles dataset.)

Site-specific forecasts in the Global domain are calculated from the MOGREPS-G model alone. However this model is time-lagged to provide additional forecast spread and reduce inter-cycle variation.

Parameters and timesteps
There are 68 weather parameters available including:

Cloud
Temperature
Pressure
Humidity
Visibility
Precipitation rate and accumulations (see note above about care required with use)
UV
Wind

For most parameters, the following timesteps are available:

Every hour from 0 to 120 hours
Every 3 hours from 123 to 192 hours

However, timesteps vary significantly for some parameters. Check the parameter documentation for more details.

This dataset also contains deterministic “weather symbol” parameters. These are designed to complement the percentiles information in cases where the user wants to extract a single “deterministic” forecast. The 50th percentile works well for some parameters, but others are better represented by the weather symbol. Check the parameter documentation for guidance on where this applies.

Latency
Data is made available shortly after the blend time.

Archive length
Data is available for the past 30 days.

Business needs
This product supports risk-based decision-making by providing uncertainty ranges rather than single deterministic values. Typical uses include:

assessing uncertainty for operational planning
evaluating weather-related risk thresholds
deriving deterministic products (e.g. 50th percentile) from probabilistic outputs

Spot forecasts provide information about weather diagnostics at single sites. By using a time series of these forecasts, you can determine how a weather diagnostic is expected to evolve at a particular location. This is ideal for users if you are not moving spatially and are instead interested in how conditions are evolving at your location. This is the kind of time-series information you see in the Met Office app when you look at a forecast for your home address.

Gridded forecasts show how a diagnostic varies spatially across a domain at a given time. By using a time series of gridded fields, you can determine how a weather diagnostic is expected to evolve across a geographic area. So you may find them more useful if you need to consider moving spatially. This kind of product is very familiar from television broadcast weather forecasts. Gridded Blended Probabilistic Forecasts are also available as percentiles and probabilities for both the UK and Global domains.

Met Office Blended Probabilistic Forecast – Global spot probabilities

This product provides probabilistic weather forecasts for 5,956 sites (or spots) across the globe. It is produced by the Met Office IMPROVER Blended Probabilistic Forecast system. It is available in NetCDF format.

Blended Probabilistic Forecast data is derived from the Met Office's operational NWP (Numerical Weather Prediction) ensembles and nowcasts. To give more reliable predictions, these are then blended and calibrated using the IMPROVER pipeline, and verified using spread–skill and reliability checks.

This is 1 of 8 Blended Probabilistic Forecast products published by the Met Office on the Registry of Open Data on AWS. Data is available for the Global and UK domains, as gridded and spot (site-specific), and represented as percentiles and probabilities.

This info is correct as of April 2026, but some things (like the number of sites, parameters and timesteps) may change in future.

How probabilities work
Ensemble forecasts show a range of possible weather outcomes. However, some users may find it more useful to see ensemble forecasts presented as probabilities, particularly when they're interested in specific thresholds.

Probabilities are generated from an ensemble forecast by counting how many members of that ensemble exceed a particular threshold value. For example, if the threshold for screen temperature is 5°C, and 9 out of 18 ensemble members show a screen temperature above 5°C, there is a 50% chance of temperatures exceeding 5°C.

How spot data works
Spot data is derived by extracting site‑specific forecasts from post‑processed gridded forecasts.

IMPROVER operates on two domains:

the UK domain, which primarily covers the region around the United Kingdom, Ireland and parts of Western Europe
the Global domain, which covers the whole world

Cloud
Lightning
Pressure
Humidity
Visibility
Precipitation rate and accumulations
UV
Wind

For most parameters, the following timesteps are available:

Every hour from 0 to 120 hours
Every 3 hours from 123 to 192 hours

However, timesteps vary significantly for some parameters. Check the parameter documentation for more details.

Latency
Data is made available shortly after the model blend time.

Archive length
Data is available for the past 30 days.

Business needs
This product supports risk-based decision-making by providing uncertainty ranges rather than single deterministic values. Typical uses include:

assessing uncertainty for operational planning
evaluating weather-related risk thresholds
deriving deterministic products (e.g. 50th percentile) from probabilistic outputs

Met Office Blended Probabilistic Forecast – UK Spot Percentiles

This product provides percentile weather forecasts for 7,213 sites (or spots) across the United Kingdom, Ireland and parts of Western Europe. It is produced by the Met Office IMPROVER Blended Probabilistic Forecast system. It is available in NetCDF format.

Blended Probabilistic Forecast data is derived from the Met Office's operational NWP (Numerical Weather Prediction) ensembles and nowcasts. To give more reliable predictions, these are then blended and calibrated using the IMPROVER pipeline, and verified using spread–skill and reliability checks.

This is 1 of 8 Blended Probabilistic Forecast products published by the Met Office on the Registry of Open Data on AWS. Data is available for the Global and UK domains, as gridded and spot (site-specific), and represented as percentiles and probabilities.

This info is correct as of April 2026, but some things (like the number of sites, parameters and timesteps) may change in future.

How percentiles work
Ensemble forecasts show a range of possible weather outcomes. However, some users may find it more useful to see ensemble forecasts presented as percentiles, particularly when they want to see where each member of an ensemble sits within the full range of possible outcomes.

Percentiles are generated from an ensemble forecast by first sorting all the members of that ensemble, for example from the coldest temperature to the warmest. To then identify a particular percentile forecast (e.g. the 10th percentile), we find the temperature at which 10% of the ensemble members predict colder conditions. As only 10% of the ensemble members are predicting a lower temperature than the 10th percentile forecast, this indicates that it is giving a relatively low forecast of temperature. Similarly, 90% of ensemble members would predict a lower temperature than the 90th percentile forecast, indicating that it is giving a relatively high forecast of temperature.

Precipitation percentiles should be used cautiously
Precipitation percentiles are potentially useful for experts. But we don't recommend that most people use them, as they are hard to interpret.

Precipitation is spatially noisy (i.e. it can vary a lot over small distances), especially when it is showery. This means that the individual ensemble forecasts that the percentiles are generated from are likely to have their heaviest precipitation positioned in different places. As a result, a high percentile (e.g. 95th) will pick up the high values from all those different locations and make it appear that heavy rain could occur over a wider area than is physically plausible. In other words, the spatial extent of the precipitation when using a high percentile is not physically realistic. High percentiles can be very useful for finding out what the values at the higher end may be, but not how they are spatially organised.

Likewise, the spatial extent of the precipitation will be greatly reduced for the lower percentiles. If there are differences between the ensemble forecasts about where it will rain or not, it is possible that a low percentile precipitation field may show zero precipitation everywhere. Again, that is potentially misleading because it suggests it might be dry everywhere, which is not what the individual forecasts are necessarily saying. It is better to view different percentile maps together (5th, 50th, 95th) to get a more informative impression.

Even the 50th percentile can be misleading as, by definition, it will never include the highest predicted values. Nor is there any guarantee that the peaks in the 50th percentile grid will align with the peaks in the 95th percentile grid.

How spot data works
Spot data is derived by extracting site‑specific forecasts from post‑processed gridded forecasts.

IMPROVER operates on two domains:

the UK domain, which primarily covers the region around the United Kingdom, Ireland and parts of Western Europe
the Global domain, which covers the whole world

Within each domain, IMPROVER post-processes most forecasts on a grid, though the resolution of this grid differs between the two domains. Site-specific forecasts are drawn from these grids at the end of the post-processing chains.

UK domain sites
There are 7,213 forecast sites in the UK domain. IMPROVER calculates forecasts for these sites from UK domain gridded forecasts. These gridded forecasts are generated from a lead-time-dependent blend of up to 4 forecasting models: an extrapolation nowcast (MONOW), UKV, MOGREPS-UK, and MOGREPS-G. The blend can also involve multiple cycles of a model at different lead times.

Parameters and timesteps
There are 61 weather parameters available including:

Cloud
Temperature
Pressure
Humidity
Visibility
Precipitation rate and accumulations
UV
Wind

For most parameters, the following timesteps are available:

Every hour from 0 to 120 hours
Every 3 hours from 123 to 186 hours

However, timesteps vary significantly for some parameters. Check the parameter documentation for more details.

Latency
Data is made available shortly after the model blend time.

Archive length
Data is available for the past 30 days.

Business needs

This product supports risk-based decision-making by providing uncertainty ranges rather than single deterministic values. Typical uses include:

assessing uncertainty for operational planning
evaluating weather-related risk thresholds
deriving deterministic products (e.g. 50th percentile) from probabilistic outputs

Met Office Blended Probabilistic Forecast – UK Spot Probabilities

This product provides probabilistic weather forecasts for 7,213 sites (or spots) across the United Kingdom, Ireland and parts of Western Europe. It is produced by the Met Office IMPROVER Blended Probabilistic Forecast system. It is available in NetCDF format.

Blended Probabilistic Forecast data is derived from the Met Office's operational NWP (Numerical Weather Prediction) ensembles and nowcasts. To give more reliable predictions, these are then blended and calibrated using the IMPROVER pipeline, and verified using spread–skill and reliability checks.

This is 1 of 8 Blended Probabilistic Forecast products published by the Met Office on the Registry of Open Data on AWS. Data is available for the Global and UK domains, as gridded and spot (site-specific), and represented as percentiles and probabilities.

This info is correct as of April 2026, but some things (like the number of sites, parameters and timesteps) may change in future.

How probabilities work
Ensemble forecasts show a range of possible weather outcomes. However, some users may find it more useful to see ensemble forecasts presented as probabilities, particularly when they're interested in specific thresholds.

Probabilities are generated from an ensemble forecast by counting how many members of that ensemble exceed a particular threshold value. For example, if the threshold for screen temperature is 5°C, and 9 out of 18 ensemble members show a screen temperature above 5°C, there is a 50% chance of temperatures exceeding 5°C.

How spot data works
Spot data is derived by extracting site‑specific forecasts from post‑processed gridded forecasts.

IMPROVER operates on two domains:

the UK domain, which primarily covers the region around the United Kingdom, Ireland and parts of Western Europe
the Global domain, which covers the whole world

Within each domain, IMPROVER post-processes most forecasts on a grid, though the resolution of this grid differs between the two domains. Site-specific forecasts are drawn from these grids at the end of the post-processing chains.

UK domain sites
There are 7,213 forecast sites in the UK domain. IMPROVER calculates forecasts for these sites from UK domain gridded forecasts. These gridded forecasts are generated from a lead-time-dependent blend of up to 4 forecasting models: an extrapolation nowcast (MONOW), UKV, MOGREPS-UK, and MOGREPS-G. The blend can also involve multiple cycles of a model at different lead times.

Parameters and timesteps
There are 61 weather parameters available including:

Cloud
Temperature
Pressure
Humidity
Visibility
Precipitation rate and accumulations
UV
Wind

For most parameters, the following timesteps are available:

Every hour from 0 to 120 hours
Every 3 hours from 123 to 186 hours

However, timesteps vary significantly for some parameters. Check the parameter documentation for more details.

This dataset also contains deterministic “weather symbol” parameters. These are designed to complement the percentiles information in cases where the user wants to extract a single “deterministic” forecast. The 50th percentile works well for some parameters, but others are better represented by the weather symbol. Check the parameter documentation for guidance on where this applies.

Latency
Data is made available shortly after the model blend time.

Archive length
Data is available for the past 30 days.

Business needs

This product supports risk-based decision-making by providing uncertainty ranges rather than single deterministic values. Typical uses include:

assessing uncertainty for operational planning
evaluating weather-related risk thresholds
deriving deterministic products (e.g. 50th percentile) from probabilistic outputs

Met Office Blended Probabilistic Forecast – UK gridded percentiles

This product provides gridded percentile weather forecasts. The grid resolution is approximately 2km and covers the UK and parts of Western Europe. It is produced by the Met Office IMPROVER Blended Probabilistic Forecast system. It is available in NetCDF format.

Blended Probabilistic Forecast data is derived from the Met Office's operational NWP (Numerical Weather Prediction) ensembles and nowcasts. To give more reliable predictions, these are then blended and calibrated using the IMPROVER pipeline, and verified using spread–skill and reliability checks.

This is 1 of 8 Blended Probabilistic Forecast products published by the Met Office on the Registry of Open Data on AWS. Data is available for the Global and UK domains, as gridded and spot (site-specific), and represented as percentiles and probabilities.

This info is correct as of April 2026, but some things (like the number of sites, parameters and timesteps) may change in future.

How percentiles work
Ensemble forecasts show a range of possible weather outcomes. However, some users may find it more useful to see ensemble forecasts presented as percentiles, particularly when they want to see where each member of an ensemble sits within the full range of possible outcomes.

Percentiles are generated from an ensemble forecast by first sorting all the members of that ensemble, for example from the coldest temperature to the warmest. To then identify a particular percentile forecast (e.g. the 10th percentile), we find the temperature at which 10% of the ensemble members predict colder conditions. As only 10% of the ensemble members are predicting a lower temperature than the 10th percentile forecast, this indicates that it is giving a relatively low forecast of temperature. Similarly, 90% of ensemble members would predict a lower temperature than the 90th percentile forecast, indicating that it is giving a relatively high forecast of temperature.

Precipitation percentiles should be used cautiously
Precipitation percentiles are potentially useful for experts. But we don't recommend that most people use them, as they are hard to interpret.

Precipitation is spatially noisy (i.e. it can vary a lot over small distances), especially when it is showery. This means that the individual ensemble forecasts that the percentiles are generated from are likely to have their heaviest precipitation positioned in different places. As a result, a high percentile (e.g. 95th) will pick up the high values from all those different locations and make it appear that heavy rain could occur over a wider area than is physically plausible. In other words, the spatial extent of the precipitation when using a high percentile is not physically realistic. High percentiles can be very useful for finding out what the values at the higher end may be, but not how they are spatially organised.

Likewise, the spatial extent of the precipitation will be greatly reduced for the lower percentiles. If there are differences between the ensemble forecasts about where it will rain or not, it is possible that a low percentile precipitation field may show zero precipitation everywhere. Again, that is potentially misleading because it suggests it might be dry everywhere, which is not what the individual forecasts are necessarily saying. It is better to view different percentile maps together (5th, 50th, 95th) to get a more informative impression.

Even the 50th percentile can be misleading as, by definition, it will never include the highest predicted values. Nor is there any guarantee that the peaks in the 50th percentile grid will align with the peaks in the 95th percentile grid.

About the grid
The grid resolution is approximately 2km and covers the UK and parts of Western Europe.

Numerical weather prediction (NWP) models generate forecasts for each grid point within a geographical area of interest. Each of these gridded forecasts corresponds to a particular diagnostic (e.g. precipitation rate) at a particular time. IMPROVER then takes an ensemble of these gridded forecasts and applies post-processing techniques to enhance them and represent them probabilistically. The resulting grid of values represents probabilities of exceeding or falling below a particular threshold.

Aspect	Values
Projection	Lambert Azimuthal Equal Area
Longitude of Prime Meridian	0.0° W
Reference datum	ETRS89 - uses the GRS80 as a reference ellipsoid.
Nominal resolution	2 km
Extreme West	-1158 km
Extreme East	924 km
Extreme North	902 km
Extreme South	-1036 km
Northwest Corner (Long, Lat)	-24.5099247, 61.31885886
Northeast Corner (Long, Lat)	15.27976922, 61.9206868
Southwest Corner (Long, Lat)	-17.11712928, 44.51715281
Southeast Corner (Long, Lat)	9.21255933, 44.899873
Semi Major Axis	6378.137 km
Semi MinorAxis	6356.75231414036 km
Longitude of Projection Origin	2.5° W
Latitude of Projection Origin	54.9° N
False Eastings	0.0 km
False Northings	0.0 km
East-West points	1042
North-South points	970
Grid type	Arakawa A

Parameters and timesteps
There are 44 weather parameters available including:

Cloud
Temperature
Pressure
Humidity
Visibility
Precipitation rate and accumulations (see note above about care required with use)
UV
Wind

For most parameters, the following timesteps are available:

Every hour from 0 to 120 hours
Every 3 hours from 123 to 186 hours

assessing uncertainty for operational planning
evaluating weather-related risk thresholds
deriving deterministic products (e.g. 50th percentile) from probabilistic outputs

Met Office Blended Probabilistic Forecast – UK gridded probabilities

This product provides gridded probabilistic weather forecasts. The grid resolution is approximately 2km and covers the UK and parts of Western Europe. It is produced by the Met Office IMPROVER Blended Probabilistic Forecast system. It is available in NetCDF format.

Blended Probabilistic Forecast data is derived from the Met Office's operational NWP (Numerical Weather Prediction) ensembles and nowcasts. To give more reliable predictions, these are then blended and calibrated using the IMPROVER pipeline, and verified using spread–skill and reliability checks.

This is 1 of 8 Blended Probabilistic Forecast products published by the Met Office on the Registry of Open Data on AWS. Data is available for the Global and UK domains, as gridded and spot (site-specific), and represented as percentiles and probabilities.

This info is correct as of April 2026, but some things (like the number of sites, parameters and timesteps) may change in future.

How probabilities work
Ensemble forecasts show a range of possible weather outcomes. However, some users may find it more useful to see ensemble forecasts presented as probabilities, particularly when they're interested in specific thresholds.

Probabilities are generated from an ensemble forecast by counting how many members of that ensemble exceed a particular threshold value. For example, if the threshold for screen temperature is 5°C, and 9 out of 18 ensemble members show a screen temperature above 5°C, there is a 50% chance of temperatures exceeding 5°C.

About the grid
The grid resolution is approximately 2km and covers the UK and parts of Western Europe.

Numerical weather prediction (NWP) models generate forecasts for each grid point within a geographical area of interest. Each of these gridded forecasts corresponds to a particular diagnostic (e.g. precipitation rate) at a particular time. IMPROVER then takes an ensemble of these gridded forecasts and applies post-processing techniques to enhance them and represent them probabilistically. The resulting grid of values represents probabilities of exceeding or falling below a particular threshold.

Aspect	Values
Projection	Lambert Azimuthal Equal Area
Longitude of Prime Meridian	0.0° W
Reference datum	ETRS89 - uses the GRS80 as a reference ellipsoid.
Nominal resolution	2 km
Extreme West	-1158 km
Extreme East	924 km
Extreme North	902 km
Extreme South	-1036 km
Northwest Corner (Long, Lat)	-24.5099247, 61.31885886
Northeast Corner (Long, Lat)	15.27976922, 61.9206868
Southwest Corner (Long, Lat)	-17.11712928, 44.51715281
Southeast Corner (Long, Lat)	9.21255933, 44.899873
Semi Major Axis	6378.137 km
Semi MinorAxis	6356.75231414036 km
Longitude of Projection Origin	2.5° W
Latitude of Projection Origin	54.9° N
False Eastings	0.0 km
False Northings	0.0 km
East-West points	1042
North-South points	970
Grid type	Arakawa A

Parameters and timesteps
There are 44 weather parameters available including:

Cloud
Temperature
Pressure
Humidity
Visibility
Precipitation rate and accumulations
Wind

For most parameters, the following timesteps are available:

Every hour from 0 to 120 hours
Every 3 hours from 123 to 186 hours

assessing uncertainty for operational planning
evaluating weather-related risk thresholds
deriving deterministic products (e.g. 50th percentile) from probabilistic outputs

Met Office Global Ensemble Prediction System (MOGREPS-G) on a 30-day rolling archive

THIS DATASET IS CHANGING

Files uploaded from late January 2026 onward will contain changes including:

precision changes
new parameters
changes to existing parameters e.g. adding vertical levels and timesteps
the height_asl_on_pressure_levels parameter will be replaced by geopotential_height_on_pressure_levels

Please check your systems are prepared for these changes.

A numerical weather prediction model that produces forecasts for the whole globe up to a week ahead. The projection used is the Equirectangular Latitude-Longitude and the grid resolution is 20km. The data is available as NetCDF files. It's offered on a free, unsupported basis, so we don’t recommend using it for any critical business purposes.

Met Office Global Ensemble Prediction System (MOGREPS-G) is a global configuration of the Unified Model, which is the Met Office's flagship Numerical Weather Prediction model.

The archive contains 30 days of data. The data is typically available approximately 10-11 hours after the model run time.

The following timesteps are available:

Every hour from 0 to 54 hours or from 0 to 132 hours (depending on the parameter)
Every 3 hours between 57 to 198 hours or from 135 to 198 hours (depending on the parameter) for data prior to late January 2026. Data produced after this run out to 246 hours.

Met Office Global and Regional Ensemble Prediction System - UK (MOGREPS-UK) on a 30-day rolling archive

THIS DATASET IS CHANGING

Files uploaded from late January 2026 onward will contain changes including:

precision changes
new parameters
changes to existing parameters e.g. adding vertical levels and timesteps
the height_asl_on_pressure_levels parameter will be replaced by geopotential_height_on_pressure_levels

Please check your systems are prepared for these changes.

A numerical weather prediction model that produces forecasts for the UK for the next 5 days. Parameters including temperature, pressure, wind, humidity, etc. are forecast at grid points separated by about 2.2 km, and the model has multiple vertical levels. The data is available as NetCDF files. It's offered on a free, unsupported basis, so we don’t recommend using it for any critical business purposes.

Met Office Global and Regional Ensemble Prediction System - UK (MOGREPS-UK) is a UK configuration of the Unified Model, which is the Met Office's flagship Numerical Weather Prediction model.

The archive contains 30 days of data. The data is typically available approximately 10-11 hours after the model run time.

For most parameters, timesteps are provided every hour from 0 to 126 hours

Molecular Profiling to Predict Response to Treatment (phs001965)

The Molecular Profiling to Predict Response to Treatment (MP2PRT) program is part of the NCI's Cancer Moonshot Initiative. The aim of this program is the retrospective characterization and analysis of biospecimens collected from completed NCI-sponsored trials of the National Clinical Trials Network and the NCI Community Oncology Research Program. This study, titled "Identification of Genetic Changes Associated with Relapse and/or Adaptive Resistance in Patients Registered as Favorable Histology Wilms Tumor on AREN03B2", performs genomic characterization (WGS 30X, Total RNAseq, miRNAseq) on a discovery set of 70 trio cases (normal tissue, tumor tissue at time of diagnosis, tumor tissue at time of relapse) from patients who relapsed with Favorable Histology Wilms Tumor. Prioritized findings from the discovery set will be validated using Targeted Sequencing in an independent validation set of 47 relapse samples. The MP2PRT study is made available on AWS via the NIH STRIDES Initiative (https://aws.amazon.com/blogs/publicsector/aws-and-national-institutes-of-health-collaborate-to-accelerate-discoveries-with-strides-initiative/).

Mouse Brain Anatomy: MouseLight Imagery

This data set, made available by Janelia's MouseLight project, consists of images and neuron annotations of the Mus musculus brain, stored in formats suitable for viewing and annotation using the HortaCloud cloud-based annotation system.

NA-CORDEX - North American component of the Coordinated Regional Downscaling Experiment

The NA-CORDEX dataset contains regional climate change scenario data and guidance for North America, for use in impacts, decision-making, and climate science. The NA-CORDEX data archive contains output from regional climate models (RCMs) run over a domain covering most of North America using boundary conditions from global climate model (GCM) simulations in the CMIP5 archive. These simulations run from 1950–2100 with a spatial resolution of 0.22°/25km or 0.44°/50km. This AWS S3 version of the data includes selected variables converted to Zarr format from the original NetCDF. Only daily data are currently available; all daily data were mapped to the Gregorian calendar. Sub-daily data may be added later. Both raw and bias-corrected data are available. Further details about this version of the dataset are available at the documentation link below.

NAIP on AWS

The National Agriculture Imagery Program (NAIP) acquires aerial imagery during the agricultural growing seasons in the continental U.S. This "leaf-on" imagery andtypically ranges from 30 centimeters to 100 centimeters in resolution and is available from the naip-analytic Amazon S3 bucket as 4-band (RGB + NIR) imagery in MRF format, on naip-source Amazon S3 bucket as 4-band (RGB + NIR) in uncompressed Raw GeoTiff format and naip-visualization as 3-band (RGB) Cloud Optimized GeoTiff format. More details on NAIP

NASA / USGS Controlled Europa DTMs

Knowledge of a planetary surface’s topography is necessary to understand its geology and enable landed mission operations. The Solid State Imager (SSI) on board NASA’s Galileo spacecraft acquired more than 700 images of Jupiter’s moon Europa. Although moderate- and high-resolution coverage is extremely limited, repeat coverage of a small number of sites enables the creation of digital terrain models (DTMs) via stereophotogrammetry. Here we provide stereo-derived DTMs of five sites on Europa. The sites are the bright band Agenor Linea, the crater Cilix, the crater Pwyll, pits and chaos adjacent to Rhadamanthys Linea, and ridged plains near Yelland Linea. We generated the DTMs using BAE’s SOCET SET® software and each was manually edited to correct identifiable errors from the automated stereo matching process. Additionally, we used the recently updated image pointing information provided by the U.S. Geological Survey (Bland, et al., 2021), which ties the DTMs to an existing horizontal datum and enables the DTMs to easily be used in coordination with that globally controlled image set. The DTMs are of the highest quality achievable with Galileo data and are therefore suitable for most scientific analysis. However, there are inherent uncertainties in the DTMs including horizontal resolutions that are typically 1–2 km (~10x the image pixel scale) and expected vertical precision (the root mean square (RMS) uncertainty in a point elevation) of 10s–100s of m. The DTMs and their uncertainties are discussed in detail in the documentation.

NASA / USGS Controlled THEMIS Mosaics

These data are infrared image mosaics, tiled to the Mars quadrangle, generated using Thermal Emission Imaging System (THEMIS) images from the 2001 Mars Odyssey orbiter mission. The mosaic is generated at the full resolution of the THEMIS infrared dataset, which is approximately 100 meters/pixel. The mosaic was absolutely photogrammetrically controlled to an improved Viking MDIM network that was develop by the USGS Astrogeology processing group using the Integrated Software for Imagers and Spectrometers. Image-to-image alignment precision is subpixel (i.e., <100m). These 8-bit, qualitative data are released as losslessly compressed Cloud Optimized GeoTiffs (COGs). Data are released using simple cylindrical (planetocentric positive East, center longitude 0, -180 to 180 longitude domain).

NASA / USGS Europa Controlled Observation Mosaics

The Solid State Imager (SSI) on NASA's Galileo spacecraft acquired more than 500 images of Jupiter's moon, Europa. These images vary from relatively low-resolution hemispherical imaging, to high-resolution targeted images that cover a small portion of the surface. Here we provide a set of 92 image mosaics generated from minimally processed, projected Galileo images with photogrammetrically improved locations on Europa's surface.

These images provide users with nearly the entire Galileo Europa imaging dataset at its native resolution and with improved relative image locations. The Solid State Imager on NASA's Galileo spacecraft provided the only moderate- to high-resolution images of Jupiter's moon, Europa. Unfortunately, uncertainty in the position and pointing of the spacecraft, as well as the position and orientation of Europa, when the images were acquired resulted in significant errors in image locations on the surface. The result of these errors is that images acquired during different Galileo orbits, or even at different times during the same orbit, are significantly misaligned (errors of up to 100 km on the surface).

The dataset provides a set of individual images that can be used for scientific analysis and mission planning activities.

NASA / USGS Europa Controlled Observations

The Solid State Imager (SSI) on NASA's Galileo spacecraft acquired more than 500 images of Jupiter's moon, Europa. These images vary from relatively low-resolution hemispherical imaging, to high-resolution targeted images that cover a small portion of the surface. Here we provide a set of 481 minimally processed, projected Galileo images with photogrammetrically improved locations on Europa's surface. These individual images were subsequently used as input into a set of 92 observation mosaics.

These images provide users with nearly the entire Galileo Europa imaging dataset at its native resolution and with improved relative image locations. The Solid State Imager on NASA's Galileo spacecraft provided the only moderate- to high-resolution images of Jupiter's moon, Europa. Unfortunately, uncertainty in the position and pointing of the spacecraft, as well as the position and orientation of Europa, when the images were acquired resulted in significant errors in image locations on the surface. The result of these errors is that images acquired during different Galileo orbits, or even at different times during the same orbit, are significantly misaligned (errors of up to 100 km on the surface).

The dataset provides a set of individual images that can be used for scientific analysis and mission planning activities.

NASA / USGS Mars Reconnaissance Orbiter (MRO) Context Camera (CTX) Targeted DTMs

As of March, 2023 the Mars Reconnaissance Orbiter (MRO) High Resolution Science Experiment (HiRISE) sensor has collected more than 5000 targeted stereopairs. During HiRISE acquisition, the Context Camera (CTX) also collects lower resolution, higher spatial extent context images. These CTX acquisitions are also targeted stereopairs. This data set contains targeted CTX DTMs and orthoimages, created using the NASA Ames Stereopipeline. These data have been created using relatively controlled CTX images that have been globally bundle adjusted using the USGS Integrated System for Imagers and Spectrometers (ISIS) jigsaw application. Relative control at global scale reduces common issues such as spacecraft jitter in the resulting DTMs. DTMs were aligned as part of 26 different groupings to the ultimate MOLA product using an iterative pc_align approach. Therefore, all DTMs and orthoimages are absolutely controlled to MOLA, a proxy product for the Mars geodetic coordinate reference frame.

NASA / USGS Released HiRISE Digital Terrain Models

These data are digital terrain models (DTMs) created by multiple different institutions and released to the Planetary Data System (PDS) by the University of Arizona. The data are processed from the Planetary Data System (PDS) stored JP2 files, map projected, and converted to Cloud Optimized GeoTiffs (COGs) for efficient remote data access. These data are controlled to the Mars Orbiter Laser Altimeter (MOLA). Therefore, they are a proxy for the geodetic coordinate reference frame. These data are not guaranteed to co-register with an uncontrolled products (e.g., the uncontrolled High Resolution Science Imaging Experiment (HiRISE) Reduced Data Record (RDR) data). Data are released using simple cylindrical (planetocentric positive East, center longitude 0, -180 - 180 longitude domain) or a pole centered polar stereographic projection. Data are projected to the appropriate IAU Well-known Text v2 (WKT2) represented projection.

NASA / USGS Uncontrolled HiRISE RDRs

These data are red and color Reduced Data Record (RDR) observations collected and originally processed by the High Resolution Imaging Science Experiment (HiRISE) team. The mdata are processed from the Planetary Data System (PDS) stored RDRs, map projected, and converted to Cloud Optimized GeoTiffs (COGs) for efficient remote data access. These data are not photogrammetrically controlled and use a priori NAIF SPICE pointing. Therefore, these data will not co-register with controlled data products. Data are released using simple cylindrical (planetocentric positive East, center longitude 0, -180 - 180 longitude domain) or a pole centered polar stereographic projection. Data are projected to the appropriate IAU Well-known Text v2 (WKT2) represented projection.

NOAA Global Forecast System (GFS)

NOTE - Upgrade NCEP Global Forecast System to v16.3.0 - Effective November 29, 2022 See notification HERE

The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). Dozens of atmospheric and land-soil variables are available through this dataset, from temperatures, winds, and precipitation to soil moisture and atmospheric ozone concentration. The entire globe is covered by the GFS at a base horizontal resolution of 18 miles (28 kilometers) between grid points, which is used by the operational forecasters who predict weather out to 16 days in the future. Horizontal resolution drops to 44 miles (70 kilometers) between grid point for forecasts between one week and two weeks.

The NOAA Global Forecast Systems (GFS) Warm Start Initial Conditions are produced by the National Centers for Environmental Prediction Center (NCEP) to run operational deterministic medium-range numerical weather predictions.
The GFS is built with the GFDL Finite-Volume Cubed-Sphere Dynamical Core (FV3) and the Grid-Point Statistical Interpolation (GSI) data assimilation system.
Please visit the links below in the Documentation section to find more details about the model and the data assimilation systems. The current operational GFS is run at 64 layers in the vertical extending from the surface to the upper stratosphere and on six cubic-sphere tiles at the C768 or 13-km horizontal resolution. A new version of the GFS that has 127 layers extending to the mesopause will be implemented for operation on February 3, 2021. These initial conditions are made available four times per day for running forecasts at the 00Z, 06Z, 12Z and 18Z cycles, respectively. For each cycle, the dataset contains the first guess of the atmosphere states found in the directory ./gdas.yyyymmdd/hh-6/RESTART, which are 6-hour GDAS forecast from the last cycle, and atmospheric analysis increments and surface analysis for the current cycle found in the directory ./gfs.yyyymmdd/hh, which are produced by the data assimilation systems.

NOAA Global Historical Climatology Network Daily (GHCN-D)

UPDATE TO GHCN PREFIXES - The NODD team is working on improving performance and access to the GHCNd data and will be implementing an updated prefix structure. For more information on the prefix changes, please see the "READ ME on the NODD Github". If you have questions, comments, or feedback, please reach out to nodd@noaa.gov with GHCN in the subject line.

Global Historical Climatology Network - Daily is a dataset from NOAA that contains daily observations over global land areas. It contains station-based measurements from land-based stations worldwide, about two thirds of which are for precipitation measurement only. Other meteorological elements include, but are not limited to, daily maximum and minimum temperature, temperature at the time of observation, snowfall and snow depth. It is a composite of climate records from numerous sources that were merged together and subjected to a common suite of quality assurance reviews. Some data are more than 175 years old. The data is in CSV format. Each file corresponds to a year from 1763 to present and is named as such.

NOAA High-Resolution Rapid Refresh (HRRR) Model

The HRRR is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

The HRRR ZARR formatted data was originally generated by the University of Utah under a grant provided by NOAA. They are are continuing to publish ZARR versions of HRRR data. For information about data in the s3://hrrrzarr/ please contact atmos-mesowest@lists.utah.edu.

NOAA's Coastal Ocean Reanalysis (CORA) Dataset: 1979-2022

NOAA's Coastal Ocean Reanalysis (CORA) for the Gulf, East Coast/Atlantic, and Caribbean (GEC) is produced using verified hourly water levels from the National Ocean Service’s Center of Operational Oceanographic Products & Services (CO-OPS). ADvanced CIRCulation Model (ADCIRC) and Simulating WAves Nearshore (SWAN) models are coupled to model coastal water levels and nearshore waves. Hourly water level observations are used for data assimilation and validation to improve the accuracy of modeled water levels and wave datasets.

Additional Details:
Metadata associated with model domain and time span:

Timeseries - 1979 to 2022
Size - Approx. 44.6 TB
Domain - Lat 5.8 to 45.8 ; Long -98.0 to -53.8
Nodes - CORA Metadata Library
Grid cells - CORA Metadata Library
Spatial Resolution:
- Centroids: 300-400 meters
- Gridded: 500 meters
- Projection: 1983 Contiguous USA Albers projection (EPSG:5070)

NOTICE: Upcoming AWS S3 Directory Restructuring:
Water level and wave datasets resulting from the computation, assimilation, validation, and optimization reanalysis datasets. All products are available in NetCDF (.nc) format:

Effective Date: May - September, 2026
Changes: CORA datasets are being restructured within NOAA’s S3 Directory Catalogs to support the addition of new domains, derived data, and product application. Before this point, datasets were stored by version to support prototyping. As production and application evolve, this change supports a more stable long-term infrastructure.
CORA Datasets will be rearranged by
- Domain: CORA-GEC, CORA-Pac, etc.
- Format: Native grid, centroid, and 500m grid, etc.
- Origin: Direct model output or a datasets derived from model outputs (E.g. Shoreline datasets, derived daily and monthly maximums, high tide flood predictions, etc.)
Impacts: The CORA_V1.1_intake.yml file will be updated for new file paths for use with GitHub Repository Notebooks, and/or hard-coded data paths should be updated to reflect new structure.

Datasets:
Water level and wave datasets resulting from the computation, assimilation, validation, and optimization reanalysis datasets. All products are available in NetCDF (.nc) format:

fort.63.nc - Water level elevation
fort.73.nc - Atmospheric pressure at sea level
fort.74.nc - Wind Velocity - 10 m elevation
maxele.63.nc - Maximum water elevation
swan_DIR.63.nc - Spectral mean wave direction
swan_TMM10.63.nc - Spectral mean wave period
swan_TPS.63.nc - Spectral peak wave period
swan_HS.63.nc - Spectral zeroth moment wave height
swan_HS_max.63.nc - Maximum spectral zeroth moment wave height

Derived Products:
Datasets resulting from the computation, modeling, or other processing using existing/collected data. All products are available in NetCDF (.nc) format:

CORA-V1.1-fort.63: Hourly water levels
CORA-V1.1-swan_DIR.63: Hourly mean wave direction
CORA-V1.1-swan_TPS.63: Hourly peak wave periods
CORA-V1.1-swan_HS.63: Hourly significant wave heights
CORA-V1.1-Grid: Hourly water levels interpolated from model nodes to uniform 500-meter resolution grid

NREL National Solar Radiation Database

Released to the public as part of the Department of Energy's Open Energy Data Initiative, the National Solar Radiation Database (NSRDB) is a serially complete collection of hourly and half-hourly values of the three most common measurements of solar radiation – global horizontal, direct normal, and diffuse horizontal irradiance — and meteorological data. These data have been collected at a sufficient number of locations and temporal and spatial scales to accurately represent regional solar radiation climates.

OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1)

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) validated product consists of radar backscatter normalized with respect to the topography. The product maps signals related to the physical properties of ground scattering objects, such as surface roughness and soil moisture and/or vegetation. The OPERA RTC-S1 product is derived from Copernicus Sentinel-1 Interferometric Wide (IW) Single Look Complex (SLC) data with a near global scope and temporal sampling coincident with the availability of S1 SLC data. Each OPERA RTC-S1 product corresponds to a single S1 burst projected onto a pre-defined UTM/Polar stereographic map projection system map grid with a 30-meter spacing. The Copernicus global 30 m (GLO-30) Digital Elevation Model (DEM) is the reference DEM used to correct for the impacts of topography and to geocode the product. The OPERA RTC-S1 product is normalized to the backscatter coefficient gamma-naught, ɣ0, obtained from the original radar brightness beta-naught, β0, through radiometric terrain correction. The RTC-S1 product is distributed as cloud optimized GeoTIFFs with one GeoTIFF file per processed polarization. The RTC-S1 product metadata is provided in the Hierarchical Data Format version 5 (HDF5) format. The OPERA RTC-S1 product contains modified Copernicus Sentinel data (2016-2025).Due to the S1 mission’s narrow orbital tube, radar-geometry layers such as incidence angle, local incidence angle, number of looks, and RTC Area Normalization Factor (ANF) vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA RTC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static-layers generation algorithm. The static layers are available in the associated OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1) dataset. Read our doc on how to get AWS Credentials to retrieve this data: https://cumulus.asf.alaska.edu/s3credentialsREADME

OpenAlex dataset

An open, comprehensive index of scolarly papers, citations, authors, institutions, and journals.

OpenCell on AWS

The OpenCell project is a proteome-scale effort to measure the localization and interactions of human proteins using high-throughput genome engineering to endogenously tag thousands of proteins in the human proteome. This dataset consists of the raw confocal fluorescence microscopy images for all tagged cell lines in the OpenCell library.These images can be interpreted both individually, to determine the localization of particular proteins of interest, and in aggregate, by training machine learning models to classify or quantify subcellular localization patterns.

OpenFold3 Training Data

This dataset contains MSAs and predicted structures used to train OpenFold3 preview, an open-source, all-atom ligand, RNA and protein structure prediction software. This includes -

PDB - 245k structures and alignments from the RCSB Protein Data Bank - https://www.rcsb.org/
Long monomer distillation set - ~13 million long (sequence length >= 200 amino acids) monomers from the MGNIFY database - https://www.ebi.ac.uk/metagenomics/.
Short monomer distillation set - 400k short (sequence length < 200 amino acid) monomers from the MGNIFY database - https://www.ebi.ac.uk/metagenomics/.
Disordered set - AF2-predicted structures for unresolved segments missing from the PDB
RNA - OF3p2-predicted RNA monomer structures generated from a clustered version of RFAM (current version) For the distillation sets MSAs were generated using the AF3 protocol, and were used to predict structures with AlphaFold2, more details can be found in our whitepaper - https://portal.openfold.omsf.io/reports/of3p2_technical_report.pdf For a full description and an interactive data explorer, please visit https://portal.openfold.omsf.io/datasets

Refgenie reference genome assets

Pre-built refgenie reference genome data assets used for aligning and analyzing DNA sequence data.

SILO climate data on AWS

SILO is a database of Australian climate data from 1889 to the present. It provides continuous, daily time-step data products in ready-to-use formats for research and operational applications. SILO's gridded datasets (in NetCDF and GeoTiff formats) are hosted on AWS Public Data. Point data (at both station and grid cell locations) are available from the SILO website. Incremental update files for mirroring point datasets at station locations are also available on AWS Public Data.

Sea Surface Temperature Daily Analysis: European Space Agency Climate Change Initiative product version 2.1

Global daily-mean sea surface temperatures, presented on a 0.05° latitude-longitude grid, with gaps between available daily observations filled by statistical means, spanning late 1981 to recent time. Suitable for large-scale oceanographic meteorological and climatological applications, such as evaluating or constraining environmental models or case-studies of marine heat wave events. Includes temperature uncertainty information and auxiliary information about land-sea fraction and sea-ice coverage. For reference and citation see: www.nature.com/articles/s41597-019-0236-x.

Sentinel-2 L2A 120m Mosaic

Sentinel-2 L2A 120m mosaic is a derived product, which contains best pixel values for 10-daily periods, modelled by removing the cloudy pixels and then performing interpolation among remaining values. As there are some parts of the world, which have lengthy cloudy periods, clouds might be remaining in some parts. The actual modelling script is available here.

Sentinel-3

This data set consists of observations from the Sentinel-3 satellite of the European Commission’s Copernicus Earth Observation Programme. Sentinel-3 is a polar orbiting satellite that completes 14 orbits of the Earth a day. It carries the Ocean and Land Colour Instrument (OLCI) for medium resolution marine and terrestrial optical measurements, the Sea and Land Surface Temperature Radiometer (SLSTR), the SAR Radar Altimeter (SRAL), the MicroWave Radiometer (MWR) and the Precise Orbit Determination (POD) instruments. The satellite was launched in 2016 and entered routine operational phase in 2017. Data is available from July 2017 onwards.

Sentinel-5P Level 2

This data set consists of observations from the Sentinel-5 Precursor (Sentinel-5P) satellite of the European Commission’s Copernicus Earth Observation Programme. Sentinel-5P is a polar orbiting satellite that completes 14 orbits of the Earth a day. It carries the TROPOspheric Monitoring Instrument (TROPOMI) which is a spectrometer that senses ultraviolet (UV), visible (VIS), near (NIR) and short wave infrared (SWIR) to monitor ozone, methane, formaldehyde, aerosol, carbon monoxide, nitrogen dioxide and sulphur dioxide in the atmosphere. The satellite was launched in October 2017 and entered routine operational phase in March 2019. Data is available from July 2018 onwards.

SiPeCaM (Sitios Permanentes de la Calibración y Monitoreo de la Biodiversidad)

The SiPeCaM goal is to create a data source that allows to evaluate changes in the biodiversity state, considering key aspect of how does the ecosystem behaves.

Speedtest by Ookla Global Fixed and Mobile Network Performance Maps

Global fixed broadband and mobile (cellular) network performance, allocated to zoom level 16 web mercator tiles (approximately 610.8 meters by 610.8 meters at the equator). Data is provided in both Shapefile format as well as Apache Parquet with geometries represented in Well Known Text (WKT) projected in EPSG:4326. Download speed, upload speed, and latency are collected via the Speedtest by Ookla applications for Android and iOS and averaged for each tile. Measurements are filtered to results containing GPS-quality location accuracy.

Storm EVent ImageRy (SEVIR)

Collection of spatially and temporally aligned GOES-16 ABI satellite imagery, NEXRAD radar mosaics, and GOES-16 GLM lightning detections.

Synthea synthetic patient generator data in OMOP Common Data Model

The Synthea generated data is provided here as a 1,000 person (1k), 100,000 person (100k), and 2,800,000 persom (2.8m) data sets in the OMOP Common Data Model format. SyntheaTM is a synthetic patient generator that models the medical history of synthetic patients. Our mission is to output high-quality synthetic, realistic but not real, patient data and associated health records covering every aspect of healthcare. The resulting data is free from cost, privacy, and security restrictions. It can be used without restriction for a variety of secondary uses in academia, research, industry, and government (although a citation would be appreciated). You can read our first academic paper here: https://doi.org/10.1093/jamia/ocx079

The Impact of Variation on Function Consortium (IGVF)

The IGVF (Impact of Genomic Variation on Function) Consortium aims to understand how genomic variation affects genome function, which in turn impacts phenotype. The NHGRI is funding this collaborative program that brings together teams of investigators who will use state-of-the-art experimental and computational approaches to model, predict, characterize and map genome function, how genome function shapes phenotype, and how these processes are affected by genomic variation. These joint efforts will produce a catalog of the impact of genomic variants on genome function and phenotypes.
The Data Corpus consists of single-cell Genomics experiments (both single modal, and multimodal, typically snRNA-seq and snATAC-seq), Characterization experiments using Massively Parallel Reporter Assays (MPRAs) and CRISPR-screens along with a variety of protein mutatation assays, and Predictive Models. There are a huge variety of files in IGVF that are stored in the AWS OpenData Set so we recommend using the metadata file or browsing the IGVF Data Portal

UK Biobank Linkage Disequilibrium Matrices

Linkage disequilibrium (LD) matrices of UK Biobank participants of a British ancestry, based on imputed genotypes.

UK Biobank Pan-Ancestry Summary Statistics

A multi-ancestry analysis of 7,221 phenotypes using a generalized mixed model association testing framework, spanning 16,119 genome-wide association studies. We provide standard meta-analysis across all populations and with a leave-one-population-out approach for each trait. The data are provided in tsv format (per phenotype) and Hail MatrixTable (all phenotypes and variants). Metadata is provided in phenotype and variant manifests.

Version 2 High Resolution Canopy Height Maps by WRI and Meta

Version 2 Global and regional Canopy Height Maps (CHMv2). Created using machine learning models on high-resolution worldwide Vantor satellite imagery.

Yale-CMU-Berkeley (YCB) Object and Model Set

This project primarily aims to facilitate performance benchmarking in robotics research. The dataset provides mesh models, RGB, RGB-D and point cloud images of over 80 objects. The physical objects are also available via the YCB benchmarking project. The data are collected by two state of the art systems: UC Berkley's scanning rig and the Google scanner. The UC Berkley's scanning rig data provide meshes generated with Poisson reconstruction, meshes generated with volumetric range image integration, textured versions of both meshes, Kinbody files for using the meshes with OpenRAVE, 600 High-resolution RGB images, 600 RGB-D images, and 600 point cloud images for each object. The Google scanner data provides 3 meshes with different resolutions (16k, 64k, and 512k polygons), textured versions of each mesh, Kinbody files for using the meshes with OpenRAVE.

Zwicky Transient Facility (ZTF)

The Zwicky Transient Facility (ZTF) is a time-domain astronomy survey that uses the Palomar 48 inch Schmidt telescope and a custom-built wide-field camera to image the night sky in three photometric filters (g, r, and i). It is a fully-automated survey aimed at a systematic exploration of optical transient phenomena. It completes a scan of the observable northern sky approximately every three nights.

iSDAsoil

iSDAsoil is a resource containing soil property predictions for the entire African continent, generated using machine learning. Maps for over 20 different soil properties have been created at 2 different depths (0-20 and 20-50cm). Soil property predictions were made using machine learning coupled with remote sensing data and a training set of over 100,000 analyzed soil samples. Included in this dataset are images of predicted soil properties, model error and satellite covariates used in the mapping process.

real-changesets

The real-changesets is an augmented representation of OpenStreetMap changesets in JSON format. It contains the current and the previous version of each feature in a changeset. It's primary used by OSMCha, the main OpenStreetMap validation tool, to have a visualization of the changeset and provide to the user the understanding of what was changed on the map. The real-changesets are created by combining the changeset metadata and the augmented diff generated by overpass.

1000 Genomes

The 1000 Genomes Project is an international collaboration which has established the most detailed catalogue of human genetic variation, including SNPs, structural variants, and their haplotype context. The final phase of the project sequenced more than 2500 individuals from 26 different populations around the world and produced an integrated set of phased haplotypes with more than 80 million variants for these individuals.

AG-LOAM Dataset

AG-LOAM dataset has been released to facilitate the evaluation of LiDAR-based odometry algorithms in agricultural environments.

It was collected by a wheeled mobile robot at the Agricultural Experimental Station of the University of California, Riverside, during Winter 2022 and Winter 2023.
It provides LiDAR point cloud data captured using a Velodyne VLP-16 sensor, along with ground-truth trajectories obtained from an RTK-GPS system.
It consists of 18 sequences collected over three phases, covering diverse planting environments, terrain conditions, path patterns, and robot motion profiles.
It spans a total operation time of 3 hours, covers a total distance of 7.5 km, and constitutes 150 GB of data.

AI3 Protein-Ligand Binding Affinity Dataset

The rapid advancement of computing technologies, particularly artificial intelligence (AI), has revolutionized various domains, including drug discovery. Curated datasets are crucial for developing reliable, generalizable, and accurate models for practical applications. Generating experimental data on a large scale is an expensive and arduous process. In domains such as medical diagnostics where real-life data is hard to obtain, synthetic data has been shown to be extremely valuable. We, teams from IIIT Hyderabad, Intel, AWS, and Insilico Medicine, have performed physics-based calculations (molecular dynamics simulations) on about 20,000 protein-ligand complexes. The dataset comprises molecular dynamics snapshots, binding affinities calculated using the MM-PBSA method, and individual energy components, including electrostatic and van der Waals interactions. DatasetFileFormats essentially incorporate i. 3D coordinates of the protein-ligand complexes (pdb) in tar.gz files, and ii. CSV files containing the energy data. DatasetUsages are on i. ML scoring function for predicting binding affinities of given protein-ligand complexes, ii. Classification models for predicting correct binding poses of ligands, iii. identification of cryptic binding pockets, and iv. optimization of binding features by exploiting the individual components of the energy (experimental data has only the total binding affinity). Further, the novelty of the dataset highlights the fact that existing AI/ML training datasets lack dynamic data and are inherently biased. Further, binding affinity data existing in the literature are obtained from different experimental protocols. Therefore, this dataset has been uniquely created (from the same computational protocols) followed by free energy calculations with molecular dynamics (MD) simulations. The dynamic data-enriched protein-ligand coordinates can be used to effectively train convolutional neural network-based regression models for more accurate binding affinity prediction.

ASF SAR Data Products for Disaster Events

synthetic Aperture Radar (SAR) data is a powerful tool for monitoring and assessing disaster events and can provide valuable insights for researchers, scientists, and emergency response teams. The Alaska Satellite Facility (ASF) curates this collection of (primarily) SAR and SAR-derived satellite data products from a variety of data sources for disaster events.

AdaptiveFlow Ligand Libraries

AdaptiveFlow Versions of Ligand Libraries in Ready-To-Dock Format

Allen Ivy Glioblastoma Atlas

This dataset consists of images of glioblastoma human brain tumor tissue sections that have been probed for expression of particular genes believed to play a role in development of the cancer. Each tissue section is adjacent to another section that was stained with a reagent useful for identifying histological features of the tumor. Each of these types of images has been completely annotated for tumor features by a machine learning process trained by expert medical doctors.

Allen Mouse Brain Atlas

The Allen Mouse Brain Atlas is a genome-scale collection of cellular resolution gene expression profiles using in situ hybridization (ISH). Highly methodical data production methods and comprehensive anatomical coverage via dense, uniformly spaced sampling facilitate data consistency and comparability across >20,000 genes. The use of an inbred mouse strain with minimal animal-to-animal variance allows one to treat the brain essentially as a complex but highly reproducible three-dimensional tissue array. The entire Allen Mouse Brain Atlas dataset and associated tools are available through an unrestricted web-based viewing application (http://mouse.brain-map.org). The collection of > 650,000 images have been made available in this Open Data bucket to enable efficient access and analysis of the this dataset.

Beat Acute Myeloid Leukemia (AML) 1.0

Beat AML 1.0 is a collaborative research program involving 11 academic medical centers who worked collectively to better understand drugs and drug combinations that should be prioritized for further development within clinical and/or molecular subsets of acute myeloid leukemia (AML) patients. Beat AML 1.0 provides the largest-to-date dataset on primary acute myeloid leukemia samples offering genomic, clinical, and drug response.This dataset contains open Clinical Supplement and RNA-Seq Gene Expression Quantification data.This dataset also contains controlled Whole Exome Sequencing (WXS) and RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification

Blended TROPOMI+GOSAT Satellite Data Product for Atmospheric Methane

A dataset of satellite retrievals of atmospheric methane that extends from 30 April 2018 to present.

BraiDyn-BC: Cued lever-pull task dataset

The BraiDyn-BC (Brain Dynamics underlying emergence of Behavioral Change) Database offers an extensive, multimodal dataset that links wide-field calcium imaging of the mouse neocortex to comprehensive behavioral measurements during a behavioral task. As one of the contents in this database, we newly provide a dataset that includes 15 sessions spanning two weeks of motor skill learning, in which 25 mice were trained to pull a lever to obtain water rewards. Simultaneous high-speed videography captures body, facial, and eye movements, and environmental parameters are monitored. The dataset also features resting-state cortical activity and sensory-evoked responses, enhancing its utility for both learning-related and sensory-driven neural dynamics studies. Data are formatted in accordance with the Neurodata Without Borders (NWB) standard, ensuring compatibility with existing analysis tools and adherence to the FAIR principles. This resource enables in-depth investigations into the neural mechanisms underlying behavior and learning. The platform encourages collaborative research, supporting the exploration of rapid within-session learning effects, long-term behavioral adaptations, and neural circuit dynamics.

COBRA

This page describes the COBRA (Classification Of Basal cell carcinoma, Risky skin cancers and Abnormalities) skin pathology dataset, which comprises over 7000 histopathology whole-slide-images related to the diagnosis of basal cell carcinoma skin cancer, the most commonly diagnosed cancer. The dataset includes biopsies and excisions and is divided into four groups. The first group contains about 2,500 BCC biopsies with subtype labels, while the second group includes 2,500 non-BCC biopsies with different types of skin dysplasia. The third group has 1,000 labelled risky cancer biopsies, including rare and dangerous types like melanoma, squamous cell carcinoma, and Merkel cell carcinoma. Finally, the fourth group contains 1,000 BCC excisions with or without free tumor margins, with 300 fully annotated. The dataset will be released over 2023 in stages, starting with the BCC and non-BCC biopsies, followed by BCC excisions, and finally the risky cancer biopsies.

COVID-19 Harmonized Data

A harmonized collection of the core data pertaining to COVID-19 reported cases by geography, in a format prepared for analysis

CanElevation - LiDAR Point Clouds

The LiDAR Point Clouds is a product that is part of the CanElevation Series created to support the National Elevation Data Strategy implemented by NRCan. This product contains point clouds from various airborne LiDAR acquisition projects conducted in Canada. These airborne LiDAR acquisition projects may have been conducted by NRCan or by various partners. The LiDAR point cloud data is licensed under an open government license and has been incorporated into the National Elevation Data Strategy. Point cloud files are distributed by LiDAR acquisition project without integration between projects. The point cloud files are distributed using the compressed .LAZ / Cloud Optimized Point Cloud (COPC) format. The COPC open format is an octree reorganization of the data inside a .LAZ 1.4 file. It allows efficient use and visualization rendering via HTTP calls (e.g. via the web), while offering the capabilities specific to the compressed .LAZ format which is already well established in the industry. Point cloud files are therefore both downloadable for local use and viewable via URL links from a cloud computing environment. The reference system used for all point clouds in the product is NAD83(CSRS), epoch 2010. The projection used is the UTM projection with the corresponding zone. Elevations are orthometric and expressed in reference to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013).

Le produit Nuages de points lidar fait partie de la Série CanÉlévation créée pour appuyer la Stratégie nationale de données d’élévation mise en oeuvre par Ressources naturelles Canada (RNCan). Ce produit contient les nuages de points obtenus lors de divers projets d’acquisition par lidar aéroporté réalisés au Canada. Ces projets d’acquisition par lidar aéroporté peuvent avoir été réalisés par RNCan ou par divers partenaires. Les données de nuages de points lidar ont une licence de type gouvernement ouvert et ont été intégrés à la Stratégie nationale de données d’élévation. Les fichiers de nuages de points sont distribués par projet d'acquisition et sans intégration entre les projets. Les fichiers de nuages de points sont distribués en format compressé .LAZ / Cloud Optimized Point Cloud (COPC). Le format ouvert COPC est une réorganisation en octree des données à l’intérieur même d’un fichier .LAZ 1.4. Il permet une utilisation et un rendu de visualisation efficace via des appels HTTP (ex : via le web), tout en offrant les capacités propres au format .LAZ compressé qui est déjà bien établi dans l’industrie. Les fichiers de nuages de points sont donc autant téléchargeables pour une utilisation locale que visualisables via des liens URL provenant d’un environnement infonuagique. Le système de référence utilisé pour tous les nuages de points du produit est le NAD83(SCRS), époque 2010. La projection utilisée est la projection UTM avec le fuseau correspondant. Les élévations sont orthométriques et exprimées par rapport au Système canadien de référence altimétrique de 2013 (CGVD2013).

Canopy Tree Height Map for the Amazon Forest (mean height composite 2020-2024) by CTrees.org

Mean canopy Tree Height for the Amazon Forest on the period 2020-2024 at 4.78 m of spatial resolution. Created using a deep learning model on high-resolution Planet imagery from the Norway's International Climate and Forest Initiative (NICFI) Satellite Data Program. From the original research paper https://doi.org/10.48550/arXiv.2501.10600

Cell Organelle Segmentation in Electron Microscopy (COSEM) on AWS

High resolution images of subcellular structures.

CitrusFarm Dataset

CitrusFarm is a multimodal agricultural robotics dataset that provides both multispectral images and navigational sensor data for localization, mapping and crop monitoring tasks.

It was collected by a wheeled mobile robot in the Agricultural Experimental Station at the University of California Riverside in the summer of 2023.
It offers a total of nine sensing modalities, including stereo RGB, depth, monochrome, near-infrared and thermal images, as well as wheel odometry, LiDAR, IMU and GPS-RTK data.
It comprises seven sequences collected from three citrus tree fields, featuring various tree species at different growth stages, distinctive planting patterns, as well as varying daylight conditions.
It spans a total operation time of 1.7 hours, covers a total distance of 7.5 km, and constitutes 1.3 TB of data.

Clinical Trial Sequencing Project - Diffuse Large B-Cell Lymphoma

The goal of the project is to identify recurrent genetic alterations (mutations, deletions, amplifications, rearrangements) and/or gene expression signatures. National Cancer Institute (NCI) utilized whole genome sequencing and/or whole exome sequencing in conjunction with transcriptome sequencing. The samples were processed and submitted for genomic characterization using pipelines and procedures established within The Cancer Genome Analysis (TCGA) project.

DeepDrug Protein Embeddings Bank (DPEB)

DPEB is a multimodal database of human protein embeddings integrating four biologically complementary representations—AlphaFold2, BioEmbeddings, ESM-2, and ProtVec—designed for enhanced protein-protein interaction prediction and functional classification.

Department of Energy’s Geothermal Data Repository (GDR) Data Lake

Data released from projects funded by the Department of Energy's Geothermal Technologies Office (DOE GTO) that are too large or complex to be conveniently accessed by traditional means. The GDR data lake aims to improve and automate access of high-value geothermal data sets, making data actionable and discoverable by researchers and industry to accelerate analysis and advance innovation. This data lake is a sister-data lake to the Department of Energy’s Open Energy Data Initiative (OEDI) Data Lake.

EURO-CORDEX - European component of the Coordinated Regional Downscaling Experiment

The EURO-CORDEX dataset contains regional climate model data for Europe, for use in impacts, decision-making, and climate science. Currently, the bucket contains monthly datasets of 2m air temperature downscaled from CMIP5 global model datasets using different regional climate models.

Exceptional Responders Initiative

The Exceptional Responders Initiative is a pilot study to investigate the underlying molecular factors driving exceptional treatment responses of cancer patients to drug therapies. Study researchers will examine molecular profiles of tumors from patients either enrolled in a clinical trial for an investigational drug(s) and who achieved an exceptional response relative to other trial participants, or who achieved an exceptional response to a non-investigational chemotherapy. An exceptional response is defined as achievement of either a complete response or a partial response for at least 6 months duration in a trial or treatment where the overall response rate is < 10%. The hope is to discover underlying molecular features that can be further investigated and may eventually predict benefit from a given drug or class of drugs for a particular patient. This pilot project will successfully characterize approximately 100 cases of tumor tissue and, when available, case-matched germline DNA. All samples will undergo whole exome sequencing, and cases with sufficient nucleic acids will undergo additional analyses (e.g. whole genome sequencing, mRNA-sequencing, mi RNA sequencing, promoter methylation analysis, SNP etc). Each case will be annotated with demographic and clinical information, along with follow-up information minimally sufficient to correlate molecular profiles with response. Both retrospective and prospective collections will be considered. The project will also accept sequencing data and clinical data from patients who have had sequencing performed outside of this project. All data will be de-identified and placed in a controlled access database so other investigators may use them for additional insights. Clinically annotated tissue specimens meeting the criteria will be provided by groups participating in the Exceptional Cases Initiative to a Biospecimen Core Resource (BCR), which will perform quality control on the tissues, and will use a standard operating procedure to isolate nucleic acids. The nucleic acids will be shipped to a sequencing center to perform whole exome sequencing and analysis. These findings will be made available to the broader cancer research community in a controlled access database.

Finnish Meteorological Institute Weather Radar Data

The up-to-date weather radar from the FMI radar network is available as Open Data. The data contain both single radar data along with composites over Finland in GeoTIFF and HDF5-formats. Available composite parameters consist of radar reflectivity (DBZ), rainfall intensity (RR), and precipitation accumulation of 1, 12, and 24 hours. Single radar parameters consist of radar reflectivity (DBZ), radial velocity (VRAD), rain classification (HCLASS), and Cloud top height (ETOP 20). Raw volume data from singe radars are also provided in HDF5 format with ODIM 2.3 conventions. Radar data becomes available as soon as it's received from the radar and pre-processed into deliverable formats. Typically the most recent radar data was collected less than 5 minutes ago.

Foundation Medicine Adult Cancer Clinical Dataset (FM-AD)

The Foundation Medicine Adult Cancer Clinical Dataset (FM-AD) is a study conducted by Foundation Medicine Inc (FMI). Genomic profiling data for approximately 18,000 adult patients with a diverse array of cancers was generated using FoundationeOne, FMI's commercially available, comprehensive genomic profiling assay. This dataset contains open Clinical and Biospecimen data.

GHRSST Level 4 MUR Global Foundation Sea Surface Temperature Analysis (v4.1)

A Group for High Resolution Sea Surface Temperature (GHRSST) Level 4 sea surface temperature analysis produced as a retrospective dataset (four day latency) and near-real-time dataset (one day latency) at the JPL Physical Oceanography DAAC using wavelets as basis functions in an optimal interpolation approach on a global 0.01 degree grid. The version 4 Multiscale Ultrahigh Resolution (MUR) L4 analysis is based upon nighttime GHRSST L2P skin and subskin SST observations from several instruments including the NASA Advanced Microwave Scanning Radiometer-EOS (AMSR-E), the JAXA Advanced Microwave Scanning Radiometer 2 on GCOM-W1, the Moderate Resolution Imaging Spectroradiometers (MODIS) on the NASA Aqua and Terra platforms, the US Navy microwave WindSat radiometer, the Advanced Very High Resolution Radiometer (AVHRR) on several NOAA satellites, and in situ SST observations from the NOAA iQuam project. The ice concentration data are from the archives at the EUMETSAT Ocean and Sea Ice Satellite Application Facility (OSI SAF) High Latitude Processing Center and are also used for an improved SST parameterization for the high-latitudes. The dataset also contains additional variables for some granules including a SST anomaly derived from a MUR climatology and the temporal distance to the nearest IR measurement for each pixel.This dataset is funded by the NASA MEaSUREs program ( http://earthdata.nasa.gov/our-community/community-data-system-programs/measures-projects ), and created by a team led by Dr. Toshio M. Chin from JPL. It adheres to the GHRSST Data Processing Specification (GDS) version 2 format specifications. Use the file global metadata "history:" attribute to determine if a granule is near-realtime or retrospective. Read our doc on how to get AWS Credentials to retrieve this data: https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME

Global Cache of Japan

Global real-time Earth system data deemed by the World Meteorological Organisation (WMO) as essential for provision of services for the protection of life and property and for the well-being of all nations. Data is sourced from all WMO Member countries / territories and retained for 24-hours. JMA operate this Global Cache service curating and publishing the dataset on behalf of WMO.

Golden Retriever Lifetime Study: Whole genome genotyping of Golden Retrievers on Axiom HD Arrays

Morris Animal Foundation’s Golden Retriever Lifetime Study is a longitudinal, prospective study following 3044 golden retrievers. The Study’s purpose is to identify the nutritional, environmental, lifestyle and genetic risk factors for cancer and other diseases. The Golden Oldie’s study enrolled an additional cohort of golden retrievers that had reached the age of 12 years or older and had not yet been diagnosed with a malignant cancer. This population can be used as a control group for conditions with high mortality in younger age. This dataset contains the data for ~1.1 million genetic markers from 3224 unique individual dogs processed using Axiom Canine Genotyping Array Sets A and B. Of the 3244 individual samples, 3024 were from the Golden Retriever Lifetime Study cohort and 200 from the Golden Oldies cohort.

Human and Mammalian Brain Atlas

Human and Mammalian Brain Atlas (HMBA) is a major atlas of the BRAIN Initiative Cell Atlas Network (BICAN) that proposes to establish a comprehensive, highly granular cell atlas in complete adult human, macaque, and marmoset brains that links brain structure, function and cellular architecture. Release artifacts have been made available in this OpenData bucket to enable utilization along with their paper publications by the neuroscience community.

I-CARE:International Cardiac Arrest REsearch consortium Electroencephalography Database

The International Cardiac Arrest REsearch consortium (I-CARE) Database includes baseline clinical information and continuous electroencephalography (EEG) recordings from 1,020 comatose patients with a diagnosis of cardiac arrest who were admitted to an intensive care unit from seven academic hospitals in the U.S. and Europe. Patients were monitored with 18 bipolar EEG channels over hours to days for the diagnosis of seizures and for neurological prognostication. Long-term neurological function was determined using the Cerebral Performance Category scale.

Imaging MIT Licensed data and models

This dataset contains a diverse range of imaging biological data and models. The data is sourced and curated by a team of experts at Biohub and is made available as part of these datasets only when it is not publicly accessible or requires transformations to support model training.

Indiana Statewide Digital Aerial Imagery Catalog

The State of Indiana Geographic Information Office and IOT Office of Technology manage a series of digital orthophotography dating back to 2005. Every year's worth of imagery is available as Cloud Optimized GeoTIFF (COG) files, original GeoTIFF, and other compressed deliverables such as ECW and MrSID. Additionally, each imagery year is organized into a tile grid scheme covering the entire geography of Indiana. All years of imagery are tiled from a 5,000 ft grid or sub tiles depending upon the resolution of the imagery. The naming of the tiles reflects the lower left coordinate from the image.

Indiana Statewide Elevation Catalog

The State of Indiana Geographic Information Office and IOT Office of Technology manage a series of digital LiDAR LAS files stored in AWS, dating back to the 2011-2013 collection and including the NRCS-funded 2016-2020 collection. These LiDAR datasets are available as uncompressed LAS files, for cloud storage and access. Each year's data is organized into a tile grid scheme covering the entire geography of Indiana, ensuring easy access and efficient processing. The tiles' naming reflects each tile's lower left coordinate, facilitating accurate data management and retrieval. The AWS storage solution ensures that these extensive datasets are readily accessible for analysis and application across various projects.

Japanese Tokenizer Dictionaries

Japanese Tokenizer Dictionaries for use with MeCab.

Kraken2 NCBI RefSeq Complete V205 database on AWS

Database for use with Kraken2 (taxonomic annotation of metagenomic sequencing reads) including all NCBI RefSeq genomes available in release V205

MIMIC-III (‘Medical Information Mart for Intensive Care’)

MIMIC-III (‘Medical Information Mart for Intensive Care’) is a large, single-center database comprising information relating to patients admitted to critical care units at a large tertiary care hospital. Data includes vital signs, medications, laboratory measurements, observations and notes charted by care providers, fluid balance, procedure codes, diagnostic codes, imaging reports, hospital length of stay, survival data, and more. The database supports applications including academic and industrial research, quality improvement initiatives, and higher education coursework. The MIMIC-III dataset is freely-available. Researchers seeking to use the database must formally request access. For details, see the getting started page. Once you have a PhysioNet account, you must enable access to the MIMIC-III dataset from your AWS account. To do this, please input your AWS account number, and request access to the MIMIC-III Clinical Database on AWS.

Medical Segmentation Decathlon

With recent advances in machine learning, semantic segmentation algorithms are becoming increasingly general purpose and translatable to unseen tasks. Many key algorithmic advances in the field of medical imaging are commonly validated on a small number of tasks, limiting our understanding of the generalisability of the proposed contributions. A model which works out-of-the-box on many tasks, in the spirit of AutoML, would have a tremendous impact on healthcare. The field of medical imaging is also missing a fully open source and comprehensive benchmark for general purpose algorithmic validation and testing covering a large span of challenges, such as: small data, unbalanced labels, large-ranging object scales, multi-class labels, and multimodal imaging, etc. This challenge and dataset aims to provide such resource through the open sourcing of large medical imaging datasets on several highly different tasks, and by standardising the analysis and validation process.

Met Office Global Deterministic 10km on a 2-year rolling archive

THIS DATASET IS CHANGING

Files uploaded from late January 2026 onward will contain changes including:

precision changes
new parameters
changes to existing parameters e.g. adding vertical levels and timesteps
the height_asl_on_pressure_levels parameter will be replaced by geopotential_height_on_pressure_levels

Please check your systems are prepared for these changes.

A numerical weather prediction forecast for the whole globe, with a resolution of approximately 0.09 degrees i.e. 10km (2,560 x 1,920 grid points). The data is available as NetCDF files. It's offered on a free, unsupported basis, so we don't recommend using it for any critical business purposes.

The global deterministic model is a global configuration of the Unified Model, which is the Met Office’s flagship Numerical Weather Prediction model. The model’s initial state is kept close to the real atmosphere using hybrid 4D-Var data assimilation.

The archive contains data from the past two years. The data is typically available approximately 3 to 6 hours after the model run time.

The following timesteps are available for most parameters:

every hour from 0 to 54 hours
every 3 hours from 57 to 144 hours
every 6 hours from 150 to 168 hours

Met Office Global Ocean model on a 2-year rolling archive

The Global Ocean component of the Met Office Global Coupled Atmosphere-Land-Ocean-Ice system which has been running in operations since May 2022. The system provides a global physical analysis and coupled forecast products providing 3D daily mean fields of temperature and salinity, zonal and meridional velocities; 2D daily mean fields of sea surface height, bottom temperature, mixed layer depth, sea ice fraction, sea ice thickness and sea ice zonal and meridional velocities; and instantaneous hourly fields for sea surface height, sea surface temperature and surface currents. The Met Office Global Coupled Atmosphere-Land-Ocean-Ice system is comprised of both deterministic and ensemble forecasting systems both of which are coupled to an interactive ocean. Ocean and ice analysis and forecast products are currently generated from the deterministic system once a day with forecast with lead time out to 7 days, interpolated onto a regular latitude-longitude grid products by our Global Marine Post Processing (MaPP-GL) system.

Met Office Global Wave model on a 2-year rolling archive

The Met Office runs global wave forecast models to support marine safety and operational decision making. Met Office configurations are developed to be run using the community wave model WAVEWATCH IIITM. The global wave configuration is designed to generate accurate forecasts for open waters of the world’s oceans and larger seas. The Met Office wave models are forced using wind data from the Met Office Global Atmospheric Hi-Res Model. The global wave model is run to provide a five day outlook for wave characteristics defining height, period and direction of waves within a given sea-state. The model uses a refined grid system in order to better represent fetch in constrained sea areas and blocking effects of islands and headlands. Please note this is a public beta and therefore a non-operational service.

Met Office NWS Ocean model on a 2-year rolling archive

The Northwest European continental shelf physical ocean model predicts temperature, salinity and circulation for waters surrounding the UK. Ocean physics analysis provides a 6-day forecast for the North-West European Atlantic shelf at 1.5km resolution:

33 depth levels
Currents
Salinity
Temperature
Mixing Layer Thickness

Met Office NWS Wave model on a 2-year rolling archive

Northwest European continental shelf regional wave model predicting sea-state and various sea and swell wave characteristics for waters surrounding the UK.The Met Office runs global and regional wave forecast models to support marine safety and operational decision making. Met Office configurations are developed to be run using the community wave model WAVEWATCH IIITM. The global wave configuration is designed to generate accurate forecasts for open waters of the world's oceans and larger seas, whilst regional configurations are run in order to improve accuracy closer to the coast. The Met Office wave models are forced using wind data from the Met Office Global Atmospheric Hi-Res Model and, where appropriate in regional configurations, currents from the Met Office shelf seas model.Met Office NWS Wave (UK wave) Model 6 day forecast to provide an outlook for wave characteristics defining height, period and direction of waves within a given sea-state. The model uses a refined grid system in order to better represent fetch in constrained sea areas and blocking effects of islands and headlands. Data are released at the model's base resolution of 0.030303 degrees longitude by 0.0135135 degrees latitude approximating to a 1.5km grid with a daily update cycle at 00, 06, 12, 18z

Met Office UK Deterministic (UKV)2km on a 2-year rolling archive

THIS DATASET IS CHANGING

Files uploaded from late January 2026 onward will contain changes including:

precision changes
new parameters
changes to existing parameters e.g. adding vertical levels and timesteps
the height_asl_on_pressure_levels parameter will be replaced by geopotential_height_on_pressure_levels

Please check your systems are prepared for these changes.

A high-resolution gridded weather forecast for the UK, with a resolution of 0.018 degrees, projected on to a 2km horizontal grid. The data is available as NetCDF files. It's offered on a free, unsupported basis, so we don’t recommend using it for any critical business purposes.

Based on the Met Office UKV model, which is a deterministic, numerical weather prediction model for the UK and Ireland. It is a UK configuration of the Unified Model, which is the Met Office’s flagship Numerical Weather Prediction model.

The archive contains data from the past two years. The data is typically available approximately 3 to 6 hours after the model run time.

The following timesteps are available:

every hour from 0 to 54 hours (for most parameters)
every 3 hours from 57 to 120 hours

For some parameters, data is available for multiple vertical height or pressure levels through the atmosphere.

Multiview Extended Video with Activities (MEVA)

The Multiview Extended Video with Activities (MEVA) dataset consists video data of human activity, both scripted and unscripted, collected with roughly 100 actors over several weeks. The data was collected with 29 cameras with overlapping and non-overlapping fields of view. The current release consists of about 328 hours (516GB, 4259 clips) of video data, as well as 4.6 hours (26GB) of UAV data. Other data includes GPS tracks of actors, camera models, and a site map. We have also released annotations for roughly 184 hours of data. Further updates are planned.

NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP-CMIP6)

The NEX-GDDP-CMIP6 dataset is comprised of global downscaled climate scenarios derived from the General Circulation Model (GCM) runs conducted under the Coupled Model Intercomparison Project Phase 6 (CMIP6) and across two of the four "Tier 1" greenhouse gas emissions scenarios known as Shared Socioeconomic Pathways (SSPs). The CMIP6 GCM runs were developed in support of the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6). This dataset includes downscaled projections from ScenarioMIP model runs for which daily scenarios were produced and distributed through the Earth System Grid Federation. The purpose of this dataset is to provide a set of global, high resolution, bias-corrected climate change projections that can be used to evaluate climate change impacts on processes that are sensitive to finer-scale climate gradients and the effects of local topography on climate conditions.

NASA High Energy Astrophysics Mission Data

NASA data for high energy astrophysics (generally x-ray and gamma-ray domains) is made available here by the High Energy Astrophysics Science Archive Research Center. The HEASARC hosts the full data archives of over 30 different missions spanning 50 years. The data archive for each mission will contain a range of data types from spacecraft housekeeping and raw photon event list data up to high level science-ready products such as images, light curves (time series), and energy spectra.

This is a relatively modest total data volume but contains significant complexity and heterogeneity among the different missions. Data provided here are stored in the Flexible Image Transport System (FITS) format common in astronomy. Higher level products are further defined to be consistent between missions following data model standards agreed by the community and maintained by the HEASARC. Analysis of these data may require software also provided by HEASARC, the HEASoft package, consisting of tools generic to all FITS data, generic to all HEASARC-compliant data, and/or specific to individual missions as appropriate. Some missions provide standard science-ready data products, while others provide low-level data types and software to generate science-ready products from them. See the links for each mission for more information on how to use the data.

The HEASARC Website also has archive browsing tools where you can query for observations corresponding to temporal and spatial constraints among others. These tools will ultimately point to files located on the archive by giving a URL beginning with the path https://heasarc.gsfc.nasa.gov/FTP/. The data that are provided in the ODR follow the same structure, so when our tools give an https access URL, a user can simply swap in s3://nasa-heasarc/ for the first part of that URL and get a cloud URI. Note also that some selections have been made to what has been copied to the ODR, while the HEASARC archive itself remains the definitive and legacy source for the complete datasets.

The HEASARC also provides user support, both generic and mission-specific, through a set of email helpdesks available through the website's Feedback page.

NASA Legacy Archive for Microwave Background Data Analysis (LAMBDA)

NASA data for cosmic microwave background (CMB) analysis is made available here by the Legacy Archive for Microwave Background Data Analysis (LAMBDA), which is a part of NASA's High Energy Astrophysics Science Archive Research Center (HEASARC). LAMBDA hosts the data archives of over 30 different CMB missions spanning 30+ years. The data archive for each mission may contain a range of data types from low-level time-ordered data to high level science-ready products such as sky maps and angular power spectra. Also provided in consistent formats are a variety of full sky maps in complementary wavebands that are useful for CMB analysis, such as maps of radio synchrotron or infrared dust emission.

Data provided here are normally stored in the generic Flexible Image Transport System (FITS) format common in astronomy. Sky maps are generally provided in the Hierarchical Equal-Area isoLatitude Pixelization (HEALPix) scheme, though the older COsmic Background Explorer (COBE) data is stored in COBE Quadrilateralizated Spherical Cube pixelization (CSC). See the links for each mission for more information on the data and how to use it. LAMBDA also provides user support, both generic and mission-specific, through a helpdesk available through the website's Feedback page."

NASA SOHO/LASCO2 comet challenge on AWS

The SOHO/LASCO data set (prepared for the challenge hosted in Topcoder) provided here comes from the instrument’s C2 telescope and comprises approximately 36,000 images spread across 2,950 comet observations. The human eye is a very sensitive tool and it is the only tool currently used to reliably detect new comets in SOHO data - particularly comets that are very faint and embedded in the instrument background noise. Bright comets can be easily detected in the LASCO data by relatively simple automated algorithms, but the majority of comets observed by the instrument are extremely faint, noise-level observations. Comets in SOHO/LASCO data are dynamic and morphologically diverse objects, and thus computationally highly complex to detect and track.

NASA Space Biology Open Science Data Repository (OSDR)

NASA’s Space Biology Open Science Data Repository (OSDR) introduces a one-stop site where users can explore and contribute a variety of NASA open science biological data. This site consolidates data from the Ames Life Sciences Data Archive (ALSDA) and GeneLab and includes information about the broader NASA Open Science and Open Data initiatives, all at one centralized location. Our mission is to maximize the utilization of the valuable biological research resources and enable new discoveries.

OSDR introduces access to data generated from spaceflight and space relevant experiments that explore the biological response of terrestrial biology through the AWS Open Data Registry page. The ALSDA is the official repository of non-human science data spanning a broad range of biological levels involving data from tissues, organs, whole organisms, physiology, and behavior. GeneLab is an open science repository hosting multiple types of ‘omics including transcriptomics, metagenomics, epigenomics, proteomics, and metabolomics data. Studies comprise of data from model organisms including microbes, plants, fruit flies, rodents, as well as human cell culture, ground study, and commercial astronaut data. In addition, the data repository includes metadata searches across several external omics database.

NEXRAD ARCO - Analysis-Ready Cloud-Optimized Weather Radar

NEXRAD Level II weather radar data converted to FAIR-compliant, analysis-ready cloud-optimized (ARCO) format using Zarr v3 and Icechunk V2. Hierarchically organized by Volume Coverage Pattern (VCP) and sweep, enabling instant time-series access to polarimetric variables (DBZH, ZDR, RHOHV, PHIDP, VELOCITY) without downloading individual files. Currently includes KLOT (Chicago, IL) with continuous updates.

NIFS Large Helical Device (LHD) Experiment

The Large Helical Device (LHD), owned and operated by the National Institute for Fusion Science (NIFS), is one of the world's largest plasma confinement device which employs a heliotron magnetic configuration generated by the superconducting coils. The objectives are to conduct academic research on the confinement of steady-state, high-temperature, high-density plasmas, core plasma physics, and fusion reactor engineering, which are necessary to develop future fusion reactors. All the archived data of the LHD plasma diagnostics are available since the beginning of the LHD experiment, started on 31st of March, 1998.

NOAA - hourly position, current, and sea surface temperature from drifters

This dataset includes hourly sea surface temperature and current data collected by satellite-tracked surface drifting buoys ("drifters") of the NOAA Global Drifter Program. The Drifter Data Assembly Center (DAC) at NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML) has applied quality control procedures and processing to edit these observational data and obtain estimates at regular hourly intervals. The data include positions (latitude and longitude), sea surface temperatures (total, diurnal, and non-diurnal components) and velocities (eastward, northward) with accompanying uncertainty estimates. Metadata include identification numbers, experiment number, start location and time, end location and time, drogue loss date, death code, manufacturer, and drifter type.

Please note that data from the Global Drifter Program are also available at 6-hourly intervals but derived via alternative methods. The 6-hourly dataset goes back further in time (1979) and may be more appropriate for studies of long-term, low frequency patterns of the oceanic circulation. Yet, the 6-hourly dataset does not resolve fully high-frequency processes such as tides and inertial oscillations as well as sea surface temperature diurnal variability.

[CITING NOAA - hourly position, current, and sea surface temperature from drifters data. Citation for this dataset should include the following information below.]
Elipot, Shane; Sykulski, Adam; Lumpkin, Rick; Centurioni, Luca; Pazos, Mayra (2022). Hourly location, current velocity, and temperature collected from Global Drifter Program drifters world-wide. [indicate subset used]. NOAA National Centers for Environmental Information. Dataset. https://doi.org/10.25921/x46c-3620.

NOAA Emergency Response Imagery

In order to support NOAA's homeland security and emergency response requirements, the National Geodetic Survey Remote Sensing Division (NGS/RSD) has the capability to acquire and rapidly disseminate a variety of spatially-referenced datasets to federal, state, and local government agencies, as well as the general public. Remote sensing technologies used for these projects have included lidar, high-resolution digital cameras, a film-based RC-30 aerial camera system, and hyperspectral imagers. Examples of rapid response initiatives include acquiring high resolution images with the Emerge/Applanix Digital Sensor System (DSS).

NOAA GFS - dynamical.org Icechunk Zarr

The Global Forecast System (GFS) is a National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) weather forecast model that generates data for dozens of atmospheric and land-soil variables, including temperatures, winds, precipitation, soil moisture, and atmospheric ozone concentration. The system couples four separate models (atmosphere, ocean model, land/soil model, and sea ice) that work together to depict weather conditions.

These datasets have been translated to cloud-optimized Icechunk Zarr format by dynamical.org.

NOAA GFS analysis - Weather analysis from the Global Forecast System (GFS) operated by NOAA NWS NCEP.
NOAA GFS forecast - Weather forecasts from the Global Forecast System (GFS) operated by NOAA NWS NCEP.

NOAA Global Ensemble Forecast System (GEFS) Re-forecast

NOAA has generated a multi-decadal reanalysis and reforecast data set to accompany the next-generation version of its ensemble prediction system, the Global Ensemble Forecast System, version 12 (GEFSv12). Accompanying the real-time forecasts are “reforecasts” of the weather, that is, retrospective forecasts spanning the period 2000-2019. These reforecasts are not as numerous as the real-time data; they were generated only once per day, from 00 UTC initial conditions, and only 5 members were provided, with the following exception. Once weekly, an 11-member reforecast was generated, and these extend in lead time to +35 days.

NOAA IOOS MARACOOS Regional Ocean Modeling System (ROMS) "Doppio" Data Assimilative Reanalysis

This dataset, identified as Doppio Analysis Version 3 Release 3 (DopAnV3R3-ini2007) initialized January 2007, comprises outputs from a Regional Ocean Modeling System (ROMS) data assimilative reanalysis of ocean circulation in the Mid-Atlantic Bight and Gulf of Maine for 2007-2024.

A multi-year reanalysis (2007-2024) of circulation in the coastal ocean and adjacent deep sea of the northeast U.S. continental shelf has been computed using the Regional Ocean Modeling System (ROMS) with four-dimensional variational (4D-Var) data assimilation (DA) of observations from satellites, land-based ocean surface current measuring radar, and all available in situ observations from the MARACOOS (maracoos.org) and NERACOOS (neracoos.org) regional associations of the U.S. Integrated Ocean Observing System (IOOS). The reanalysis downscales open boundary information from the Copernicus Marine Service global analysis. The dynamic model is forced by regional meteorological analyses, observed daily river discharges, and harmonic tides that augment the open boundary conditions. The analysis covers the period 2-Jan-2007 to 31-Dec-2024 on a 7-km horizontal grid with 40 vertical terrain-following s-coordinate levels. Ocean state variables computed are sea level, velocity, temperature, and salinity. Air-sea fluxes of heat and momentum are included.

The ROMS 4D-Var DA configuration and its performance in comparison to observations (both assimilated and independent) are described by Wilkin et al. (2022). The underlying ROMS ocean circulation model configuration is described by López et al (2020). Wilkin et al. (2022) also compare this analysis to Copernicus and the U.S. Navy Global Ocean Forecast System (GOFS) HYCOM model.

All ROMS model outputs here are in netCDF format and the data and metadata follow CF-1.4 conventions for the description of coordinates and variables. The reanalysis results are provided on the model’s native 3-D grid in terrain-following coordinates aggregated into three collections with individual files containing 3 days of model output.

His: 1-hourly snapshots of the ocean state “history” (sea level and 3-D velocity, temperature and salinity) for 02-Jan-2007 01:00 through 31-Dec-2024 00:00
Flx: 1-hourly snapshots of surface air-sea flux forcing (wind stress and net heat flux) from the posterior reanalysis for 02-Jan-2007 01:00 through 31-Dec-2024 00:00
Avg: Daily averages for 02-Jan-2007 12:00 through 30-Dec-2024 12:00

In addition to access here in the Registry of Open Data in AWS, Rutgers University operates a THREDDS (Thematic Real-time Environmental Distributed Data Services) web service at https://tds.marine.rutgers.edu/thredds/roms/doppio/catalog.html that supports geospatial and temporal sub-setting. Access protocols supported are OPeNDAP, WMS and netCDF subsetting. That collection includes:
monthly averages for 17-Jan-2007 through 15-Dec-2021
yearly averages for 2007 through 2021
monthly ensemble averages

The observations that were assimilated, after quality control and the formation of super-observations as described by Wilkin et al. (2022), can be accessed via an ERDDAP service at https://tds.marine.rutgers.edu/erddap/tabledap/DOPPIO_REANALYSIS_Ver3_OBS.graph. The dataset includes observation type and provenance fields (documented in the ERDDAP metadata) that allow sub-selection by data types (temperate, salinity, velocity, etc.) or observing platform (e.g., Jason altimeter satellite, AVHRR SST, IOOS gliders, etc.). The ERDDAP catalog includes outputs of the DA system in observation space; namely, prior and posterior model errors, and the Copernicus and GOFS values interpolated to the observation position. An observation scale field flags data that were rejected by 4D-Var QC.

NOAA Multi-Radar/Multi-Sensor System (MRMS)

The MRMS system was developed to produce severe weather, transportation, and precipitation products for improved decision-making capability to improve hazardous weather forecasts and warnings, along with hydrology, aviation, and numerical weather prediction.

MRMS is a system with fully-automated algorithms that quickly and intelligently integrate data streams from multiple radars, surface and upper air observations, lightning detection systems, satellite observations, and forecast models. Numerous two-dimensional multiple-sensor products offer assistance for hail, wind, tornado, quantitative precipitation estimations, convection, icing, and turbulence diagnosis.

MRMS is being used to develop and test new Federal Aviation Administration (FAA) NextGen products in addition to advancing techniques in quality control, icing detection, and turbulence in collaboration with the National Center for Atmospheric Research, the University Corporation for Atmospheric Research, and Lincoln Laboratories.

MRMS was deployed operationally in 2014 at the National Center for Environmental Prediction (NCEP). All of the 100+ products it produces are available via NCEP to all of the WFOs, RFCs, CWSUs and NCEP service centers. In addition, the MRMS product suite is publicly available to any other entity who wishes to access and use the data. Other federal agencies that use MRMS include FEMA, DOD, FAA, and USDA.

MRMS is the proposed operational version of the WDSS-II and NMQ research systems.

The MRMS system was jointly developed in cooperation with the Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO) (formerly CIMMS), and the University of Oklahoma retains the right to commercially license the software. Several leading weather information companies have previously licensed the MRMS system from the University of Oklahoma for commercial use, although the software is available for government at no cost.

NOAA North American Multi-Model Ensemble (NMME)

The North American Multi-Model Ensemble (NMME) is an experimental multi-model seasonal forecasting system consisting of coupled models from US modeling centers including NOAA/NCEP, NOAA/GFDL, NCAR, NASA, and Canada's ECCC.

The need for the development of NMME operational predictive capability was recommended in US National Academies report "Assessment of Intraseasonal to Interannual Climate Prediction and Predictability". Indeed, the national effort is required to meet the specific tailored regional prediction and decision support needs of a large community. The multi-model ensemble approach has proven extremely effective at quantifying prediction uncertainty due to uncertainty in model formulation, and has proven to produce better prediction quality (on average) than any single model ensemble. This multi-model approach is the basis for several international collaborative prediction research efforts, including an operational European system. There are numerous examples of how this multi-model ensemble approach yields superior forecasts compared to any single model.

NapierOne Mixed File Dataset

NapierOne is a modern cybersecurity mixed file data set, primarily aimed at, but not limited to, ransomware detection and forensic analysis. The dataset contains over 500,000 distinct files, representing 44 distinct popular file types. It was designed to address the known deficiency in research reproducibility and improve consistency by facilitating research replication and repeatability. The data set was inspired by the Govdocs1 data set and it is intended that ‘NapierOne’ be used as a complement to this original data set. An investigation was performed with the goal of determining the common files types currently in use. No specific research was found that explicitly provided this information, so an alternative consensus approach was employed. This involved combining the findings from multiple sources of file type usage into an overall ranked list. After which 5,000 real-world example files were gathered, and a specific data subset was created, for each of the common file types identified. In some circumstances, multiple data subsets were created for a specific file type, each subset representing a specific characteristic for that file type. For example, there are multiple data subsets for the ZIP file type with each subset containing examples of a specific compression method. Ransomware execution tends to produce files that have high entropy, so examples of file types that naturally have this attribute are also present. The resulting entire data set comprises of more than 90 separate data subsets divided between 44 distinct file types, resulting in over 500,000 unique files in total. Currently, the data set contains examples of the following file types APK, BIN, BMP, CSS, CSV, DOC, DOCX, DWG, ELF, EPS,EPUB, EXE, GIF, GZIP, HTML, ICS, JS, JPG, JSON, MKV, MP3, MP4, ODS, OXPS, PDF, PNG, PPT, PPTX, PS1, RAR, SVG, TAR, TIF, TXT, WEBP, XLS, XLSX, XML, ZIP, ZLIB, 7Zip

National Cancer Institute Imaging Data Commons (IDC) Collections

Imaging Data Commons (IDC) is a repository within the Cancer Research Data Commons (CRDC) that manages imaging data and enables its integration with the other components of CRDC. IDC hosts a growing number of imaging collections that are contributed by either funded US National Cancer Institute (NCI) data collection activities, or by the individual researchers.Image data hosted by IDC is stored in DICOM format.

National Climate Database (NCDB)

The National Climate Database (NCDB) seeks to be the definitive source of climate data for energy applications. The goal of the NCDB is to provide unbiased high temporal and spatial resolution climate data needed for renewable energy modeling. The NCDB seeks to maintain the inherent relationship between the various parameters that are needed to model solar, wind, hydrology and load and provide data for multiple important climate scenarios.

National Herbarium of NSW

The National Herbarium of New South Wales is one of the most significant scientific, cultural and historical botanical resources in the Southern hemisphere. The 1.43 million preserved plant specimens have been captured as high-resolution images and the biodiversity metadata associated with each of the images captured in digital form. Botanical specimens date from year 1770 to today, and form voucher collections that document the distribution and diversity of the world's flora through time, particularly that of NSW, Austalia and the Pacific.The data is used in biodiversity assessment, systematic botanical research, ecosystem conservation and policy development. The data is used by scientists, students and the public.

ONT Methylation Benchmarking Datasets

ONT Methylation Benchmarking Datasets are generated to benchmark existing methylation-calling tools on the Oxford Nanopore sequencing platform using their recent R10.4.1 flowcell chemistry. It spans a diverse range of species, including bacteria (E. coli, H. pylori J99, H. pylori 26695, A. variabilis, T. denticola), plants (Rice, Arabidopsis), and mammals (mouse, human).In addition, the dataset includes EMSeq data for E. coli, plant, and mouse samples, which can serve as ground truth for methylation studies. It also provides unmethylated whole-genome amplified (WGA) DNA for H. pylori 26695 and a dam- dcm- double mutant (DM) of E. coli that lacks canonical 5mC and 6mA methylation. These variants, together with their wild-type counterparts, offer value for both training and benchmarking DNA methylation calling models.

OPERA Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 product (Version 1)

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Annual from Harmonized Landsat Sentinel-2 (HLS) product Version 1 summarizes the DIST-ALERT data product into an annual vegetation disturbance data product. Vegetation disturbance is mapped when there is an indicated decrease in vegetation cover within an HLS Version 2 pixel. The product also provides auxiliary generic disturbance information as determined from the variations of the reflectance through the DIST-ALERT scenes to provide information about more general disturbance trends. The DIST-ANN product tracks changes at the annual scale, aggregating changes identified in the DIST-ALERT product. Only confirmed disturbances from the associated year are reported together with the date of initial disturbance. As confirmed disturbances are determined using subsequent cloud-free observations to determine if the loss detections persist, the required number of HLS scenes depends on visibility of the target. Due to this dependency, summarizing the DIST-ALERT in the DIST-ANN product will have some latency contingent on the algorithmic calibration and is detailed in the Algorithm Theoretical Basis Document (ATBD).The OPERA_L3_DIST-ANN-HLS (or DIST-ANN) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate COG. There are 21 layers contained within the DIST-ANN product: vegetation disturbance status, historical vegetation cover indicator, maximum vegetation cover indicator, maximum vegetation anomaly value, vegetation disturbance confidence layer, date of initial vegetation disturbance, number of detected vegetation loss anomalies, vegetation disturbance duration, date of last observation assessed for vegetation disturbance, and several generic disturbance layers. Each product layer is gridded to the same resolution and tiling system as HLS V2: 30 meter (m) and Military Grid Reference System (MGRS). See the Product Specification Document (PSD) for a more detailed description of the individual layers provided in the DIST-ANN product. The OPERA_L3_DIST-ANN-HLS product contains modified Copernicus Sentinel data (2020-2025).Known Issues

Additional usage constraints are provided under Section 5 of the Algorithm Theoretical Basis Document (ATBD). Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 Static Layers validated product (Version 1)

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) Static Layers (RTC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Radiometric Terrain Corrected (RTC) SAR Backscatter from Sentinel-1 (S1) (RTC-S1) validated product. Due to the S1 mission’s narrow orbital tube, radar-geometry layers such as incidence angle, local incidence angle, number of looks, and RTC Area Normalization Factor (ANF) vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA RTC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static-layers generation algorithm. Static layers are provided as single-band cloud-optimized GeoTIFF (COG) files, with map grid matching RTC-S1 products with the same burst ID. The standard OPERA RTC-S1 product is derived from the original Copernicus Sentinel-1 (S1) interferometric wide (IW) single-look complex (SLC) data, provided by the European Space Agency, with a temporal sampling coincident with the availability of Sentinel-1A and Sentinel-1B SLC data. The OPERA RTC-S1-STATIC and RTC-S1 products are provided at a near global scope (land masses excluding Antarctica). The RTC-S1 products are available in the associated OPERA Radiometric Terrain Corrected SAR Backscatter from Sentinel-1 validated product (Version 1) dataset. Read our doc on how to get AWS Credentials to retrieve this data: https://cumulus.asf.alaska.edu/s3credentialsREADME

Open City Model (OCM)

Open City Model is an initiative to provide cityGML data for all the buildings in the United States. By using other open datasets in conjunction with our own code and algorithms it is our goal to provide 3D geometries for every US building.

Open Human Genome Library

The Open Human Genome Library (OpenHGL) is a collection of high-quality de novo human assemblies that are publicly available in genomic databases (e.g. NCBI and CNCB) or from individual research papers. It provides consistent naming and uniform formats across datasets, supporting efficient subsequence retrieval and approximate string search.

Open VLF: Scientific Open Data Initiative for CRAAM's SAVNET and AWESOME VLF Data.

This platform is maintained by CRAAM (Mackenzie Radio Astronomy and Astrophysics Center), a research center operated by UPM (Mackenzie Presbyterian University) and INPE (National Institute for Space Research), to provide public and free access for researchers, students, and the interested public to VLF (Very Low Frequency) data from CRAAM's antenna systems. Amazon AWS supports all data stored through the AWS Open Data Program. Very Low Frequency (VLF) signals can be used for navigation services, communication with submarines, and are a powerful tool to study the low-altitude Earth's ionosphere, atmospheric and geophysics phenomena, space weather, magnetic field, and solar flares. Here, we provide historical and updated VLF data from two of CRAAM’s antenna systems, the SAVNET and AWESOME systems. The SAVNET consists of a network of 11 VLF receiving/tracking stations located in Latin America and Antarctica. Five stations are in Brazil, three in Peru, one in Argentina, one in Mexico, and one in the Brazilian Antarctic Research Station Comandante Ferraz. The CRAAM AWESOME systems include two antennas in Brazil and Antarctica. Data since 2002 from both systems are available, with occasional gaps during periods when data collection is interrupted. Data provided here is stored in different formats (.mat for AWESOME antenna system and .fits for SAVNET system). For documentation and details, please visit the project website Open VLF.

OpenProteinSet

Multiple sequence alignments (MSAs) for 140,000 unique Protein Data Bank (PDB) chains and 16,000,000 UniClust30 clusters. Template hits are also provided for the PDB chains and 270,000 UniClust30 clusters chosen for maximal diversity and MSA depth. MSAs were generated with HHBlits (-n3) and JackHMMER against MGnify, BFD, UniRef90, and UniClust30 while templates were identified from PDB70 with HHSearch, all according to procedures outlined in the supplement to the AlphaFold 2 Nature paper, Jumper et al. 2021. We expect the database to be broadly useful to structural biologists training or validating deep learning models for protein structure prediction and related tasks.

OpenRoboCare Multi-Modal Expert Demonstration Dataset for Robot-Assisted Caregiving

A comprehensive multimodal dataset capturing real-world caregiving routines from 21 occupational therapists performing 15 daily caregiving tasks. The dataset includes synchronized RGB-D video, tactile sensing, eye-gaze tracking, pose annotations, and action labels across 315 sessions totaling 19.8 hours of expert demonstrations. Data modalities include anonymized RGB images, depth maps, 44-sensor tactile readings, 2D/3D pose tracking, temporal action annotations, and first/third-person videos, enabling research in robot learning from demonstration, multimodal perception, and safe human-robot interaction for caregiving applications.

PD12M

PD12M is a collection of 12.4 million CC0/PD image-caption pairs for the purpose of training generative image models.

Pohang Canal Dataset: A Multimodal Maritime Dataset for Autonomous Navigation in Restricted Waters

This dataset presents a multi-modal maritime dataset acquired in restricted waters in Pohang, South Korea. The sensor suite is composed of three LiDARs (one 64-channel LiDAR and two 32-channel LiDARs), a marine radar, two visual cameras used as a stereo camera, an infrared camera, an omnidirectional camera with 6 directions, an AHRS, and a GPS with RTK. The dataset includes the sensor calibration parameters and SLAM-based baseline trajectory. It was acquired while navigating a 7.5 km route that includes a narrow canal area, inner and outer port areas, and a near-coastal area. The aim of this dataset is to facilitate research on autonomous surface vehicles.

ProteinGym

ProteinGym is a benchmark suite for assessing the performance of protein fitness prediction and design models. It comprises a large curated collection of 200+ high-throughput experimental assays (~3M mutated sequences), as well as clinical annotations from experts about the pathogenicity of mutants in over 3k human genes.

QIIME 2 Tutorial Data

QIIME 2 (pronounced “chime two”) is a microbiome multi-omics bioinformatics and data science platform that is trusted, free, open source, extensible, and community developed and supported.

Rain over Africa

The Rain over Africa (RoA) dataset consists of spaceborn estimates of precipitation of Rain over Africa using only geostationary imagery and obtained through a convolutional and quantile regression neural network. The dataset also contains some uncertainty estimates.

SPaRCNet data:Seizures, Rhythmic and Periodic Patterns in ICU Electroencephalography

The IIIC dataset includes 50,697 labeled EEG samples from 2,711 patients' and 6,095 EEGs that were annotated by physician experts from 18 institutions. These samples were used to train SPaRCNet (Seizures, Periodic and Rhythmic Continuum patterns Deep Neural Network), a computer program that classifies IIIC events with an accuracy matching clinical experts.

STOIC2021 Training

The STOIC project collected Computed Tomography (CT) images of 10,735 individuals suspected of being infected with SARS-COV-2 during the first wave of the pandemic in France, from March to April 2020. For each patient in the training set, the dataset contains binary labels for COVID-19 presence, based on RT-PCR test results, and COVID-19 severity, defined as intubation or death within one month from the acquisition of the CT scan. This S3 bucket contains the training sample of the STOIC dataset as used in the STOIC2021 challenge on grand-challenge.org.

Sentinel-1

Sentinel-1 is a pair of European radar imaging (SAR) satellites launched in 2014 and 2016. Its 6 days revisit cycle and ability to observe through clouds makes it perfect for sea and land monitoring, emergency response due to environmental disasters, and economic applications. This dataset represents the global Sentinel-1 GRD archive, from beginning to the present, converted to cloud-optimized GeoTIFF format.

Sentinel-2 ACOLITE-DSF Aquatic Reflectance for the Conterminous United States

Aquatic reflectance produced with the dark spectrum fitting (DSF) algorithm as implemented in the Atmospheric Correction for OLI “lite” (ACOLITE) software (version 20221114.0). Aquatic reflectance is defined here as unitless water-leaving radiance reflectance and represents the ratio of water-leaving radiance (units of watts per square meter per steradian per nanometer) to downwelling irradiance (units of watts per square meter per nanometer) multiplied by pi.

Software Heritage Graph Dataset

Software Heritage is the largest existing public archive of software source code and accompanying development history. The Software Heritage Graph Dataset is a fully deduplicated Merkle DAG representation of the Software Heritage archive.The dataset links together file content identifiers, source code directories, Version Control System (VCS) commits tracking evolution over time, up to the full states of VCS repositories as observed by Software Heritage during periodic crawls. The dataset’s contents come from major development forges (including GitHub and GitLab), FOSS distributions (e.g., Debian), and language-specific package managers (e.g., PyPI). Crawling information is also included, providing timestamps about when and where all archived source code artifacts have been observed in the wild. Author and committer information is anonymized.

Sophos/ReversingLabs 20 Million malware detection dataset

A dataset intended to support research on machine learning techniques for detecting malware. It includes metadata and EMBER-v2 features for approximately 10 million benign and 10 million malicious Portable Executable files, with disarmed but otherwise complete files for all malware samples. All samples are labeled using Sophos in-house labeling methods, have features extracted using the EMBER-v2 feature set, well as metadata obtained via the pefile python library, detection counts obtained via ReversingLabs telemetry, and additional behavioral tags that indicate the rough behavior of the samples.

State of Colorado Imagery

The State of Colorado has gathered public historical imagery ranging from 2005 to 2021.

TESS-GAIA Light Curve (TGLC)

TESS-Gaia Light Curve (TGLC) is a PSF-based TESS full-frame image (FFI) light curve product. Using Gaia DR3 as priors, the team forward models the FFIs with the effective point spread function to remove contamination from nearby stars. The resulting light curves show a photometric precision closely tracking the pre-launch prediction of the noise level: TGLC's photometric precision consistently reaches ≲2% at 16th TESS magnitude even in crowded fields, demonstrating excellent decontamination and deblending power.

The Human Microbiome Project

The NIH-funded Human Microbiome Project (HMP) is a collaborative effort of over 300 scientists from more than 80 organizations to comprehensively characterize the microbial communities inhabiting the human body and elucidate their role in human health and disease. To accomplish this task, microbial community samples were isolated from a cohort of 300 healthy adult human subjects at 18 specific sites within five regions of the body (oral cavity, airways, urogenital track, skin, and gut). Targeted sequencing of the 16S bacterial marker gene and/or whole metagenome shotgun sequencing was performed for thousands of these samples. In addition, whole genome sequences were generated for isolate strains collected from human body sites to act as reference organisms for analysis. Finally, 16S marker and whole metagenome sequencing was also done on additional samples from people suffering from several disease conditions.

Transcriptomic MIT Licensed data and models

This dataset contains a transcriptomics biological data and models. The models embed transcriptomic data and facilitate transcriptomic analysis. The data is sourced and curated by a team of experts at Biohub and is made available as part of these datasets only when it is not publicly accessible or requires transformations to support model training.

UCSF Renal Mass CT Dataset

This dataset provides a set of 831 3D Multiphase CT exams of renal masses, registered across phases with annotations identifying the masses

Variant Effect Predictor (VEP) and the Loss-Of-Function Transcript Effect Estimator (LOFTEE) Plugin

VEP determines the effect of genetic variants (SNPs, insertions, deletions, CNVs or structural variants) on genes, transcripts, and protein sequence, as well as regulatory regions. The European Bioinformatics Institute produces the VEP tool/db and releases updates every 1 - 6 months. The latest release contains 267 genomes from 232 species containing 5567663 protein coding genes. This dataset hosts the last 5 releases for human, rat, and zebrafish. Also, it hosts the required reference files for the Loss-Of-Function Transcript Effect Estimator (LOFTEE) plugin as it is commonly used with VEP.

Vesuvius Challenge - CT Scans of Herculaneum Papyri

This dataset contains reconstructed micro-CT volumes of carbonized Herculaneum papyri produced as part of the Vesuvius Challenge. The scanned scroll library survived the eruption of Mount Vesuvius in AD 79. It is the only intact library known to have survived from antiquity. The volumetric reconstructions are distributed as OME-Zarr multiscale datasets to support research in virtual unwrapping, segmentation, and text recovery of ancient scrolls. Deciphering these scrolls could forever change our understanding of Roman history.

WIS2 Global Cache on AWS

Global real-time Earth system data deemed by the World Meteorological Organisation (WMO) as essential for provision of services for the protection of life and property and for the well-being of all nations. Data is sourced from all WMO Member countries / territories and retained for 24-hours. Met Office and NOAA operate this Global Cache service curating and publishing the dataset on behalf of WMO.

Wind AI Bench

This data lake contains multiple datasets related to fundamental problems in wind energy research. This includes data for wind plant power production for various layouts/wind flow scenarios, data for two- and three-dimensional flow around different wind turbine airfoils/blades, wind turbine noise production, among others. The purpose of these datasets is to establish a standard benchmark against which new AI/ML methods can be tested, compared, and deployed. Details regarding the generation and formatting of the data for each dataset is included in the metadata as well as example notebooks and documentation that show how to access the data for ML modeling.

run_dbcan CAZyme and CGC annotation database on AWS

Database for use with run_dbcan (CAZyme and CGC annotation), including CAZyme, Transporter, Transcription factor, Signaling Transduction Protein, Sulfatase, Peptidase, and Polysaccharide utilization Loci.

1940 Census Population Schedules, Enumeration District Maps, and Enumeration District Descriptions

The 1940 Census population schedules were created by the Bureau of the Census in an attempt to enumerate every person living in the United States on April 1, 1940, although some persons were missed. The 1940 census population schedules were digitized by the National Archives and Records Administration (NARA) and released publicly on April 2, 2012. The 1940 Census enumeration district maps contain maps of counties, cities, and other minor civil divisions that show enumeration districts, census tracts, and related boundaries and numbers used for each census. The coverage is nation wide and includes territorial areas. The 1940 Census enumeration district descriptions contain written descriptions of census districts, subdivisions, and enumeration districts.

1950 Census Population Schedules, Enumeration District Maps, and Enumeration District Descriptions

The 1950 Census population schedules were created by the Bureau of the Census in an attempt to enumerate every person living in the United States on April 1, 1950, although some persons were missed. The 1950 census population schedules were digitized by the National Archives and Records Administration (NARA) and released publicly on April 1, 2022. The 1950 Census enumeration district maps contain maps of counties, cities, and other minor civil divisions that show enumeration districts, census tracts, and related boundaries and numbers used for each census. The coverage is nation wide and includes territorial areas. The 1950 Census enumeration district descriptions contain written descriptions of census districts, subdivisions, and enumeration districts.

2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File

The 2010 Census Production Settings Redistricting Data (P.L. 94-171) Demonstration Noisy Measurement File (2023-04-03) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9 , and implemented in https://github.com/uscensusbureau/DAS_2020_Redistricting_Production_Code). The NMF was produced using the official “production settings,” the final set of algorithmic parameters and privacy-loss budget allocations, that were used to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File and the 2020 Census Demographic and Housing Characteristics File.

The NMF consists of the full set of privacy-protected statistical queries (counts of individuals or housing units with particular combinations of characteristics) of confidential 2010 Census data relating to the redistricting data portion of the 2010 Demonstration Data Products Suite – Redistricting and Demographic and Housing Characteristics File – Production Settings (2023-04-03). These statistical queries, called “noisy measurements” were produced under the zero-Concentrated Differential Privacy framework (Bun, M. and Steinke, T [2016] https://arxiv.org/abs/1605.02065; see also Dwork C. and Roth, A. [2014] https://www.cis.upenn.edu/~aaroth/Papers/privacybook.pdf) implemented via the discrete Gaussian mechanism (Cannone C., et al., [2023] https://arxiv.org/abs/2004.00010), which added positive or negative integer-valued noise to each of the resulting counts. The noisy measurements are an intermediate stage of the TDA prior to the post-processing the TDA then performs to ensure internal and hierarchical consistency within the resulting tables. The Census Bureau has released these 2010 Census demonstration data to enable data users to evaluate the expected impact of disclosure avoidance variability on 2020 Census data. The 2010 Census Production Settings Redistricting Data (P.L.94-171) Demonstration Noisy Measurement File (2023-04-03) has been cleared for public dissemination by the Census Bureau Disclosure Review Board (CBDRB-FY22-DSEP-004).

The data includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2010 Census Edited File (CEF), which includes confidential data initially collected in the 2010 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) (https://www2.census.gov/programs-surveys/decennial/2020/program-management/data-product-planning/2010-demonstration-data-products/04-Demonstration_Data_Products_Suite/2023-04-03/). As these 2010 Census demonstration data are intended to support study of the design and expected impacts of the 2020 Disclosure Avoidance System, the 2010 CEF records were pre-processed before application of the zCDP framework. This pre-processing converted the 2010 CEF records into the input-file format, response codes, and tabulation categories used for the 2020 Census, which differ in substantive ways from the format, response codes, and tabulation categories originally used for the 2010 Census.

The NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints—information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2010 Census Production Settings Privacy-Protected Microdata File - Redistricting (P.L. 94-171) and Demographic and Housing Characteristics File (2023-04-03) —are provided.

2020 Census Redistricting Data (P.L. 94-171) Noisy Measurement File

The 2020 Census Redistricting Data (P.L. 94-171) Noisy Measurement File (NMF) is an intermediate output of the 2020 Census Disclosure Avoidance System (DAS) TopDown Algorithm (TDA) (as described in Abowd, J. et al [2022] https://doi.org/10.1162/99608f92.529e3cb9, and implemented in the DAS 2020 Redistricting Production Code). The NMF was generated using the Census Bureau's implementation of the Discrete Gaussian Mechanism, calibrated to satisfy zero-Concentrated Differential Privacy with bounded neighbors.

The NMF values, called noisy measurements are the output of applying the Discrete Gaussian Mechanism to counts from the 2020 Census Edited File (CEF). They are generally inconsistent with one another (for example, in a county composed of two tracts, the noisy measurement for the county's total population may not equal the sum of the noisy measurements of the two tracts' total population), and frequently negative (especially when the population being measured was small), but are integer-valued. The NMF was later post-processed as part of the DAS code to take the form of microdata and to satisfy various constraints. The NMF documented here contains both the noisy measurements themselves as well as the data needed to represent the DAS constraints; thus, the NMF could be used to reproduce the steps taken by the DAS code to produce microdata from the noisy measurements by applying the production code base.

The 2020 Census Redistricting Data (P.L. 94-171) Noisy Measurement File includes zero-Concentrated Differentially Private (zCDP) (Bun, M. and Steinke, T [2016]) noisy measurements, implemented via the discrete Gaussian mechanism. These are estimated counts of individuals and housing units included in the 2020 Census Edited File (CEF), which includes confidential data initially collected in the 2020 Census of Population and Housing. The noisy measurements included in this file were subsequently post-processed by the TopDown Algorithm (TDA) to produce the 2020 Census Redistricting Data (P.L. 94-171) Summary File.

The NMF provides estimates of counts of persons in the CEF by various characteristics and combinations of characteristics including their reported race and ethnicity, whether they were of voting age, whether they resided in a housing unit or one of 7 group quarters types, and their census block of residence after the addition of discrete Gaussian noise (with the scale parameter determined by the privacy-loss budget allocation for that particular query under zCDP). Noisy measurements of the counts of occupied and vacant housing units by census block are also included. Lastly, data on constraints--information into which no noise was infused by the Disclosure Avoidance System (DAS) and used by the TDA to post-process the noisy measurements into the 2020 Census Redistricting Data (P.L. 94-171) Summary File --are provided.

4D Nucleome (4DN)

The goal of the National Institutes of Health (NIH) Common Fund’s 4D Nucleome (4DN) program is to study the three-dimensional organization of the nucleus in space and time (the 4th dimension). The nucleus of a cell contains DNA, the genetic “blueprint” that encodes all of the genes a living organism uses to produce proteins needed to carry out life-sustaining cellular functions. Understanding the conformation of the nuclear DNA and how it is maintained or changes in response to environmental and cellular cues over time will provide insights into basic biology as well as aspects of human health and disease. The 4DN is an international consortium of researchers who generate data that include results from a variety of genomics and imaging assays with a focus on, but not exclusive to, those that demonstrate close contact between chromatin loci that are non-adjacent on the linear DNA sequence of chromosomes. Additional assays probe the nuclear landscape in the context of interactions of chromatin with specific proteins, RNAs and epigenetic changes.

A Global Drought and Flood Catalogue from 1950 to 2016

Hydrological extremes, in the form of droughts and floods, have impacts on a wide range of sectors including water availability, food security, and energy production, among others. Given continuing large impacts of droughts and floods and the expectation for significant regional changes projected in the future, there is an urgent need to provide estimates of past events and their future risk, globally. However, current estimates of hydrological extremes are not robust and accurate enough, due to lack of long-term data records, standardized methods for event identification, geographical inconsistencies and data uncertainties. To tackle these challenges, we develop the first Global Drought and Flood Catalogue (GDFC) [He et al., 2020] for 1950-2016 by merging the latest in situ and remote-sensing datasets with state-of-the-art land surface and hydrodynamic modeling to provide a continuous and consistent estimate of the terrestrial water cycle and its extremes. This GDFC also includes an unprecedented level of detailed analysis of drought and large-scale flood events using a multivariate risk assessment framework, which incorporates regional spatial-temporal characteristics (i.e., duration, spatial extent, severity) and global hazard maps for different return periods. This catalogue forms a basis for analyzing the changing risk of droughts and floods and can underscore national and international climate change assessments and provide a key reference for climate change studies and climate model evaluations.

ASL 1000

This dataset provides a high-fidelity collection of American Sign Language (ASL) videos annotated with 2D landmarks for hands, pose, and face. The data is designed to train advanced research and development in ASL recognition, translation, gesture analysis, and computer animation. The annotations for this dataset were generated using an automated data pipeline to pre-annotate keyframes from the source videos. As a final, critical step, all automated annotations were subsequently reviewed and meticulously corrected by human labellers to ensure the highest level of accuracy and reliability, making it suitable for training production-grade machine learning models.

Africa Soil Information Service (AfSIS) Soil Chemistry

This dataset contains soil infrared spectral data and paired soil property reference measurements for georeferenced soil samples that were collected through the Africa Soil Information Service (AfSIS) project, which lasted from 2009 through 2018. In this release, we include data collected during Phase I (2009-2013.) Georeferenced samples were collected from 19 countries in Sub-Saharan African using a statistically sound sampling scheme, and their soil properties were analyzed using both conventional soil testing methods and spectral methods (infrared diffuse reflectance spectroscopy). The two types of data can be paired to form a training dataset for machine learning, such that certain soil properties can be well-predicted through less expensive spectral techniques.

AgricultureVision

Agriculture-Vision aims to be a publicly available large-scale aerial agricultural image dataset that is high-resolution, multi-band, and with multiple types of patterns annotated by agronomy experts. The original dataset affiliated with the 2020 CVPR paper includes 94,986 512x512images sampled from 3,432 farmlands with nine types of annotations: double plant, drydown, endrow, nutrient deficiency, planter skip, storm damage, water, waterway and weed cluster. All of these patterns have substantial impacts on field conditions and the final yield. These farmland images were captured between 2017 and 2019 across multiple growing seasons in numerous farming locations in the US. Each field image contains four color channels: Near-infrared (NIR), Red, Green and Blue. We first randomly split the 3,432 farmland images with a 6/2/2 train/val/test ratio. We then assign each sampled image to the split of the farmland image they are cropped from. This guarantees that no cropped images from the same farmland will appear in multiple splits in the final dataset. The generated (supervised) Agriculture-Vision dataset thus contains 56,944/18,334/19,708 train/val/test images. Additionally, we continue to grow this dataset. In 2021 as a part of the Prize Challenge at CVPR, we have added sequences of full-field imagery across 52 fields to promote the use of weakly supervised methods.

Allen Institute for Brain Science - Synaptic Physiology Public Data Set

This is a large-scale survey that describes the physiology (strength, kinetics, and short term plasticity) of thousands of synapses from patch clamp experiments in mouse visual cortex and human middle temporal gyrus.

Allen Institute for Neural Dynamics - Extracellular Electrophysiology Compression Benchmark

Extracellular electrophysiology data is growing at a remarkable pace. This data, collected neuropixels probes by the Allen Institute and the International Brain Lab can be used to benchmark throughput rates and storage ratios of various data compression algorithms.

Allen Institute for Neural Dynamics - Extracellular Electrophysiology Hybrid Evaluation Benchmark

Evaluation of spike sorting methods is a challenging task, as it requires both ground-truth data and a variety of sorting algorithms to compare against. This dataset contains a set of hybrid data specifically designed for benchmarking spike sorting methods.

Allen institute intratelencephalic neuron connectivity paper supplemental data

organized and data files for plotting figures in the manuscript of VISp intratelencephalic (IT) neuron connectivity using MICrONS EM dataset

Animal Tracking - Acoustic Telemetry - Quality controlled detections

Since 2007, the Integrated Marine Observing System’s Animal Tracking Facility (formerly known as the Australian Animal Tracking And Monitoring System (AATAMS)) has established a permanent array of acoustic receivers around Australia to detect the movements of tagged marine animals in coastal waters. Simultaneously, the Animal Tracking Facility developed a centralised national database (https://animaltracking.aodn.org.au/) to encourage collaborative research across the Australian research community and provide unprecedented opportunities to monitor broad-scale animal movements. The resulting dataset comprises observations of tagged animals in Australian waters collected by IMOS infrastructure as well as receivers operated by independent research projects and organisations. This dataset constitutes a valuable resource facilitating meta-analysis of animal movement, distributions, and habitat use, and is important for relating species distribution shifts with environmental covariates.This dataset comprises all available (2007 – ongoing) quality-controlled animal detections collected across the collaborative, continental IMOS network for a range of aquatic species (fish, sharks, rays, reptiles, and mammals). Here, raw animal detections collated via the IMOS Australian Animal Acoustic Telemetry Database have been quality-controlled as per Hoenner et al. (2018). This dataset is updated on a six-monthly basis.Note - There is a static snapshot of the database (up until 2017) (http://dx.doi.org/10.4225/69/5979810a7dd6f), and this has been documented in a Scientific Data Publication (Hoenner et al. 2018).

Astrophysics Division Galaxy Segmentation Benchmark Dataset

Pan-STARSS imaging data and associated labels for galaxy segmentation into galactic centers, galactic bars, spiral arms and foreground stars derived from citizen scientist labels from the Galaxy Zoo: 3D project.

Atmospheric Models from Météo-France

Global and high-resolution regional atmospheric models from Météo-France.

ARPEGE World covers the entire world at a base horizontal resolution of 0.5° (~55km) between grid points, it predicts weather out up to 114 hours in the future.
ARPEGE Europe covers Europe and North-Africa at a base horizontal resolution of 0.1° (~11km) between grid points, it predicts weather out up to 114 hours in the future.
AROME France covers France at a base horizontal resolution of 0.025° (~2.5km) between grid points, it predicts weather out up to 42 hours in the future.
AROME France HD covers France and neighborhood at a base horizontal resolution of 0.01° (~1.5km) between grid points, it predicts weather out up to 42 hours in the future.

Dozens of atmospheric variables are available through this datase: temperatures, winds, precipitation...Our work is based on open-data from Météo-France, but we are not affiliated or endorsed by Météo-France.

Aurora Multi-Sensor Dataset

The Aurora Multi-Sensor Dataset is an open, large-scale multi-sensor dataset with highly accurate localization ground truth, captured between January 2017 and February 2018 in the metropolitan area of Pittsburgh, PA, USA by Aurora (via Uber ATG) in collaboration with the University of Toronto. The de-identified dataset contains rich metadata, such as weather and semantic segmentation, and spans all four seasons, rain, snow, overcast and sunny days, different times of day, and a variety of traffic conditions.
The Aurora Multi-Sensor Dataset contains data from a 64-beam Velodyne HDL-64E LiDAR sensor and seven 1920x1200-pixel resolution cameras including a forward-facing stereo pair and five wide-angle lenses covering a 360-degree view around the vehicle.
This data can be used to develop and evaluate large-scale long-term approaches to autonomous vehicle localization. Its size and diversity make it suitable for a wide range of research areas such as 3D reconstruction, virtual tourism, HD map construction, and map compression, among others.
The data was first presented at the International Conference on Intelligent Robots and Systems (IROS) in 2020, where it was nominated as a Finalist for Best Application Paper at the conference.

Biodiversity Heritage Library Metadata and Page Images

The Biodiversity Heritage Library (BHL) is the world’s largest open access digital library for biodiversity literature and archives. BHL operates as a worldwide consortium of natural history, botanical, research, and national libraries working together to digitize the natural history literature held in their collections and make it freely available for open access.

Biological and Physical Sciences (BPS) Microscopy Benchmark Training Dataset

Fluorescence microscopy images of individual nuclei from mouse fibroblast cells, irradiated with Fe particles or X-rays with fluorescent foci indicating 53BP1 positivity, a marker of DNA damage. These are maximum intensity projections of 9-layer microscopy Z-stacks.

Biological and Physical Sciences (BPS) RNA Sequencing Benchmark Training Dataset

RNA sequencing data from spaceflown and control mouse liver samples, sourced from NASA GeneLab and augmented with generative adversarial network.

Brain Encoding Response Generator (BERG)

Brain Encoding Response Generator (BERG) is a resource consisting of multiple pre-trained encoding models of the brain and an accompanying Python package to generate accurate in silico neural responses to arbitrary stimuli with just a few lines of code.

Brain/MINDS Marmoset Connectivity Resource on AWS

Brain/MINDS Marmoset Connectivity Resource (BMCR) is a resource that provides access to anterograde and retrograde neuronal tracer data, made available by Brain/MINDS project. It is currently restricted to injections into the prefrontal cortex of a marmoset brain but is planned to include injections into entire cortical areas and representative subcortical brain regions.

BrainGlobe Atlases

BrainGlobe provides an archive and standardised interface to anatomical atlases from multiple species. This dataset includes these atlases, and other data (e.g. sample neuroanatomy data) to allow the greatest use of the atlases.

BrainSeq - Neurogenomics to Drive Novel Target Discovery for Neuropsychiatric Disorders

This ambitious project seeks to characterize the genetic and epigenetic regulation of multiple facets of transcription in distinct brain regions across the human lifespan in samples of major neuropsychiatric disorders and controls. Initially focused on schizophrenia and mood disorders, the goal of this consortium is to elucidate the underlying molecular mechanisms of genetic associations with the goal of identifying novel therapeutic targets. The consortium currently consists of seven pharmaceutical companies and a not-for-profit medical research institution working as a precompetitive team to generate and analyze publicly available archival brain genomic data related to neuropsychiatric illness.

Broad Genome References

Broad maintained human genome reference builds hg19/hg38 and decoy references.

COVID-19 Data Lake

A centralized repository of up-to-date and curated datasets on or related to the spread and characteristics of the novel corona virus (SARS-CoV-2) and its associated illness, COVID-19. Globally, there are several efforts underway to gather this data, and we are working with partners to make this crucial data freely available and keep it up-to-date. Hosted on the AWS cloud, we have seeded our curated data lake with COVID-19 case tracking data from Johns Hopkins and The New York Times, hospital bed availability from Definitive Healthcare, and over 45,000 research articles about COVID-19 and related coronaviruses from the Allen Institute for AI.

CanElevation - Canada Digital Elevation Models

The Canadian DEM represents the current coverage of elevation data available. This dataset includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived products. This dataset includes a 1m, 2m and 30m DEM. The 1m and 2 m products are a combination of DEM data generated from airborne LiDAR and optical digital images. The 30 m DEM integrates data from the Copernicus DEM acquired during the TanDEM-X Mission, with the DEM data derived from airborne lidar and provides a complete coverage for Canada.

Le modèle numérique d’élévation (MNE) canadien représente la couverture actuelle des données d’élévation disponibles. Ce jeu de données comprend un Modèle Numérique de Terrain (MNT), un Modèle Numérique de Surface (MNS) et d’autres produits dérivés. Ce jeu de données propose des MNE de résolution 1 m, 2 m et 30 m. Les produits 1 m et 2 m sont issus d’une combinaison de données MNE générées à partir de LiDAR aéroporté et d’images numériques optiques. Le MNE de 30 m intègre des données provenant du MNE Copernicus acquis lors de la mission TanDEM-X, ainsi que les données MNE issues du LiDAR aéroporté, ce qui permet d’assurer une couverture complète du Canada.

Cancer Genome Characterization Initiatives - Burkitt Lymphoma, HIV+ Cervical Cancer

The Cancer Genome Characterization Initiatives (CGCI) program supports cutting-edge genomics research of adult and pediatric cancers. CGCI investigators develop and apply advanced sequencing methods that examine genomes, exomes, and transcriptomes within various types of tumors. The program includes Burkitt Lymphoma Genome Sequencing Project (BLGSP) project and HIV+ Tumor Molecular Characterization Project - Cervical Cancer (HTMCP-CC) project. The dataset contains open Clinical Supplement, Biospecimen Supplement, RNA-Seq Gene Expression Quantification, miRNA-Seq Isoform Expression Quantification, and miRNA Expression Quantification data. This dataset also contains controlled WGS/Targeted Sequencing/RNA-Seq/miRNA-Seq Aligned Reads, and RNA-Seq Splice Junction Quantification

Cell Painting Image Collection

The Cell Painting Image Collection is a collection of freely downloadable microscopy image sets. Cell Painting is an unbiased high throughput imaging assay used to analyze perturbations in cell models. In addition to the images themselves, each set includes a description of the biological application and some type of "ground truth" (expected results). Researchers are encouraged to use these image sets as reference points when developing, testing, and publishing new image analysis algorithms for the life sciences. We hope that the this data set will lead to a better understanding of which methods are best for various biological image analysis applications.

Cloud Indexes for Bowtie, Kraken, HISAT, and Centrifuge

Genomic tools use reference databases as indexes to operate quickly and efficiently, analogous to how web search engines use indexes for fast querying. Here, we aggregate genomic, pan-genomic and metagenomic indexes for analysis of sequencing data.

Collection of open nation-scale LiDAR datasets

The goal of this project is to collect all publicly available large scale LiDAR datasets and archive them in an uniform fashion for easy access and use. Initial efforts to collect the datasets are concentrated on Europe and will be in future expanded to USA and other regions, striving for global coverage. Every dataset includes files in original data format and translated to COPC format. For faster browsing, we include an overview file that includes a small subset of data points from every dataset file in a single COPC file.

Consented Activities of People

The Consented Activities of People (CAP) dataset is a fine grained activity dataset for visual AI research curated using the Visym Collector platform.

Copernicus Digital Elevation Model (DEM)

The Copernicus DEM is a Digital Surface Model (DSM) which represents the surface of the Earth including buildings, infrastructure and vegetation. We provide two instances of Copernicus DEM named GLO-30 Public and GLO-90. GLO-90 provides worldwide coverage at 90 meters. GLO-30 Public provides limited worldwide coverage at 30 meters because a small subset of tiles covering specific countries are not yet released to the public by the Copernicus Programme. Note that in both cases ocean areas do not have tiles, there one can assume height values equal to zero. Data is provided as Cloud Optimized GeoTIFFs and comes from Copernicus DEM 2021 release.

CoversBR

CoversBR is the first large audio database with, predominantly, Brazilian music for the tasks of Covers Song Identification (CSI) and Live Song Identifications (LSI). Due to copyright restrictions audios of the songs cannot be made available, however metadata and files of features have public access. Audio streamings captured from radio and TV channels for the live song identification task will be made public. CoversBR is composed of metadata and features extracted from 102298 songs, distributed in 26366 groups of covers/versions, with an average of 3.88 versions per group. The entire collection adds up to a total of approximately 7070 hours and the average song length is 240 seconds (4 minutes).

Covid Job Impacts - US Hiring Data Since March 1 2020

This dataset provides daily updates on the volume of US job listings filtered by geography industry job family and role; normalized to pre-covid levels.These data files feed the business intelligence visuals at covidjobimpacts.greenwich.hr, a public-facing site hosted by Greenwich.HR and OneModel Inc. Data is derived from online job listings tracked continuously, calculated daily and published nightly. On average data from 70% of all new US jobs are captured, and the dataset currently contains data from 3.3 million hiring organizations.Data for each filter segment is represented as the 7-day average of new job listings for a specific date, expressed as a percentage of the corresponding value on March 1, 2020.

Cryo-EM SPA Workflow Records

The “Cryo-EM SPA Workflow Records” contains all outputs of all processing steps involved in cryogenic electron microscopy (cryo-EM) single particle analysis (SPA), including both intermediate and final output data. The primary focus will be on data generated by RELION and CryoSPARC, two widely used software packages for :Cryo-EM SPA. These records will be archived systematically. To ensure the data remains reproducible while minimizing storage demands, large-sized files that can be regenerated will be excluded prior to registration. The aim is to retain only the essential metadata, processing parameters, and representative outputs that allow for full reconstruction of the analysis pipeline. This approach balances the need for long-term data preservation with practical considerations for storage capacity. Through this effort, we seek to enhance transparency and reproducibility in cryo-EM research by providing a structured and accessible record of the analysis process. Importantly, the use of this dataset is intended to facilitate the development of future AI algorithms in the field of Cryo-EM.

DNAStack COVID19 SRA Data

The Sequence Read Archive (SRA) is the primary archive of high-throughput sequencing data, hosted by the National Institutes of Health (NIH). The SRA represents the largest publicly available repository of SARS-CoV-2 sequencing data. This dataset was created by DNAstack using SARS-CoV-2 sequencing data sourced from the SRA. Where possible, raw sequence data were processed by DNAstack through a unified bioinformatics pipeline to produce genome assemblies and variant calls. The use of a standardized workflow to produce this harmonized dataset allows public data generated using different methodologies to be combined and compared for a more powerful global analysis of available SARS-CoV-2 data, allowing researchers rapid access to aggregated downstream results for accelerated insight generation. Methodology: Reads from the SRA were extracted in FASTQ format, then entered into a different pipeline depending on the sequencing technology used to create the reads: the ARTIC protocol for Oxford Nanopore-derived reads; the SIGNAL pipeline for paired-end Illumina reads; and the CoSA pipeline (using DeepVariant for variant calling) for PacBio reads. Briefly, reads were primer-trimmed and aligned to the SARS-CoV-2 reference genome, following which contiguous regions were assembled and variant sites were called. Pangolin was then used to assign viral lineage based on the assembled genome.

Danish Meteorological Institute (DMI) Reanalysis dataset v0.5

DANRA is a high-resolution meteorological reanalysis dataset for Denmark and Northwestern Europe covering the period September 1990 to December 2023

Dendritic Consortium Multimodal Dataset

The Dendritic Consortium provides a multimodal dataset integrating calcium and voltage imaging, electrophysiology, electron microscopy, proteomics, and computational models of Baz1a pyramidal neurons in the mouse primary visual cortex (V1).

DigitalCorpora

Disk images, memory dumps, network packet captures, and files for use in digital forensics research and education. All of this information is accessible through the digitalcorpora.org website, and made available at s3://digitalcorpora/. Some of these datasets implement scenarios that were performed by students, faculty, and others acting in persona. As such, the information is synthetic and may be used without prior authorization or IRB approval. Details of these datasets can be found at http://www.simson.net/clips/academic/2009.DFRWS.Corpora.pdf

Downscaled CMIP6 projections over CONUS at 30-arcsecond horizontal resolution

The NEX-DCP30-CMIP6 dataset contains daily, monthly, and annual downscaled CMIP6 projections over CONUS at 30-arcsecond hoirzontal resolution for maximum temperature, minimum temperature, precipitation, and vapor pressure.

Downscaled Climate Data for Alaska (v1.1, August 2023)

This dataset contains historical and projected dynamically downscaled climate data for the State of Alaska and surrounding regions at 20km spatial resolution and hourly temporal resolution. Select variables are also summarized into daily resolutions. This data was produced using the Weather Research and Forecasting (WRF) model (Version 3.5). We downscaled both ERA-Interim historical reanalysis data (1979-2015) and both historical and projected runs from 2 GCM’s from the Coupled Model Inter-comparison Project 5 (CMIP5): GFDL-CM3 and NCAR-CCSM4 (historical run: 1970-2005 and RCP 8.5: 2006-2100). Note - this dataset was updated in August, 2023 to retain the latitude and longitude grids as variables in the files.

E11bio PRISM

This dataset was generated using E11.bio's PRISM technology (Protein Reconstruction and Identification through Multiplexing), a platform that combines viral barcoding, expansion microscopy, and iterative immunolabeling for large-scale neuronal reconstruction.Neurons in the mouse hippocampal CA3 were transduced with a library of adeno-associated viruses (AAVs) encoding diverse “protein bits”—small epitope tags that act as combinatorial barcodes. Tissue was then processed with an expansion microscopy protocol, physically enlarging the sample ~5× to achieve an effective voxel size of ~35 × 35 × 80 nm. Across multiple cycles of staining, imaging, and antibody stripping, the same expanded tissue was repeatedly labeled, enabling iterative immunostaining for dozens of molecular targets.The dataset includes:

Light microscopy data of multiplexed brain tissue
Segmentations of cell morphology and protein expression in the tissue
Files for faster visualization of the data (e.g. precomputed format)
Additional supporting files (e.g. model predictions, manual annotations etc.)

ECMWF AIFS Single - dynamical.org Icechunk Zarr

The Artificial Intelligence Forecasting System (AIFS) is a data driven forecast model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). This is the non-ensemble configuration of AIFS that produces a single forecast trace. AIFS is trained on ECMWF's ERA5 re-analysis and ECMWF's operational numerical weather prediction (NWP) analyses.

These datasets have been translated to cloud-optimized Icechunk Zarr format by dynamical.org.

ECMWF AIFS Single forecast - Weather forecasts from the ECMWF Artificial Intelligence Forecasting System (AIFS) Single model.

EMBER Open Datasets

This is data from, Ecosystem for Multi-modal Brain-behavior Experimentation and Research (EMBER), It contains time series behavioral and neuroscience data from animal and deidentified human subjects across multiple modalities.

EMory BrEast Imaging Dataset (EMBED)

EMBED is a racially diverse mammography dataset containing 3.4M screening and diagnostic images from 110,000 patients collected from 2013-2020, with an equal representation of black and white women. The dataset is comprised of 2D, synthetic 2D (C-view), and 3D (digital breast tomosynthesis, i.e. DBT) images. It contains 60,000 annotated lesions linked to structured imaging descriptors and ground truth pathologic outcomes grouped into six severity classes. This release represents 20% of the total 2D and C-view dataset and is available for research use. DBT, US, and MRI exams will be added at a later date.

Acknowledgements - We would like to thank Glendor, Inc and MD.ai for assistance with image de-identification.

Emory Knee Radiograph (MRKR) dataset

The Emory Knee Radiograph (MRKR) dataset is a large, demographically diverse collection of 503,261 knee radiographs from 83,011 patients, 40% of which are African American. This dataset provides imaging data in DICOM format along with detailed clinical information, including patient- reported pain scores, diagnostic codes, and procedural codes, which are not commonly available in similar datasets. The MRKR dataset also features imaging metadata such as image laterality, view type, and presence of hardware, enhancing its value for research and model development. MRKR addresses significant gaps in existing datasets by offering a more representative sample for studying osteoarthritis and related outcomes, particularly among minority populations, thereby providing a valuable resource for clinicians and researchers. Acknowledgments - We would like to thank MD.ai for assistance with image de-identification.

FLAb: Fitness Landscapes for Antibodies

FLAb is the largest publicly available therapeutic antibody dataset designed to train and benchmark protein AI models. It provides open-access, high-quality developability data on diverse therapeutic properties, including expression, thermostability, immunogenicity, aggregation, polyreactivity, binding affinity, and pharmacokinetics.

Ford Multi-AV Seasonal Dataset

This research presents a challenging multi-agent seasonal dataset collected by a fleet of Ford autonomous vehicles at different days and times during 2017-18. The vehicles The vehicles were manually driven on an average route of 66 km in Michigan that included a mix of driving scenarios like the Detroit Airport, freeways, city-centres, university campus and suburban neighbourhood, etc. Each vehicle used in this data collection is a Ford Fusion outfitted with an Applanix POS-LV inertial measurement unit (IMU), four HDL-32E Velodyne 3D-lidar scanners, 6 Point Grey 1.3 MP Cameras arranged on the rooftop for 360 degree coverage and 1 Pointgrey 5 MP camera mounted behind the windsheild for forward field of view. We present the seasonal variation in weather, lighting, construction and traffic conditions experienced in dynamic urban environments. This dataset can help design robust algorithms for autonomous vehicles and multi-agent systems. Each log in the dataset is time-stamped and contains raw data from all the sensors, calibration values, pose trajectory, ground truth pose, and 3D maps. All data is available in Rosbag format that can be visualized, modified and applied using the open-source Robot Operating System (ROS).

GATK Structural Variation (SV) Data

This dataset holds the data needed to run a structural variation discovery pipeline for Illumina short-read whole-genome sequencing (WGS) data in AWS.

GEDI L2A Elevation and Height Metrics Data Global Footprint Level V002

The Global Ecosystem Dynamics Investigation (GEDI) mission aims to characterize ecosystem structure and dynamics to enable radically improved quantification and understanding of the Earth’s carbon cycle and biodiversity. The GEDI instrument produces high resolution laser ranging observations of the 3-dimensional structure of the Earth. GEDI is attached to the International Space Station (ISS) and collects data globally between 51.6° N and 51.6° S latitudes at the highest resolution and densest sampling of any light detection and ranging (lidar) instrument in orbit to date. Each GEDI Version 2 granule encompasses one-fourth of an ISS orbit and includes georeferenced metadata to allow for spatial querying and subsetting.The GEDI instrument was removed from the ISS and placed into storage on March 17, 2023. No data were acquired during the hibernation period from March 17, 2023, to April 24, 2024. GEDI has since been reinstalled on the ISS and resumed operations as of April 26, 2024.The purpose of the GEDI Level 2A Geolocated Elevation and Height Metrics product (GEDI02_A) is to provide waveform interpretation and extracted products from each GEDI01_B received waveform, including ground elevation, canopy top height, and relative height (RH) metrics. The methodology for generating the GEDI02_A product datasets is adapted from the Land, Vegetation, and Ice Sensor (LVIS) algorithm. The GEDI02_A product is provided in HDF5 format and has a spatial resolution (average footprint) of 25 meters.The GEDI02_A data product contains 156 layers for each of the eight beams, including ground elevation, canopy top height, relative return energy metrics (e.g., canopy vertical structure), and many other interpreted products from the return waveforms. Additional information for the layers can be found in the GEDI Level 2A Dictionary.Known Issues

Data acquisition gaps: GEDI data acquisitions were suspended on December 19, 2019 (2019 Day 353) and resumed on January 8, 2020 (2020 Day 8).
Incorrect Reference Ground Track (RGT) number in the filename for select GEDI files: GEDI Science Data Products for six orbits on August 7, 2020, and November 12, 2021, had the incorrect RGT number in the filename. There is no impact to the science data, but users should reference this document for the correct RGT numbers.
Known Issues: Section 8 of the User Guide provides additional information on known issues.

Improvements/Changes from Previous Versions

Metadata has been updated to include spatial coordinates.
Granule size has been reduced from one full ISS orbit (~~5.83 GB) to four segments per orbit (~~1.48 GB).
Filename has been updated to include segment number and version number.
Improved geolocation for an orbital segment.
Added elevation from the SRTM digital elevation model for comparison.
Modified the method to predict an optimum algorithm setting group per laser shot.
Added additional land cover datasets related to phenology, urban infrastructure, and water persistence.
Added selected_mode_flag dataset to root beam group using selected algorithm.
Removed shots when the laser is not firing.
Modified file name to include segment number and dataset version. Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

GHRSST Level 2P Global Sea Surface Skin Temperature from the MODIS on the NASA Terra satellite (GDS2)

NASA produces skin sea surface temperature (SST) products from the Infrared (IR) channels of the Moderate-resolution Imaging Spectroradiometer (MODIS) onboard the Terra satellite. Terra was launched by NASA on December 18, 1999, into a sun synchronous, polar orbit with a daylight descending node at 10:30 am, to study the global dynamics of the Earth atmosphere, land and oceans. The MODIS captures data in 36 spectral bands at a variety of spatial resolutions. Two SST products can be present in these files. The first is a skin SST produced for both day and night observations, derived from the long wave IR 11 and 12 micron wavelength channels, using a modified nonlinear SST algorithm intended to provide continuity with SST derived from heritage and current NASA sensors. At night, a second SST product is produced using the mid-infrared 3.95 and 4.05 micron channels which are unique to MODIS; the SST derived from these measurements is identified as SST4. The SST4 product has lower uncertainty, but due to sun glint can only be produced at night. MODIS L2P SST data have a 1 km spatial resolution at nadir and are stored in 288 five minute granules per day. Full global coverage is obtained every two days, with coverage poleward of 32.3 degree being complete each day. The production of MODIS L2P SST files is part of the Group for High Resolution Sea Surface Temperature (GHRSST) project, and is a joint collaboration between the NASA Jet Propulsion Laboratory (JPL), the NASA Ocean Biology Processing Group (OBPG), and the Rosenstiel School of Marine and Atmospheric Science (RSMAS). Researchers at RSMAS are responsible for SST algorithm development, error statistics and quality flagging, while the OBPG, as the NASA ground data system, is responsible for the production of daily MODIS ocean products. JPL acquires MODIS ocean granules from the OBPG and reformats them to the GHRSST L2P netCDF specification with complete metadata and ancillary variables, and distributes the data as the official Physical Oceanography Data Archive (PO.DAAC) for SST. The R2019.0 supersedes the previous R2014.0 datasets which can be found at https://doi.org/10.5067/GHMDT-2PJ02 Read our doc on how to get AWS Credentials to retrieve this data: https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME

GPM DPR Precipitation Profile L2A 1.5 hours 5 km V07 (GPM_2ADPR) at GES DISC

Version 07 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 07. .2ADPR provides single- and dual-frequency-derived precipitation estimates from the Ku and Ka radars of the Dual-Frequency Precipitation Radar (DPR) on the core GPM spacecraft. The output consists of three main classes of precipitation products: those derived from the Ku-band frequency over a wide swath (245 km), those derived from the Ka-band frequency over a narrow swath (125 km), and those derived from the dual-frequency data over the narrow swath. The Ka-band results are further divided into the standard and high-sensitivity estimates. In the standard sensitivity mode, the fields of view within the inner swath are matched to those of the Ku-band. Data from these matched-beam Ku- and Ka-band fields of view are used to derive the dual-frequency precipitation products. The retrievals are performed at each radar range bin along the slant path of the radar instrument field of view (IFOV). Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

Genomic Characterization of Metastatic Castration Resistant Prostate Cancer

Biopsies of castration resistant prostate cancer metastases were subjected to whole genome sequencing (WGS), along with RNA-sequencing (RNA-Seq). The overarching goal of the study is to illuminate molecular mechanisms of acquired resistance to therapeutic agents, and particularly androgen signaling inhibitors, in the treatment of metastatic castration resistant prostate cancer (mCRPC). This study is made available on AWS via the NIH STRIDES Initiative.

Genoxus Annotation

Genoxus Annotation is a harmonized and curated collection of human genetic variant databases designed to support accurate and salable variant annotation. Variant annotation following genetic testing such as whole genome sequencing (WGS) or whole exome sequencing (WES) is a critical step in identifying and interpreting disease-associated genetic factors. As sequencing technologies continue to generate large volumes of genomic data, robust and well-structured annotation resources are essential for translating raw variant calls into clinically meaningful insights. Genoxus Annotation v1.0 integrates data from NCBI ClinVar. ClinVar provides curated information on the clinical significance of a broad spectrum of genetic variants including single nucleotide variants (SNVs), insertions (INS), deletions (DEL), INDELs, copy number variations (CNVs), and structural variants (SVs) along with their associated diseases and traits. GWAS catalog complements ClinVar by focusing primarily on SNVs identified through genome wide association studies, linking common variants to complex diseases and phenotype traits. (GWAS data is planned in a future release.) By harmonizing variant representations, standardizing disease terminology, and consolidating evidence across sources, Genoxus Annotation provides a unified framework that streamlines variant interpretation for research and clinical applications.

Harvard Electroencephalography Database

The Harvard EEG Database will encompass data gathered from four hospitals affiliated with Harvard University:Massachusetts General Hospital (MGH), Brigham and Women's Hospital (BWH), Beth Israel Deaconess Medical Center (BIDMC), and Boston Children's Hospital (BCH).

Harvard-Emory ECG Database

The Harvard-Emory ECG database (HEEDB) is a large collection of 12-lead electrocardiography (ECG) recordings, prepared through a collaboration between Harvard University and Emory University investigators.

Hecatomb Databases

Preprocessed databases for use with the Hecatomb pipeline for viral and phage sequence annotation.

Human Cell Atlas

The Human Cell Atlas (HCA) is a collaborative community of international scientists. Our mission is to create comprehensive reference maps of all the cells in the human body as a basis for both understanding human health and diagnosing, monitoring, and treating disease. The HCA registry has more than one thousand member scientists from hundreds of institutions around the world. The project is steered and governed by an Organizing Committee, co-chaired by Aviv Regev and Sarah Teichmann.

IWMI DIWASA Blue ET for Africa

Blue evapotranspiration (Blue ET) is the portion of ET derived from blue water sources, including surface water (rivers, lakes, reservoirs) and groundwater used for irrigation. It is a key component of blue water fluxes in water accounting. Blue ET consists of evaporation from irrigated fields, transpiration from irrigated crops, and water lost from artificial storage. It helps assess water productivity in irrigated agriculture, quantify consumptive water use, and support sustainable water resource management, particularly in water-scarce regions.

Indexes for Kaiju

This dataset comprises pre-built indexes for the bioinformatics software Kaiju, which is used for taxonomic classification of metagenomic sequencing data. Various indexes for different source reference databases are available.

Indian Supreme Court Judgments

This dataset contains judgements from the Indian Supreme Court, downloaded from ecourts website. It contains judgments from 1950 to 2025, along with raw metadata (in json format) and structured metadata in parquet format. Judgments are available in both English and regional Indian languages in zip format for easier download.

Integrative Analysis of Lung Adenocarcinoma in Environment and Genetics Lung cancer Etiology (Phase 2)

We performed whole genome sequencing and whole exome sequencing of 31 lung adenocarcinoma (LUAD) samples from the Environment And Genetics in Lung cancer Etiology (EAGLE) study. The EAGLE study is made available on AWS via the NIH STRIDES Initiative (https://aws.amazon.com/blogs/publicsector/aws-and-national-institutes-of-health-collaborate-to-accelerate-discoveries-with-strides-initiative/).

James Webb Space Telescope (JWST)

The James Webb Space Telescope (JWST) is the world's next flagship infrared observatory led by NASA with its partners, ESA (European Space Agency), and CSA (Canadian Space Agency). JWST offers scientists the opportunity to observe galaxy evolution, the formation of stars and planets, exoplanetary systems, and our own solar system, in ways never before possible.

LEarning biOchemical Prostate cAncer Recurrence from histopathology sliDes challenge (LEOPARD) Dataset

"This dataset contains the all data for the LEarning biOchemical Prostate cAncer Recurrence from histopathology sliDes challenge or LEOPARD.Prostate cancer, impacting 1.4 million men annually, is a prevalent malignancy (H. Sung et al., 2021). A substantial number of these individuals undergo prostatectomy as the primary curative treatment. The efficacy of this surgery is assessed, in part, by monitoring the concentration of prostate-specific antigen (PSA) in the bloodstream. While the role of PSA in prostate cancer screening is debatable (W. F. Clark et al., 2018; E. A. M. Heijnsdijk et al., 2018), it serves as a valuable biomarker for postprostatectomy follow-up in patients. Following successful surgery, PSA concentration is typically undetectable (<0.1 ng/mL) within 4-6 weeks (S. S. Goonewardene et al., 2014). However, approximately 30% of patients experience biochemical recurrence, signifying the resurgence of prostate cancer cells. This recurrence serves as a prognostic indicator for progression to clinical metastases and eventual prostate cancer-related mortality (C. L. Amling, 2014; S. J. Freedland et al., 2005; M. Han et al., 2001; T. Van den Broeck et al., 2001. Current clinical practices gauge the risk of biochemical recurrence by considering the International Society of Urological Pathology (ISUP) grade, PSA value at diagnosis, and TNM staging criteria (J. I. Epstein et al., 2016). A recent European consensus guideline suggests categorizing patients into low-risk, intermediate-risk, and high-risk groups based on these factors (N. Mottet et al., 2021). Notably, a high ISUP grade independently assigns a patient to the intermediate (grade 2/3) or high-risk group (grade 4/5). The Gleason growth patterns, representing morphological patterns of prostate cancer, are used to categorize cancerous tissue into ISUP grade groups (J. I. Epstein, 2010; P. M. Pierorazio et al., 2013; G. J. L. H. van Leenders et al., 2020; J. I. Epstein et al., 2016). However, the ISUP grade has limitations, such as grading disagreement among pathologists (J. I. Epstein et al., 2016) and coarse descriptors of tissue morphology. Recently, deep learning was shown (H. Pinckaers et al., 2022; O. Eminaga et. al., 2024) to be able to predict the biochemical recurrence of prostate cancer. Hypothesizing that deep learning could uncover finer morphological features' prognostic value, we are organizing the LEarning biOchemical Prostate cAncer Recurrence from histopathology sliDes (LEOPARD) challenge. The goal of this challenge is to yield top-performance deep learning solutions to predict the time to biochemical recurrence from H&E-stained histopathological tissue sections, i.e. based on morphological features.

Multi-Anatomy Post-Surgical Magnetic Resonance Dataset (MAPSMR)

The MAPSMR dataset is a multi-organ, post-surgical MRI benchmark dataset focused on organ absence and altered anatomy after common abdominal and pelvic surgeries. The dataset includes cases such as cholecystectomy, prostatectomy, nephrectomy, colectomy, hepatectomy, and related procedures, with annotations identifying surgically absent organs and post-treatment anatomical changes.

NASA Physical Sciences Informatics (PSI)

NASA's Physical Sciences Research Program, along with its predecessors, has conducted significant fundamental and applied research in the physical sciences. The International Space Station (ISS) is an orbiting laboratory that provides an ideal facility to conduct long-duration experiments in the near absence of gravity and allows continuous and interactive research similar to Earth-based laboratories. This enables scientists to pursue innovations and discoveries not currently achievable by other means. NASA's Physical Sciences Research Program also benefits from collaborations with several of the ISS international partners—Europe, Russia, Japan, and Canada—and foreign governments with space programs, such as France, Germany and Italy.

In fulfillment of the Open Science model, NASA's Physical Sciences Research Program is pleased to offer the PSI data repository for physical science experiments performed in reduced-gravity environments such as the ISS, Space Shuttle flights, and Free-flyers. PSI also includes data from some related ground-based studies. The PSI system is accessible and open to the public. This provides the opportunity for researchers to data mine results from prior flight investigations, expanding on the research performed. This approach will allow numerous ground-based investigations to be conducted from one flight experiment’s data, exponentially increasing our body of knowledge. PSI is an Open Data initiative.

NIH NLM NCBI PubMed Central (PMC) Article Datasets - Full-Text Biomedical and Life Sciences Journal Articles on AWS

PubMed Central® (PMC) is a free full-text archive of biomedical and life sciences journal article at the U.S. National Institutes of Health's National Library of Medicine (NIH/NLM). The PubMed Central (PMC) Article Datasets include full-text articles archived in PMC and made available under license terms that allow for text mining and other types of secondary analysis and reuse. The articles are organized on AWS based on PMCID and version number:

The PMC Open Access (OA) Subset, which includes all articles in PMC that are available for reuse based on terms specified by the publisher. The majority of available articles have machine-readable Creative Commons license

The Author Manuscript Dataset, which includes all articles collected under a funder policy in PMC and made available in machine-readable formats for text mining. NOTEL Author manuscripts with Creative Commons licenses are also part of the PMC Open Access Subset.

The Historical OCR Dataset, which includes historical articles, published in the 18th, 19th, and 20th centuries, scanned as part of an NLM digitization project, that have Creative Commons licenses. NOTE: These articles are also part of the PMC Open Access Subset.

These datasets collectively span more than half of PMC's total collection of full-text articles. PMC enables access to these datasets to expand the impact of open access and publicly-funded research; enable greater machine learning across the spectrum of scientific research; reach new audiences; and open new doors for discovery. The bucket in this registry contains individual article versions in NISO Z39.96-2015 JATS XML format as well as in plain text as extracted from the XML and the full article PDF. Media files and supplementary material files are also available for all open access articles. The bucket is updated continuously with new and updated articles. Also includes JSON metadata objects for each article version and a CVS inventory file.

NOAA Analysis of Record for Calibration (AORC) Dataset

Announcements:

April 2, 2026: The Office of Water Prediction (OWP) has identified some brittle components of the AORC data processing pipelines that, by our estimations, have resulted in 0.04% of all available rows being incorrectly masked. We've resolved the issue and we are currently regenerating the Zarr files to ensure a complete record. We will post an updated announcement once the corrected data is posted.

The Analysis Of Record for Calibration (AORC) is a gridded record of near-surface weather conditions covering the continental United States and Alaska and their hydrologically contributing areas. It is defined on a latitude/longitude spatial grid with a mesh length of 30 arc seconds (~800 m), and a temporal resolution of one hour. Elements include hourly total precipitation, temperature, specific humidity, terrain-level pressure, downward longwave and shortwave radiation, and west-east and south-north wind components. It spans the period from 1979 across the Continental U.S. (CONUS) and from 1981 across Alaska, to the near-present (at all locations). This suite of eight variables is sufficient to drive most land-surface and hydrologic models and is used as input to the National Water Model (NWM) retrospective simulation. While the native AORC process generates netCDF output, the data is post-processed to create a cloud optimized Zarr formatted equivalent for dissemination using cloud technology and infrastructure.

**AORC Version 1.1 dataset creation**
The AORC dataset was created after reviewing, identifying, and processing multiple large-scale, observation, and analysis datasets. There are two versions of The Analysis Of Record for Calibration (AORC) data.

The initial AORC Version 1.0 dataset was completed in November 2019 and consisted of a grid with 8 elements at a resolution of 30 arc seconds. The AORC version 1.1 dataset was created to address issues "[see Table 1 in Fall et al., 2023](https://onlinelibrary.wiley.com/doi/10.1111/1752-1688.13143)" in the version 1.0 CONUS dataset. Full documentation on version 1.1 of the AORC data and the related journal publication are provided below.

The native AORC version 1.1 process creates a dataset that consists of netCDF files with the following dimensions: 1 hour, 4201 latitude values (ranging from 25.0 to 53.0), and 8401 longitude values (ranging from -125.0 to -67).

The data creation runs with a 10-day lag to ensure the inclusion of any corrections to the input Stage IV and NLDAS data.

**Note** - The full extent of the AORC grid as defined in its data files exceed those cited above; those outermost rows and columns of data grids are filled with missing values and are the remnant of an early set of required AORC extents that have since been adjusted inward.

**AORC Version 1.1 Zarr Conversion**

The goal for converting the AORC data from netCDF to Zarr was to allow users to quickly and efficiently load/use the data. For example, one year of data takes 28 mins to load via NetCDF while only taking 3.2 seconds to load via Zarr (resulting in a substantial increase in speed). For longer periods of time, the percentage increase in speed using Zarr (vs NetCDF) is even higher. Using Zarr also leads to less memory and CPU utilization.

It was determined that the optimal conversion for the data was 1 year worth of Zarr files with a chunk size of 18MB. The chunking was completed across all 8 variables. The chunks consist of the following dimensions: 144 time, 128 latitude, and 256 longitude. To create the files in the Zarr format, the NetCDF files were rechunked using chunk() and "[Xarray](https://docs.xarray.dev/en/stable/)". After chunking the files, they were converted to a monthly Zarr file. Then, each monthly Zarr file was combined using "[to_zarr](https://docs.xarray.dev/en/latest/generated/xarray.Dataset.to_zarr.html)" to create a Zarr file that represents a full year

Users wanting more than 1 year of data will be able to utilize Zarr utilities/libraries to combine multiple years up to the span of the full data set.

There are eight variables representing the meteorological conditions
**Total Precipitaion (APCP_surface)**
1) Hourly total precipitation (kgm-2 or mm) for Calibration (AORC) dataset
**Air Temperature (TMP_2maboveground)** 1) Temperature (at 2 m above-ground-level (AGL)) (K)
**Specific Humidity (SPFH_2maboveground)**
1) Specific humidity (at 2 m AGL) (g g-1)
**Downward Long-Wave Radiation Flux (DLWRF_surface)**
1) longwave (infrared) 2) radiation flux (at the surface) (W m-2)
**Downward Short-Wave Radiation Flux (DSWRF_surface)**
1) Downward shortwave (solar) 2) radiation flux (at the surface) (W m-2)
**Pressure (PRES_surface)**
1) Air pressure (at the surface) (Pa)
**U-Component of Wind (UGRD_10maboveground)"
1)U (west-east) - components of the wind (at 10 m AGL) (m s-1)
**V-Component of Wind (VGRD_10maboveground)"
1) V (south-north) - components of the wind (at 10 m AGL) (m s-1)

**Precipitation and Temperature**

The gridded AORC precipitation dataset contains one-hour Accumulated Surface Precipitation (APCP) ending at the “top” of each hour, in liquid water-equivalent units (kg m-2 to the nearest 0.1 kg m-2), while the gridded AORC temperature dataset is comprised of instantaneous, 2 m above-ground-level (AGL) temperatures at the top of each hour (in Kelvin, to the nearest 0.1).

**Specific Humidity, Pressure, Downward Radiation, Wind**

The development process for the six additional dataset components of the Conus AORC [i.e., specific humidity at 2m above ground (kg kg-1); downward longwave and shortwave radiation fluxes at the surface (W m-2); terrain-level pressure (Pa); and west-east and south-north wind components at 10 m above ground (m s-1)] has two distinct periods, based on datasets and methodology applied: 1979–2015 and 2016–present.

NOAA Climate Forecast System (CFS)

The Climate Forecast System (CFS) is a model representing the global interaction between Earth's oceans, land, and atmosphere. Produced by several dozen scientists under guidance from the National Centers for Environmental Prediction (NCEP), this model offers hourly data with a horizontal resolution down to one-half of a degree (approximately 56 km) around Earth for many variables. CFS uses the latest scientific approaches for taking in, or assimilating, observations from data sources including surface observations, upper air balloon observations, aircraft observations, and satellite observations.
Please note that the data in this bucket are the CFSv2 Operational Forecasts. To obtain other CFSv2 products such as the Operational Analysis, please visit our website.

NOAA GEFS - dynamical.org Icechunk Zarr

The Global Ensemble Forecast System (GEFS) is a National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) weather forecast model. GEFS creates 31 separate forecasts (ensemble members) to describe the range of forecast uncertainty.

These datasets have been translated to cloud-optimized Icechunk Zarr format by dynamical.org.

NOAA GEFS forecast, 35 day - Weather forecasts from the Global Ensemble Forecast System (GEFS) operated by NOAA NWS NCEP.
NOAA GEFS analysis - Weather analysis from the Global Ensemble Forecast System (GEFS) operated by NOAA NWS NCEP.

NOAA Global Forecast System (GFS) netCDF Formatted Data

The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). Dozens of atmospheric and land-soil variables are available through this dataset, from temperatures, winds, and precipitation to soil moisture and atmospheric ozone concentration. The GFS data files stored here can be immediately used for OAR/ARL’s NOAA-EPA Atmosphere-Chemistry Coupler Cloud (NACC-Cloud) tool, and are in a Network Common Data Form (netCDF), which is a very common format used across the scientific community. These particular GFS files contain a comprehensive number of global atmosphere/land variables at a relatively high spatiotemporal resolution (approximately 13x13 km horizontal, vertical resolution of 127 levels, and hourly), are not only necessary for the NACC-Cloud tool to adequately drive community air quality applications (e.g., U.S. EPA’s Community Multiscale Air Quality model; https://www.epa.gov/cmaq), but can be very useful for a myriad of other applications in the Earth system modeling communities (e.g., atmosphere, hydrosphere, pedosphere, etc.). While many other data file and record formats are indeed available for Earth system and climate research (e.g., GRIB, HDF, GeoTIFF), the netCDF files here are advantageous to the larger community because of the comprehensive, high spatiotemporal information they contain, and because they are more scalable, appendable, shareable, self-describing, and community-friendly (i.e., many tools available to the community of users). Out of the four operational GFS forecast cycles per day (at 00Z, 06Z, 12Z and 18Z) this particular netCDF dataset is updated daily (/inputs/yyyymmdd/) for the 12Z cycle and includes 24-hr output for both 2D (gfs.t12z.sfcf$0hh.nc) and 3D variables (gfs.t12z.atmf$0hh.nc).

Also available are netCDF formatted Global Land Surface Datasets (GLSDs) developed by Hung et al. (2024). The GLSDs are based on numerous satellite products, and have been gridded to match the GFS spatial resolution (~13x13 km). These GLSDs contain vegetation canopy data (e.g., land surface type, vegetation clumping index, leaf area index, vegetative canopy height, and green vegetation fraction) that are supplemental to and can be combined with the GFS meteorological netCDF data for various applications, including NOAA-ARL's canopy-app. The canopy data variables are climatological, based on satellite data from the year 2020, combined with GFS meteorology for the year 2022, and are created at a daily temporal resolution (/inputs/geo-files/gfs.canopy.t12z.2022mmdd.sfcf000.global.nc)

NOAA HRRR - dynamical.org Icechunk Zarr

The High-Resolution Rapid Refresh (HRRR) is a NOAA real-time 3-km resolution, hourly updated, cloud-resolving, convection-allowing atmospheric model, initialized by 3km grids with 3km radar assimilation. Radar data is assimilated in the HRRR every 15 min over a 1-h period adding further detail to that provided by the hourly data assimilation from the 13km radar-enhanced Rapid Refresh.

These datasets have been translated to cloud-optimized Icechunk Zarr format by dynamical.org.

NOAA HRRR forecast, 48 hour - Weather forecasts from the High-Resolution Rapid Refresh (HRRR) model operated by NOAA NWS NCEP.
NOAA HRRR analysis - Analysis data from the High-Resolution Rapid Refresh (HRRR) model operated by NOAA NWS NCEP.

NOAA JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem (J-SCOPE)

J-SCOPE (JISAO’s Seasonal Coastal Ocean Prediction of the Ecosystem) is funded by NOAA and presented by NANOOS. This project aims to provide experimental seasonal forecasts (six to nine months) of upper ocean properties, based on operational simulations by NOAA's Climate Forecast System (CFS) model, and dynamical downscaling with a high-resolution version of the Regional Ocean Model System (ROMS) that includes a state-of-the-art biogeochemical module. Forecasts of specific oceanic properties crucial to the nearshore and coastal marine ecosystem such as upwelling, pH, mixed layer depth, oxygen concentration and plankton distributions are anticipated with updates on a monthly basis. For more information about the forecast system, please read Siedlecki et al. 2016.The Regional Ocean Modeling System (ROMS; Rutgers version 3) is configured for the Washington and Oregon coasts after Giddings et al. More information about ROMS can be found here. The Cascadia domain was developed by the Coastal Modeling Group at the UW is roughly 1.5 km in resolution. More information about the model physics can be found here. Our implementation of ROMS includes 17 rivers forced with daily river discharge and temperature data from the USGS gauging stations and an Environment Canada gauging station for the Fraser River as described by Giddings et al. Tides are included. Water entering the domain at the southern and western boundaries is supplied by CFS. Empirical relationships were derived relating nutrients and oxygen to salinity from the observations of Connolly et al (2010) as described by Davis et al and Siedlecki et al. The rivers enter the domain with constant saturated values of oxygen and a seasonal cycle for nutrients from a climatology of USGS gauging stations data described by Davis et al. In the hindcast simulations, the rivers are based on observed streamflows, but in the re-forecast and forecast runs, the rivers are forced using a climatology of local river discharge data over seven years (2000-2007).

The forecast system predicts the sea-surface temperature (SST), chlorophyll stock, dissolved oxygen concentration, carbon variables, and sardine as well as hake habitat. Each forecast is composed of three model runs that make up an ensemble. Each model run is initialized at a different time within the initialization month (for April, for example, April 6, April 15, April 25), and has complementary forcing files from the large scale model, CFS. J-SCOPE produces January, April, and September initialized forecasts annually.

The details of the wind forcing for each model run can be found on the California Current Indicators tab. For each of the predicted quantities listed above, we report the ensemble average anomaly as well as the relative uncertainty within the ensemble, which is defined as the standard deviation of the ensemble divided by the mean of the ensemble and is reported as a percentage of the mean. All of these quantities are reported as monthly averaged anomalies from our April-initialized reforecast climatology, which spans 2009-2017. An anomaly is an indication of how different conditions are to what they have been in the past. For more information about anomalies, please see the NANOOS Climatology App. These predicted quantities are key indicators for the California Current Integrated Ecosystem Assessment report.

J-SCOPE has also been run as a re-forecast and a hindcast each spanning 1998 to present and also rely on CFS forcing. The re-forecasts extend to 2013 when the true forecasts began. The historical output is available daily while the re-forecasts and forecasts are available as monthly averages. The historical simulation is used to evaluate the Year-in Review and is released after the year is over in the early part of the following year.

NOAA S-104 Water Level Data

S-104 is a data and metadata encoding specification that is part of the S-100 Universal Hydrographic Data Model, an international standard for hydrographic data. This collection of data contains water level forecast guidance from NOAA's Global Surge and Tide Operational Forecast System 2-D (STOFS-2D-Global), an operational hydrodynamic nowcast and forecast modeling system for global water level conditions. These datasets are encoded as HDF-5 files conforming to the S-104 specification, and are geospatially subset into individual tiles conforming to the NOAA/OCS Nautical Product Tiling Scheme, with filenames indicating the corresponding NOAA Electronic Navigational Chart (ENC) Cell Identifier. A set of prototype S-104 tiles has been created for the Charleston, SC area for a select model run cycle. Each individual S-104 (HDF-5) file contains all forecast projections from a single model run for that geographic area. A single S-104 file will contain multiple gridded arrays each containing a forecast valid at a distinct time in the future, out to the forecast horizon of STOFS-2D-Global, which is 180 hours or 7.5 days. The water level forecast guidance includes the combined effects of storm surge (sub-tidal) and tides (astronomical tide predictions).

NOAA Unified Forecast System Subseasonal to Seasonal Prototypes

The Unified Forecast System Subseasonal to Seasonal prototypes consist of reforecast data from the UFS atmosphere-ocean coupled model experimental prototype version 5, 6, 7, and 8 produced by the Medium Range and Subseasonal to Seasonal Application team of the UFS-R2O project. The UFS prototypes are the first dataset released to the broader weather community for analysis and feedback as part of the development of the next generation operational numerical weather prediction system from NWS. The datasets includes all the major weather variables for atmosphere, land, ocean, sea ice, and ocean waves.

Acknowledgment - The Unified Forecast System (UFS) atmosphere-ocean coupled model experimental version # data used in this study are made available through the UFS Research to Operations (UFS-R2O) project sponsored by the National Weather Service (NWS) Office of Science and Technology Integration (OSTI) Modeling Program Division and the National Oceanic and Atmospheric Administration (NOAA) Oceanic and Atmospheric Research (OAR) Weather Program Office (WPO).

NOAA World Ocean Database (WOD)

The World Ocean Database (WOD) is the largest uniformly formatted, quality-controlled, publicly available historical subsurface ocean profile database. From Captain Cook's second voyage in 1772 to today's automated Argo floats, global aggregation of ocean variable information including temperature, salinity, oxygen, nutrients, and others vs. depth allow for study and understanding of the changing physical, chemical, and to some extent biological state of the World's Oceans. Browse the bucket via the AWS S3 explorer: https://noaa-wod-pds.s3.amazonaws.com/index.html

NUVIEW - Multi-State Geospatial Data

NUVIEW hosts and manages a unified collection of geospatial datasets from multiple U.S. states and agencies (LiDAR, orthophoto imagery, DEM/DSM, and derivative products). Data are organized in a single S3 bucket with a logical sub-folder hierarchy: /state_or_agency_product_type/acqusition_project_name/.... All assets are cloud-optimized (COG GeoTIFFs, COPC (Cloud Optimized Point Cloud) LAZ point clouds, etc.) and available under open licenses.

National Archives Catalog

The National Archives Catalog dataset contains all of the descriptions; authority records; digitized and electronic records; and tags, transcriptions and comments for NARA’s archival holdings available in the Catalog.

National Cancer Institute Center for Cancer Research - Diffuse Large B Cell Lymphoma (DLBCL) Genomics and Expression

The study describes integrative analysis of genetic lesions in 574 diffuse large B cell lymphomas (DLBCL) involving exome and transcriptome sequencing, array-based DNA copy number analysis and targeted amplicon resequencing. The dataset contains open RNA-Seq Gene Expression Quantification data.

Nighttime-Fire-Flare

Detection of nighttime combustion (fire and gas flaring) from daily top of atmosphere data from NASA's Black Marble VNP46A1 product using VIIRS Day/Night Band and VIIRS thermal bands.

OPERA Coregistered Single-Look Complex from Sentinel-1 Static Layers validated product (Version 1)

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 (S1) Static Layers (CSLC-S1-STATIC) validated product contains static radar geometry layers associated with the OPERA Coregistered Single-Look Complex (CSLC) from Sentinel-1 (S1) validated product. Due to the S1 mission’s narrow orbital tube, radar-geometry layers vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA CSLC-S1 product, as they are produced only once or a limited number of times, to account for changes in the DEM, in the S1 orbit, or in the static layers generation algorithm. Each OPERA CSLC-S1-STATIC product is distributed as a Hierarchical Data Format version 5 (HDF5) file following the CF-1.8 convention containing both data raster layers and product metadata and corresponds to matching CSLC-S1 products with the same burst ID. OPERA CSLC-S1 products are available over North America which includes the USA and U.S. Territories, Canada within 200 km of the U.S. border, and all mainland countries from the southern U.S. border down to and including Panama. The CSLC-S1 products are available in the associated OPERA Coregistered Single-Look Complex from Sentinel-1 validated product (Version 1) dataset. Read our doc on how to get AWS Credentials to retrieve this data: https://cumulus.asf.alaska.edu/s3credentialsREADME

OPERA Coregistered Single-Look Complex from Sentinel-1 validated product (Version 1)

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Coregistered Single-Look Complex (CSLC) from Sentinel-1 validated product consists of Single Look Complex (SLC) images which contain both amplitude and phase information of the complex radar return. The amplitude is primarily determined by ground surface properties (e.g., terrain slope, surface roughness, and physical properties), and phase primarily represents the distance between the radar and ground targets corrected for the geometrical distance between the two based on the knowledge from Digital Elevation Model and platform’s position, i.e., the CSLC phase represents residual geometrical distance between the sensor and target, the atmospheric propagation delay and the target movements. The CSLC-S1 product is derived from Copernicus Sentinel-1A and Sentinel-1B Interferometric Wide (IW) SLC data. The CSLC images are precisely aligned or “coregistered” to a pre-defined UTM/Polar stereographic map projection systems and posted at 5x10 m spacing in east and north direction, respectively. Each CSLC-S1 product corresponds to a single S1 burst and is distributed as a Hierarchical Data Format version 5 (HDF5) file following the CF-1.8 convention containing both data raster layers (e.g., geocoded complex backscatter, low-resolution correction look-up tables) and product metadata. OPERA CSLC-S1 products are available over North America which includes the USA and U.S. Territories, Canada within 200 km of the U.S. border, and all mainland countries from the southern U.S. border down to and including Panama. The OPERA CSLC-S1 product contains modified Copernicus Sentinel data (2016-2025).Due to the S1 mission’s narrow orbital tube, radar-geometry layers vary slightly over time for each position on the ground, and therefore are considered static. These static layers are provided separately from the OPERA CLSLC-S1 product, as they are produced only once or a limited number of times. The static layers are available in the associated OPERA Coregistered Single-Look Complex from Sentinel-1 Static Layers validated product (Version 1). Read our doc on how to get AWS Credentials to retrieve this data: https://cumulus.asf.alaska.edu/s3credentialsREADME

OPERA Dynamic Surface Water Extent from Sentinel-1 (Version 1)

This dataset contains Level-3 Dynamic OPERA Surface Water Extent from Sentinel-1 (DSWx-S1) product version 1. DSWx-S1 provides near-global geographical mapping of surface water extent over land at a spatial resolution of 30 meters over the Military Grid reference System (MGRS) grid system, with a temporal revisit frequency between 6-12 days. Using Sentinel-1 radar observations, DSWx-S1 maps open inland water bodies greater than 3 hectares and 200 meters in width, irrespective of cloud conditions and daylight illumination that often pose challenges to optical sensors. Forward production of the DSWx-S1 data record began in Sept 2024. Each product is distributed as a set of 3 GeoTIFF (Geographic Tagged Image File Format) files including water classification and associated confidence layers.

The OPERA DSWx-S1 product contains modified Copernicus Sentinel data (2024-2025).

To access the calibration/validation database for OPERA Dynamic Surface Water Extent Products, please contact podaac@podaac.jpl.nasa.gov Read our doc on how to get AWS Credentials to retrieve this data: https://archive.podaac.earthdata.nasa.gov/s3credentialsREADME

OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 product (Version 1)

The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) product Version 1 maps vegetation disturbance alerts that are derived from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C Multi-Spectral Instrument (MSI). A vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The Level-3 data product also provides additional information about more general disturbance trends and auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes. HLS data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration or small magnitude/long duration.The OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within the DIST-ALERT product. The layers for both vegetation and generic disturbance include disturbance status, loss or anomaly, maximum loss anomaly, disturbance confidence layer, date of disturbance, count of observations with loss anomalies, days of ongoing anomalies, and day of last disturbance detection. Additional layers are vegetation cover percent, historical percent vegetation cover, and data mask. See the Product Specification Document (PSD) for a more detailed description of the individual layers provided in the DIST-ALERT product.The OPERA_L3_DIST-ALERT-HLS product contains modified Copernicus Sentinel data (2020-2025).Known Issues

Additional usage constraints are provided under Section 5 of the Algorithm Theoretical Basis Document (ATBD). Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

OPERA Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 provisional product (Version 0)

The OPERA_L3_DIST-ALERT-HLS Version 0 data product was decommissioned on April 25, 2025. Users are encouraged to use the OPERA_L3_DIST-ALERT-HLS V1 data product which was released on March 14, 2024, and has achieved stage 1 validation.The Observational Products for End-Users from Remote Sensing Analysis (OPERA) Land Surface Disturbance Alert from Harmonized Landsat Sentinel-2 (HLS) provisional data product Version 0 maps vegetation disturbance alerts from data collected by Landsat 8 and Landsat 9 Operational Land Imager (OLI) and Sentinel-2A, Sentinel-2B, and Sentinel-2C Multi-Spectral Instrument (MSI). Vegetation disturbance alert is detected at 30 meter (m) spatial resolution when there is an indicated decrease in vegetation cover within an HLS pixel. The product also provides auxiliary generic disturbance information as determined from the variations of the reflectance through the HLS scenes to provide information about more general disturbance trends. HLS data represent the highest temporal frequency data available at medium spatial resolution. The combined observations will provide greater sensitivity to land changes, whether of large magnitude/short duration, or small magnitude/long duration. The OPERA_L3_DIST-ALERT-HLS (or DIST-ALERT) data product is provided in Cloud Optimized GeoTIFF (COG) format, and each layer is distributed as a separate file. There are 19 layers contained within in the DIST-ALERT product: vegetation disturbance status, current vegetation cover indicator, current vegetation anomaly value, historical vegetation cover indicator, max vegetation anomaly value, vegetation disturbance confidence layer, date of initial vegetation disturbance, number of detected vegetation loss anomalies, and vegetation disturbance duration. See the Product Specification for a more detailed description of the individual layers provided in the DIST-ALERT product. Known Issues

Additional usage constraints are provided under Section 5 of the Algorithm Theoretical Basis Document (ATBD). Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

OS-Climate Physrisk

Collection of adapted and derived hazard indicator datasets optimized for running physical climate risk analyses.

Ocean Radar - Newcastle site - Sea water velocity - Delayed mode

The Newcastle (NEWC) HF ocean radar system covers an area of the Central Coast, New South Wales, an area subject to the variability of the East Australian Current (EAC) and its coupling with coastal winds, tides, and waves. In this area the EAC separates from the coast and generates several eddies which control the larval species and the higher marine species and ecosystems in which they forage.The NEWC HF ocean radar system consists of two SeaSonde crossed loop direction finding stations located at Sea Rocks (32.441575 S 152.539022 E) and Red Head (33.010245 S 151.727059 E). These radars operate at a frequency of 5.2625 MHz, with a bandwidth of 25 KHz, a maximum range of 200 Km and a range resolution of 6 Km. Within the HF radar coverage area surface currents are measured.

OpenCRAVAT

OpenCRAVAT is a module variant annotation tool developed by KarchinLab at Johns Hopkins. This dataset is a mirror of the OpenCRAVAT store available at https://store.opencravat.org. You can configure OpenCRAVAT to use this mirror by editing the "cravat-system.yml" file. The path to this file is in the first output line of the command "oc config system". In that file, change the value of "store_url" to "https://opencravat-store-aws.s3.amazonaws.com".

Oregon Health & Science University Chronic Neutrophilic Leukemia Dataset

The OHSU-CNL study offers the whole exome and RNA-sequencing on a cohort of 100 cases with rare hematologic malignancies such as Chronic neutrophilic leukemia (CNL), atypical chronic myeloid leukemia (aCML), and unclassified myelodysplastic syndrome/myeloproliferative neoplasms (MDS/MPN-U). This dataset contains open RNA-Seq Gene Expression Quantification data.

PALSAR-2 ScanSAR Turkey & Syria Earthquake (L2.1 & L1.1)

JAXA has responded to the Earthquake events in Turkey and Syria by conducting emergency disaster observations and providing data as requested by the Disaster and Emergency Management Authority (AFAD), Ministry of Interior in Turkey, through Sentinel Asia and the International Disaster Charter. Additional information on the event and dataset can be found here. The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broad area observation mode with observation width of 350 km. Polarization data are stored as 16-bit digital numbers (DN). The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation: γ0 = 10*log10(DN2) - 83.0 dB. Included in this dataset are ALOS PALSAR Level 1.1 and 2.1 data. Level 1.1 is range and single look azimuth compressed data represented by complex I and Q channels to preserve the magnitude and phase information. Range coordinate is in slant range. In the case of ScanSAR mode, an image file is generated per each scan. Level 2.1 data is orthorectified from level 1.1 data by using digital elevation model. Pixel spacing is selectable depending on observation modes. Image coordinate in map projection is geocoded.

Pancreatic Cancer Organoid Profiling

This study generated a collection of patient-derived pancreatic normal and cancer organoids and it was sequenced using Whole Genome Sequencing (WGS), Whole Exome Sequencing (WXS) and RNA-Seq as well as matched tumor and normal tissue if available. The study provides a valuable resource for pancreatic cancer researchers. The dataset contains open RNA-Seq Gene Expression Quantification data and controlled WGS/WXS/RNA-Seq Aligned Reads, WXS Annotated Somatic Mutation, WXS Raw Somatic Mutation, and RNA-Seq Splice Junction Quantification.

RAPID NRT Flood Maps

Near Real-time and archival data of High-resolution (10 m) flood inundation dataset over the Contiguous United States, developed based on the Sentinel-1 SAR imagery (2016-current) archive, using an automated Radar Produced Inundation Diary (RAPID) algorithm.

REDASA COVID-19 Open Data

The REaltime DAta Synthesis and Analysis (REDASA) COVID-19 snapshot contains the output of the curation protocol produced by our curator community. A detailed description can be found in our paper. The first S3 bucket listed in Resources contains a large collection of medical documents in text format extracted from the CORD-19 dataset, plus other sources deemed relevant by the REDASA consortium. The second S3 bucket contains a series of documents surfaced by Amazon Kendra that were considered relevant for each medical question asked. The final S3 bucket contains the GroundTruth annotations created by our curator community.

RNA structure by fragmentation frequency

The fragSTRUC project devises a software to extract RNA secondary structure information from Illumina datasets, based on divalent ions in standard RNA-seq library preparation fragmenting sequences at non-base-paired regions of RNA.

Reference Indexes for krepp

krepp is an alignment-free method for estimating distances and phylogenetic placement of individual reads to many thousands of reference genomes in a scalable manner using k-mers. This dataset includes k-mer-based indexes consisting of ultra-large reference genome sets that can be efficiently analyzed using krepp.

Reference data for HiFi human WGS

Reference data bundle for analyzing HiFi human whole genome sequencing data

SatPM2.5

Fine particulate matter (PM2.5) concentrations are estimated using information from satellite-, simulation- and monitor-based sources. Aerosol optical depth from multiple satellites (MODIS, VIIRS, MISR, SeaWiFS, and VIIRS) and their respective retrievals (Dark Target, Deep Blue, MAIAC) is combined with simulation (GEOS-Chem) based upon their relative uncertainties as determined using ground-based sun photometer (AERONET) observations to produce geophysical estimates that explain most of the variance in ground-based PM2.5 measurements. A subsequent statistical fusion incorporates additional information from ground-based PM2.5 measurements.

Satellogic EarthView dataset

Satellogic EarthView dataset includes high-resolution satellite images captured over all continents. The dataset is organized in Hive partition format and hosted by AWS. The dataset can be accessed via STAC browser or aws cli. Each item of the dataset corresponds to a specific region and date, with some of the regions revisited for additional data. The dataset provides Top-of-Atmosphere (TOA) reflectance values across four spectral bands (Red, Green, Blue, Near-Infrared) at a Ground Sample Distance (GSD) of 1 meter, accompanied by comprehensive metadata such as off-nadir angles, sun elevation, and other pertinent details. Users should note that due to an artifact in region delineation, a small number of regions present overlaps.

SeeFar V0

A collection of multi-resolution satellite images from both public and commercial satellites. The dataset is specifically curated for training geospatial foundation models.

Sentinel-1 SLC dataset for South and Southeast Asia, Taiwan, Korea and Japan

The S1 Single Look Complex (SLC) dataset contains Synthetic Aperture Radar (SAR) data in the C-Band wavelength. The SAR sensors are installed on a two-satellite (Sentinel-1A and Sentinel-1B) constellation orbiting the Earth with a combined revisit time of six days, operated by the European Space Agency. The S1 SLC data are a Level-1 product that collects radar amplitude and phase information in all-weather, day or night conditions, which is ideal for studying natural hazards and emergency response, land applications, oil spill monitoring, sea-ice conditions, and associated climate change effects.

Somatic Mosaicism across Human Tissues (SMaHT)

The Somatic Mosaicism across Human Tissues (SMaHT) project is an NIH Common Fund consortium (2023-) aimed to comprehensively characterize somatic variation ("mosaicism") in normal human tissues. While most genetic studies have relied on blood-derived DNA, SMaHT captures the full spectrum of DNA variation across cell types, tissues, and organs from phenotypically normal individuals to better understand the role of somatic mosaicism in human development, aging, and disease progression.Researchers in the consortium develop and apply experimental and computational methods, paired with the state-of-the-art sequencing technologies, to accurately detect even rare mutations (frequency < 1%) in subpopulations of cells. In addition to generating the production data across ~20 tissue types from 150 post-mortem donors, SMaHT also produces datasets from cell line and tissue homogenate samples, to benchmark and develop new technologies and computational tools for mosaic variant detection.The resulting data include high-coverage whole-genome and transcriptome data using both short-read and long-read sequencing technologies from multiple platforms (e.g., Illumina, PacBio, Oxford Nanopore Technologies, Ultima Genomics). SMaHT will also generate comprehensive genome-wide catalogs of somatic variants. We anticipate that this resource will be valuable not only for researchers studying somatic mosaicism, but also for the broader scientific community interested in large-scale WGS data from normal human tissues. More about the SMaHT project: program announcement, https://commonfund.nih.gov/smaht, and https://smaht.org/. More about the data portal: https://data.smaht.org/ and types of data generated: https://data.smaht.org/about/consortium/data

Sounds of Central African landscapes

Archival soundscapes recorded in the rainforest landscapes of Central Africa, with a focus on the vocalizations of African forest elephants (Loxodonta cyclotis).

State of Colorado Elevation Data

The State of Colorado has gathered public historical elevation data.

Sub-Meter Canopy Tree Height of California in 2020 by CTrees.org

Canopy Tree Height maps for California in 2020. Created using a deep learning model on very-high-resolution airborne imagery from the National Agriculture Imagery Program (NAIP) by United States Department of Agriculture (USDA).

TESS-SPOC

The data products for the TESS-SPOC FFI targets are the same as for the TESS two-minute cadence targets: calibrated target pixel files, simple aperture photometry (SAP) flux time series, presearch data conditioning corrected (PDCSAP) flux time series, and cotrending basis vectors (CBV). Since TESS-SPOC relies on FFIs, data are sampled at the FFI cadence.

TIGER Training

"This dataset contains the training data for the Tumor InfiltratinG lymphocytes in breast cancER or TIGER challenge. TIGER is the first challenge on fully automated assessment of tumor-infiltrating lymphocytes (TILs) in breast cancer histopathology slides. TILs are proving to be an important biomarker in cancer patients as they can play a part in killing tumor cells, particularly in some types of breast cancer. Identifying and measuring TILs can help to better target treatments, particularly immunotherapy, and may result in lower levels of other more aggressive treatments, including chemotherapy."

Tabula Sapiens

Tabula Sapiens is a benchmark, first-draft human cell atlas of over 1.1M cells from 28 organs of 24 normal human subjects. This work is the product of the Tabula Sapiens Consortium. Taking the organs from the same individual controls for genetic background, age, environment, and epigenetic effects, and allows detailed analysis and comparison of cell types that are shared between tissues. Our work creates a detailed portrait of cell types as well as their distribution and variation in gene expression across tissues and within the endothelial, epithelial, stromal and immune compartments. We have built directly on our unique skills, experience, and data infrastructure from Tabula Muris and Tabula Muris Senis to create a high-quality human reference dataset and portal at a 10-fold larger scale from these prior efforts.A critical factor in the Tabula projects is our large collaborative network of PIs with deep expertise at preparation of diverse organs, enabling all organs from a subject to be successfully processed within a single day. We have built the logistics and infrastructure capable of tracking hundreds of samples and thousands of 384-well plates from tissue through sample prep, library construction and on to sequencing and ultimately computational and expert cell annotation with tight quality control.Tabula Sapiens leverages our network of human tissue experts and a close collaboration with a Donor Network West, a not-for-profit organ procurement organization. We use their experience to balance and assign cell types from each tissue compartment and optimally mix high-quality plate-seq data and high-volume droplet-based data to provide a broad and deep benchmark atlas.The first version of Tabula Sapiens contained nearly 500,000 cells from 24 organs of 15 normal human subjects. With the second version, Tabula Sapiens 2.0, we have built an integrated map of 28 tissues collected across 24 donors. Nine new donors and four new tissues were collected and analyzed together with the original Tabula Sapiens 1.0 dataset. All tissues and organs, with the exception of the respective reproductive organs, were profiled from both male (n=11) and female (n=13) donors. The donor's age ranges from 22 to 74 years old, offering one of the most comprehensive molecular profiles of human tissues across the adult lifespan (7 donors under the age of 40, 11 donors between 40 and 60, and 6 donors over 60 years of age).Our goal is to make sequence data rapidly and broadly available to the scientific community as a community resource and we welcome collaborative interaction on the project and analyses. By accessing these data, you agree to cite our work as The Tabula Sapiens Consortium, Science 376, eabl4896 (2022) if using the original v1 dataset, or to cite our work as The Tabula Sapiens Consortium, biorxiv (2024) if you are using the Tabula Sapiens v2 data release. All processed data is available (see below) and the raw data is browsable from AWS. Redistribution of these data should include the full text of the data use policy.If you wish to gain access to the raw fastq files, please submit a request using the controlled access form to sign the data use agreement, upon which we may grant you access to the files. Raw fastq files should not be transferred to any third party.

Terra Fusion Data Sampler

The Terra Basic Fusion dataset is a fused dataset of the original Level 1 radiances from the five Terra instruments. They have been fully validate to contain the original Terra instrument Level 1 data. Each Level 1 Terra Basic Fusion file contains one full Terra orbit of data and is typically 15 – 40 GB in size, depending on how much data was collected for that orbit. It contains instrument radiance in physical units; radiance quality indicator; geolocation for each IFOV at its native resolution; sun-view geometry; bservation time; and other attributes/metadata. It is stored in HDF5, conformed to CF conventions, and accessible by netCDF-4 enhanced models. It’s naming convention follows: TERRA_BF_L1B_OXXXX_YYYYMMDDHHMMSS_F000_V000.h5. A concise description of the dataset, along with links to complete documentation and available software tools, can be found on the Terra Fusion project page: https://terrafusion.web.illinois.edu.

Terra is the flagship satellite of NASA’s Earth Observing System (EOS). It was launched into orbit on December 18, 1999 and carries five instruments. These are the Moderate-resolution Imaging Spectroradiometer (MODIS), the Multi-angle Imaging SpectroRadiometer (MISR), the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), the Clouds and Earth’s Radiant Energy System (CERES), and the Measurements of Pollution in the Troposphere (MOPITT).

The Terra Basic Fusion dataset is an easy-to-access record of the Level 1 radiances for instruments on the Terra mission for selected WRS-2 paths covering the years 2000-2015. These paths are Paths 20-26 (e.g., US corn belt), 108 (e.g., Japan), 125 (e.g., China), 143 (e.g., India), 150 (e.g., Showa Station, Antarctica), 169 (e.g., Europe and Africa), 188 (e.g., Nigeria calibration site), and 233 (e.g., Greenland).

Transiting Exoplanet Survey Satellite (TESS)

The Transiting Exoplanet Survey Satellite (TESS) is a multi-year survey that has discovered exoplanets in orbit around bright stars across the entire sky using high-precision photometry. The survey also enables a wide variety of stellar astrophysics, solar system science, and extragalactic variability studies. More information about TESS is available at MAST and the TESS Science Support Center.

Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED)

The Tropical Cyclone Precipitation, Infrared, Microwave and Environmental Dataset (TC PRIMED) is a dataset centered around passive microwave observations of global tropical cyclones from low-Earth-orbiting satellites. TC PRIMED is a compilation of tropical cyclone data from various sources, including 1) tropical cyclone information from the National Oceanic and Atmospheric Administration (NOAA) National Weather Service National Hurricane Center (NHC) and Central Pacific Hurricane Center (CPHC) and the U.S. Department of Defense Joint Typhoon Warning Center, 2) low-Earth-orbiting satellite observations and products from the National Aeronautics and Space Administration (NASA) Global Precipitation Measurement (GPM) mission constellation satellites, and 3) environmental fields and diagnostics calculated from the European Center for Medium-Range Weather Forecasts (ECMWF) fifth-generation reanalysis (ERA5).

UMASSD-FVCOM-GOM3-Hindcast

The Finite Volume Community Ocean Model (FVCOM) was used to simulate ocean water levels, velocity, temperature and salinity over a multi-decadal period (1984-present) in the waters of the Northeast US including the Gulf of Maine. The model was configured and run by the Dr. Changshen Chen, Director of the Marine Ecosystems Dynamics Modeling Laboratory in the School for Marine Science & Technology at the University of Massachusetts Dartmouth. The triangular mesh has a varying horizontal resolution from several hundred meters inshore to several kilometers offshore, and 45 terrain-following vertical layers. The model output was saved at hourly intervals from 2009-08-21 to 2022-06-17.

USGS COAWST (Coupled Ocean Atmosphere Wave and Sediment Transport) Forecast Model Archive, US East and Gulf Coasts

The COAWST modeling system has been used to simulate ocean, wave and sediment transport processes along the of US East Coast and Gulf of Mexico. The grid has a horizontal resolution of approximately 5km and is resolved with 16 vertical terrain following levels. The model has been executed on a daily basis since August 2009 with outputs written every hour. This archive contains model output from 2009-08-21 to 2022-06-17.

UniProt

The Universal Protein Resource (UniProt) is a comprehensive resource for protein sequence and annotation data. The UniProt databases are the UniProt Knowledgebase (UniProtKB), the UniProt Reference Clusters (UniRef), and the UniProt Archive (UniParc). The UniProt consortium and host institutions EMBL-EBI, SIB Swiss Institute of Bioinformatics and PIR are committed to the long-term preservation of the UniProt databases.

Whiffle WINS50 Open Data on AWS

Large Eddy Simulation (LES) data of the Winds of the North Sea in 2050 (WINS50) project.

3-Band Cryo Data | Wide-field Infrared Survey Explorer (WISE)

The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The 3-Band Cryo Data Release contains 3.4, 4.6 and 12 micron (W1, W2, W3) imaging data that were acquired between 6 Aug and 29 Sept 2010 while the detectors were cooled by the inner cryogen tank following the exhaustion of the outer tank.

3DCoMPaT: Composition of Materials on Parts of 3D Things

3D CoMPaT is a richly annotated large-scale dataset of rendered compositions of Materials on Parts of thousands of unique 3D Models. This dataset primarily focuses on stylizing 3D shapes at part-level with compatible materials. Each object with the applied part-material compositions is rendered from four equally spaced views as well as four randomized views. We introduce a new task, called Grounded CoMPaT Recognition (GCR), to collectively recognize and ground compositions of materials on parts of 3D objects. We present two variations of this task and adapt state-of-art 2D/3D deep learning methods to solve the problem as baselines for future research. We hope our work will help ease future research on compositional 3D Vision.

A2D2: Audi Autonomous Driving Dataset

An open multi-sensor dataset for autonomous driving research. This dataset comprises semantically segmented images, semantic point clouds, and 3D bounding boxes. In addition, it contains unlabelled 360 degree camera images, lidar, and bus data for three sequences. We hope this dataset will further facilitate active research and development in AI, computer vision, and robotics for autonomous driving.

ABoVE: Bias-Corrected IMERG Monthly Precipitation for Alaska and Canada, 2000-2020

This dataset is a modification to the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run microwave-only, daily precipitation Version 06 data. It provides bias-corrected IMERG monthly precipitation data for Alaska and Canada from June 2000 through December 2020 in Cloud-Optimized GeoTIFF (*.tif) format. Data are provided in the units of mm/day. NASA's IMERG data product is one of the most advanced satellite precipitation products with a 0.1-degree spatial resolution and near global coverage. This dataset bias-corrected IMERG's HQprecipitation precipitation estimates, which are based on passive microwave (PMW)-only retrievals, using a linear regression method. This method utilizes empirical measurements from rain gauge stations from the Global Historical Climatology Network (GHCN) and a digital elevation model. This bias correction approach improves estimates at elevations above 500 m a.s.l., which are typically underestimated. Read our doc on how to get AWS Credentials to retrieve this data: https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME

AI2 Diagram Dataset (AI2D)

4,817 illustrative diagrams for research on diagram understanding and associated question answering.

AI2 Meaningful Citations Data Set

630 paper annotations

AI2 Reasoning Challenge (ARC) 2018

7,787 multiple choice science questions and associated corpora

AIRS/Aqua L1B Infrared (IR) geolocated and calibrated radiances V005 (AIRIBRAD) at GES DISC

WARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space.The thermal impact of the DSM caused a shift of the centroids of spectral response functions (SRF) of about 1% of the width of the SRF, equivalent to a frequency shift of 9 parts per million. This shift is reflected in the “spectral_freq” parameter (observed frequencies) in the L1b v5 files for each 6 minute granule. The magnitude of the effect on brightness temperatures (BT) depends on the spectral gradient of each channel. Maximum BT shifts are approximately +- 0.5 K, although many channels experience far smaller BT shifts. Approximately 1803 channels have BT shifts of less than 0.1 K and 575 channels are now shifted in BT by more than 0.1 K, while 231 of these channels have BT shifts greater than 0.2 K.Users of the L1b v5 product who are concerned that these shifts may impact their science investigations and applications are encouraged to switch to the AIRS L1c v6.7.4 product, which, among many other improvements, converts the spectra to a fixed frequency grid. END OF WARNING.The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1B data set contains AIRS calibrated and geolocated radiances in milliWatts/m^2/cm^-1/steradian for 2378 infrared channels in the 3.74 to 15.4 micron region of t he spectrum. The AIRS instrument is co-aligned with AMSU-A so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. The AIRIBRAD_005 products are stored in files (often referred to as "granules") that contain 6 minutes of data, 90 footprints across track by 135 lines along track. Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

ARCO-OCEAN

ARCO-OCEAN is an analysis-ready cloud-optimized dataset providing physical properties of the ocean, waves, and sea ice for a period of about 28 years between the 1st of January 1993 and the 30th of June 2021. The dataset includes also atmospheric and hydrological variables that would be needed as boundary conditions and used to drive a numerical simulation. The dataset is the result of collecting, processing, merging and optimizing for the cloud different data sources, all retrospective analyses (reanalyses) or hindcasts of different Earth system components. The dataset has been designed with machine learning in mind, and takes inspiration from similar datasets derived from ERA5.

ARPA-E PERFORM Forecast data

The ARPA-E PERFORM Program is an ARPA-E funded program that aim to use time-coincident power and load seeks to develop innovative management systems that represent the relative delivery risk of each asset and balance the collective risk of all assets across the grid. A risk-driven paradigm allows operators to: (i) fully understand the true likelihood of maintaining a supply-demand balance and system reliability, (ii) optimally manage the system, and (iii) assess the true value of essential reliability services. This paradigm shift is critical for all power systems and is essential for grids with high levels of stochastic resources. Projects will propose methods to quantify and manage risk at the asset level and at the system level.In support of the ARPA-E PERFORM project, NREL has produced a set of time-coincident load, wind, and solar generation profiles, including actual and forecasting time series. Both actuals and forecasts are provided in form of time-series with high temporal and spatial fidelity. Both deterministic and probabilistic forecasts are contained in the dataset.

ASTER Level 1T Precision Terrain Corrected Registered At-Sensor Radiance V004

The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Level 1 Precision Terrain Corrected Registered At-Sensor Radiance (AST_L1T) data contains calibrated at-sensor radiance, which corresponds with the ASTER Level 1B (AST_L1B) that has been geometrically corrected and rotated to a north-up UTM projection. The AST_L1T is created from a single resampling of the corresponding ASTER L1A (AST_L1A) product. The bands available in the AST_L1T depend on the bands in the AST_L1A and can include up to three Visible and Near Infrared (VNIR) bands, six Shortwave Infrared (SWIR) bands, and five Thermal Infrared (TIR) bands. The AST_L1T dataset does not include the aft-looking VNIR band 3. The AST_L1T product has a spatial resolution of 15 meters (m) for the VNIR bands, 30 m for the SWIR bands, and 90 m for the TIR bands.The precision terrain correction process incorporates GLS2000 digital elevation data with derived ground control points (GCPs) to achieve topographic accuracy for all daytime scenes where correlation statistics reach a minimum threshold. Alternate levels of correction are possible (systematic terrain, systematic, or precision) for scenes acquired at night or that otherwise represent a reduced quality ground image (e.g., cloud cover).For daytime images, if the VNIR or SWIR telescope collected data and precision correction was attempted, each precision terrain corrected image will have an accompanying independent quality assessment. It will include the geometric correction available for distribution as both a text file and single band browse images with the valid GCPs overlaid.This multi-file product also includes georeferenced full resolution browse images. The number of browse images and the band combinations of the images depends on the bands available in the corresponding AST_L1A dataset.Known Issues

Since October 1, 2017, a correction addresses zero-filled scans in low-latitude, ascending orbit (nighttime) TIR data. Additional details are available in the ASTER L1T User Advisory.
Data from the SWIR bands collected after April 2008 may show anomalous saturation and striping. See the ASTER SWIR User Advisory for further information.

Improvements/Changes from Previous Versions

Enhanced Geolocation Accuracy: Version 4 uses Collection 2 Ground Control Points (GCPs) compared against Global Land Survey (GLS) 2000 standards to improve positional accuracy.
Radiometric Calibration Update: Version 4 applies Radiometric Calibration Coefficient Version 5 (RCC V5) to improve the radiometric accuracy of the raw DNs, based on research by Tsuchida and others (2020), published in Remote Sensing. Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

ATLAS/ICESat-2 L2A Global Geolocated Photon Data V006

This data set (ATL03) contains height above the WGS 84 ellipsoid (ITRF2014 reference frame), latitude, longitude, and time for all photons downlinked by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. The ATL03 product was designed to be a single source for all photon data and ancillary information needed by higher-level ATLAS/ICESat-2 products. As such, it also includes spacecraft and instrument parameters and ancillary data not explicitly required for ATL03. Read our doc on how to get AWS Credentials to retrieve this data: https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME

ATLAS/ICESat-2 L3A Land and Vegetation Height V006

This data set (ATL08) contains along-track heights above the WGS84 ellipsoid (ITRF2014 reference frame) for the ground and canopy surfaces. The canopy and ground surfaces are processed in fixed 100 m data segments, which typically contain more than 100 signal photons. The data were acquired by the Advanced Topographic Laser Altimeter System (ATLAS) instrument on board the Ice, Cloud and land Elevation Satellite-2 (ICESat-2) observatory. Read our doc on how to get AWS Credentials to retrieve this data: https://data.nsidc.earthdatacloud.nasa.gov/s3credentialsREADME

Aging Mouse Brain Epigenetic

Aging is a major risk factor for neurodegenerative diseases, yet underlying epigenetic mechanisms remain unclear. Here, we generated a comprehensive single-nucleus cell atlas of brain aging across multiple brain regions, comprising 132,551 single-cell methylomes and 72,666 joint chromatin conformation-methylome nuclei. Integration with companion transcriptomic and chromatin accessibility data yielded a cross-modality taxonomy of 36 major cell types.

All-Sky Data | Wide-field Infrared Survey Explorer (WISE)

The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The All-Sky Release includes all data taken during the WISE full cryogenic mission phase, 7 January 2010 to 6 August 2010, in the 3.4, 4.6, 12, and 22 micron bands (i.e., W1, W2, W3, W4) that were processed with improved calibrations and reduction algorithms.

AllWISE Data | Wide-field Infrared Survey Explorer (WISE)

The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The AllWISE Data Release combines data from all cryogenic and post-cryogenic survey phases and provides a comprehensive view of the mid-infrared sky. The Images Atlas includes 18,240 FITS image sets at 3.4, 4.6, 12 and 22 microns. The Source Catalog contains position, apparent motion, and flux information for over 747 million objects detected on the Atlas Images.

Allen Brain Observatory - Visual Coding AWS Public Data Set

The Allen Brain Observatory – Visual Coding is a large-scale, standardized survey of physiological activity across the mouse visual cortex, hippocampus, and thalamus. It includes datasets collected with both two-photon imaging and Neuropixels probes, two complementary techniques for measuring the activity of neurons in vivo. The two-photon imaging dataset features visually evoked calcium responses from GCaMP6-expressing neurons in a range of cortical layers, visual areas, and Cre lines. The Neuropixels dataset features spiking activity from distributed cortical and subcortical brain regions, collected under analogous conditions to the two-photon imaging experiments. We hope that experimentalists and modelers will use these comprehensive, open datasets as a testbed for theories of visual information processing.

Allen Institute for Neural Dynamics - Mouse Neuroanatomy and Physiology Data

The Allen Institute for Neural Dynamics (AIND) is committed to FAIR, Open, and Reproducible science. We therefore share all of the raw and derived data we collect publicly with rich metadata, including preliminary data collected during methods development, as near to the time of collection as possible.

Analysis Ready Sentinel-1 Backscatter Imagery

The Sentinel-1 mission is a constellation of C-band Synthetic Aperature Radar (SAR) satellites from the European Space Agency launched since 2014. These satellites collect observations of radar backscatter intensity day or night, regardless of the weather conditions, making them enormously valuable for environmental monitoring. These radar data have been processed from original Ground Range Detected (GRD) scenes into a Radiometrically Terrain Corrected, tiled product suitable for analysis. This product is available over the Contiguous United States (CONUS) since 2017 when Sentinel-1 data became globally available.

Astrophysics Division Galaxy Morphology Benchmark Dataset

Hubble Space Telescope imaging data and associated identification labels for galaxy morphology derived from citizen scientist labels from the Galaxy Zoo: Hubble project.

CANOE (Canadian Aquatic Navigation for Observation of the Environment) Dataset

This autonomous marine navigation dataset includes data from a 360-degree Navtech radar, a 128-beam Ouster OS1 lidar with integrated IMU, a Teledyne Bumblebee stereo camera, Oculus M3000d imaging sonar, motor inputs, and GNSS. This dataset was collected on a lake and reservoir in Ontario, Canada. The intended purpose of this dataset is to enable the development and benchmarking of autonomous navigation algorithms in aquatic environments. In the future, we hope to release localization and odometry benchmarks.

CHIMERA

This dataset contains the training data for the CHIMERA - Combining HIstology, Medical imaging (radiology) and molEcular data for medical pRognosis and diAgnosis challenge. The CHIMERA Challenge aims to advance precision medicine in cancer care by addressing the critical need for multimodal data integration. Despite significant progress in AI, integrating transcriptomics, pathology, and radiology across clinical departments remains a complex challenge. Clinicians are faced with large, heterogeneous datasets that are difficult to analyze effectively. AI has the potential to unify multimodal data, but several technical barriers remain, such as defining appropriate fusion stages and handling missing modalities.

CMS 2008-2010 Data Entrepreneurs’ Synthetic Public Use File (DE-SynPUF) in OMOP Common Data Model

DE-SynPUF is provided here as a 1,000 person (1k), 100,000 person (100k), and 2,300,000 persom (2.3m) data sets in the OMOP Common Data Model format. The DE-SynPUF was created with the goal of providing a realistic set of claims data in the public domain while providing the very highest degree of protection to the Medicare beneficiaries’ protected health information. The purposes of the DE-SynPUF are to:

allow data entrepreneurs to develop and create software and applications that may eventually be applied to actual CMS claims data;
train researchers on the use and complexity of conducting analyses with CMS claims data prior to initiating the process to obtain access to actual CMS data; and,
support safe data mining innovations that may reveal unanticipated knowledge gains while preserving beneficiary privacy. The files have been designed so that programs and procedures created on the DE-SynPUF will function on CMS Limited Data Sets. The data structure of the Medicare DE-SynPUF is very similar to the CMS Limited Data Sets, but with a smaller number of variables. The DE-SynPUF also provides a robust set of metadata on the CMS claims data that have not been previously available in the public domain. Although the DE-SynPUF has very limited inferential research value to draw conclusions about Medicare beneficiaries due to the synthetic processes used to create the file, the Medicare DE-SynPUF does increase access to a realistic Medicare claims data file in a timely and less expensive manner to spur the innovation necessary to achieve the goals of better care for beneficiaries and improve the health of the population.

COVID-19 Genome Sequence Dataset

This repository within the ACTIV TRACE initiative houses a comprehensive collection of datasets related to SARS-CoV-2. The processing of SARS-CoV-2 Sequence Read Archive (SRA) files has been optimized to identify genetic variations in viral samples. This information is then presented in the Variant Call Format (VCF). Each VCF file corresponds to the SRA parent-run's accession ID. Additionally, the data is available in the parquet format, making it easier to search and filter using the Amazon Athena Service. The SARS-CoV-2 Variant Calling Pipeline is designed to handle new data every six hours, with updates to the AWS ODP bucket occurring daily.

COVID-19 Open Research Dataset (CORD-19)

Full-text and metadata dataset of COVID-19 and coronavirus-related research articles optimized for machine readability.

Common Screens

A corpus of web screenshot and metadata data composed of over 70 million websites.

Community Earth System Model v2 ARISE (CESM2 ARISE)

Data from ARISE-SAI Experiments with CESM2

Conformational Space of Short Peptides

Co-managed by Toyoko and the Structural Biology Group at the Universidad Nacional de Quilmes, this dataset allows us to explore the conformational space of all possible peptides using the 20 common amino acids. It consists of a collection of exhaustive molecular dynamics simulations of tripeptides and pentapeptides.

Corn Kernel Counting Dataset

Dataset associated with the March 2021 Frontiers in Robotics and AI paper "Broad Dataset and Methods for Counting and Localization of On-Ear Corn Kernels", DOI: 10.3389/frobt.2021.627009

Coupled Model Intercomparison Project Phase 5 (CMIP5) University of Wisconsin-Madison Probabilistic Downscaling Dataset

The University of Wisconsin Probabilistic Downscaling (UWPD) is a statistically downscaled dataset based on the Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models. UWPD consists of three variables, daily precipitation and maximum and minimum temperature. The spatial resolution is 0.1°x0.1° degree resolution for the United States and southern Canada east of the Rocky Mountains.

The downscaling methodology is not deterministic. Instead, to properly capture unexplained variability and extreme events, the methodology predicts a spatially and temporally varying Probability Density Function (PDF) for each variable. Statistics such as the mean, mean PDF and annual maximum statistics can be calculated directly from the daily PDF and these statistics are included in the dataset. In addition, “standard”, “raw” data is created by randomly sampling from the PDFs to create a “realization” of the local scale given the large-scale from the climate model. There are 3 realizations for temperature and 14 realizations for precipitation.

The directory structure of the data is as follows
[cmip_version]/[scenario]/[climate_model]/[ensemble_member]/
The realizations are as follows
prcp_[realization_number][year].nc temp[realization_number][year].nc
The time mean files averaged over certain year bounds are as follows
prcp_mean[year_bound_1][year_bound_2].nc temp_mean[year_bound_1][year_bound_2].nc
The time-mean Cumulative Distribution Function (CDF) files are as follows
prcp_cdf[year_bound_1][year_bound_2].nc temp_cdf[year_bound_1][year_bound_2].nc
The CDF of the annual maximum precipitation is given for each year in the record prcp_annual_max_cdf[start_year_of_scenario]_[end_year_of_scenario].nc

Crowdsourced Bathymetry

Community provided bathymetry data collected in collaboration with the International Hydrographic Organization.

DWD ICON-EU - dynamical.org Icechunk Zarr

ICON-EU is a regional weather forecast model operated by Deutscher Wetterdienst (DWD), Germany's national meteorological service. ICON-EU is a nested configuration of DWD's global ICON (Icosahedral Non-hydrostatic) model that provides high-resolution forecasts over Europe.

These datasets have been translated to cloud-optimized Icechunk Zarr format by dynamical.org.

DWD ICON-EU forecast, 5 day - High-resolution weather forecasts for Europe from the ICON-EU model operated by Deutscher Wetterdienst (DWD).

Danish Meteorological Institute (DMI) Open Data Forecasts

DMI forecast data consist of various models where each model contains different set of parameters relating to a specific domain like ocean (WAM), storm flooding (DKSS) or weather (HARMONIE)

Defense Meteorology Satellite Program (DMSP) Auroral Particle Flux

The United States Air Force (USAF) Defense Meteorological Satellite Program (DMSP) SSJ precipitating particle instrument measures in-situ total flux and energy distribution of electrons and ions at low earth orbit. These precipitating particles are of interest for space weather operations and research, in part because they produce aurora during normal and very strong geomagnetic storms. This dataset contains both sensor-level raw data (as detailed in Redmon et al. 2017) and a high-level machine-learning-ready data product.

Discrete Reasoning Over the content of Paragraphs (DROP)

The DROP dataset contains 96k Question and Answer pairs (QAs) over 6.7K paragraphs, split between train (77k QAs), development (9.5k QAs) and a hidden test partition (9.5k QAs).

ECMWF AIFS ENS - dynamical.org Icechunk Zarr

The Artificial Intelligence Forecasting System (AIFS) is a data driven forecast model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). AIFS-ENS is the ensemble configuration of AIFS, containing 51 ensemble members. AIFS is trained on ECMWF's ERA5 re-analysis and ECMWF's operational numerical weather prediction (NWP) analyses.

These datasets have been translated to cloud-optimized Icechunk Zarr format by dynamical.org.

ECMWF AIFS ENS forecast - Ensemble weather forecasts from the ECMWF Artificial Intelligence Forecasting System (AIFS) ENS model.

ECMWF IFS ENS - dynamical.org Icechunk Zarr

The Integrated Forecasting System (IFS) is a global forecast model developed by ECMWF. ENS is an ensemble configuration of IFS, containing 51 ensemble members. IFS consists of a numerical model of the Earth system, which includes an atmospheric model at its heart, coupled with models of other Earth system components such as the ocean. The data assimilation system combines the latest weather observations with a recent forecast to obtain the best possible estimate of the current state of the Earth system.

These datasets have been translated to cloud-optimized Icechunk Zarr format by dynamical.org.

ECMWF IFS ENS forecast, 15 day, 0.25 degree - Ensemble weather forecasts from the ECMWF Integrated Forecasting System (IFS).

ERA5-for-WRF Open Data on AWS

ERA5 reanalysis data on AWS, preprocessed for use with the Weather Research and Forecasting (WRF) model.

East Coast Community Ocean Forecast System (ECCOFS)

The East Coast Community Ocean Forecast System (ECCOFS) is a data assimilating ocean analysis and forecast system being developed by Rutgers University, the University of California Santa Cruz, Fathom Science Inc., and the National Ocean Service (NOS) of NOAA for transition to operations at NCEP in 2028. The ECCOFS domain spans the eastern seaboard of North America and Intra-Americas Seas from the Grand Banks of Newfoundland in the north to the mouth of the Orinoco River, Venezuela, in the south. ECCOFS will complement the existing WCOFS (West Coast Operational Forecast System) to achieve complete forecast coverage of U.S. territorial seas adjacent to the 48 contiguous states and Puerto Rico. Each day ECCOFS generates an ocean analysis using the Regional Ocean Modeling System (ROMS) 4-Dimensional Variational (4D-Var) data assimilation (DA) system constrained by ocean observations. The forecast resolution is 3 km in the horizontal at 50 vertical terrain-following levels. A more detailed overview is presented here.

The meteorological forcing data are marine boundary layer conditions (air temperature, humidity, pressure), net shortwave radiation, and downward longwave radiation at 1-hourly intervals from the NWS High Resolution Rapid Refresh (HRRR) 4-km, 48-hour forecast for the northwest Atlantic Ocean and western Caribbean Sea. In regions beyond the HRRR domain, and in forecast days 3 to 5, the NWS GFS (Global Forecast System) forecast is adopted.

Sub-tidal frequency open boundary conditions are drawn from the Copernicus Marine Service Global Ocean Physics Analysis and Forecast (GLOBAL_ANALYSISFORECAST_PHY_001_024). These data are augmented by boundary harmonic barotropic tidal forcing from the Oregon State University TPXO analysis (12 harmonic constituents, adjusted for the 18.6-year nodal cycle). Luni-solar gravitational tide generating forces for 7 constituents are imposed in the ECCOFS domain interior.

Daily average forecast river discharge data from the Global Flood Awareness System (GloFAS) river routing model of ECMWF precipitation set freshwaiter inflows at 93 rivers where annual mean discharge exceeds 50 m3/s. More details on the forward model configuration are presented here.

Observations presently assimilated include satellite sea level and temperature, and in situ temperature and salinity from moorings, drifters, profiling floats, autonomous underwater vehicles, ships of opportunity, and sensors affixed to fishing gear. When fully configured, ECCOFS will also assimilate satellite salinity, ocean surface currents measured by land-based high-frequency radars, and temperature data from animal tags.

Recognizing that oceanic conditions that can be meaningfully constrained by existing observing networks have length scales much greater than the forecast model resolution of 3 km, ECCOFS employs a Mixed Resolution assimilation approach wherein the ocean forecast is computed on the 3-km grid, but the iterations of the Tangent Linear (TL) and Adjoint (AD) model inner loops of 4D-Var are computed on a lower resolution (6-km) grid that demands less time for execution. The control variables of the assimilation are the initial conditions (at day 0 minus 3) and time varying boundary conditions (over 3 days). More details on the assimilation methodology are presented here.

End of Term Web Archive Dataset

The End of Term Web Archive (EOT) captures and saves U.S. Government websites at the end of presidential administrations. The EOT has thus far preserved websites from administration changes in 2008, 2012, 2016, 2020 and 2024. Data from these web crawls have been made openly available in several formats in this dataset.

Ensemble Meteorological Dataset for Planet Earth, EM-Earth

EM-Earth provides data for precipitation, mean air temperature, air temperature range, and dew-point temperature at 0.1° spatial resolution over global land areas from 1950 to 2019. EM-Earth provides hourly/daily deterministic estimates, and daily probabilistic estimates (25 ensemble members), to meet the diverse requirements of hydrometeorological applications.

Essential-Web v1.0: 24T tokens of organized web data

A 24-trillion-token dataset in which every document is annotated with a twelve-category taxonomy covering topic, format, content complexity, and quality.

GEDI L4A Footprint Level Aboveground Biomass Density, Version 2.1

This dataset contains Global Ecosystem Dynamics Investigation (GEDI) Level 4A (L4A) Version 2 predictions of the aboveground biomass density (AGBD; in Mg/ha) and estimates of the prediction standard error within each sampled geolocated laser footprint. In this version, the granules are in sub-orbits. The algorithm setting group selection used for GEDI02_A Version 2 has been modified for Evergreen Broadleaf Trees in South America to reduce false positive errors resulting from the selection of waveform modes above ground elevation as the lowest mode. The footprints are located within the global latitude band observed by the International Space Station (ISS), nominally 51.6 degrees N and S and reported for the period 2019-04-18 to 2024-11-27. No acquisitions occurred while the GEDI instrument was in storage on the International Space Station (ISS) from March 2023 to April 2024. The GEDI instrument consists of three lasers producing a total of eight beam ground transects, which instantaneously sample eight ~25 m footprints spaced approximately every 60 m along-track. The GEDI beam transects are spaced approximately 600 m apart on the Earth's surface in the cross-track direction, for an across-track width of ~4.2 km. Footprint AGBD was derived from parametric models that relate simulated GEDI Level 2A (L2A) waveform relative height (RH) metrics to field plot estimates of AGBD. Height metrics from simulated waveforms associated with field estimates of AGBD from multiple regions and plant functional types (PFTs) were compiled to generate a calibration dataset for models representing the combinations of world regions and PFTs (i.e., deciduous broadleaf trees, evergreen broadleaf trees, evergreen needleleaf trees, deciduous needleleaf trees, and the combination of grasslands, shrubs, and woodlands). For each of the eight beams, additional data are reported with the AGBD estimates, including the associated uncertainty metrics, quality flags, model inputs, and other information about the GEDI L2A waveform for this selected algorithm setting group. Model inputs include the scaled and transformed GEDI L2A RH metrics, footprint geolocation variables and land cover input data including PFTs and the world region identifiers. Additional model outputs include the AGBD predictions for each of the six GEDI L2A algorithm setting groups with AGBD in natural and transformed units and associated prediction uncertainty for each GEDI L2A algorithm setting group. Providing these ancillary data products will allow users to evaluate and select alternative algorithm setting groups. Also provided are outputs of parameters and variables from the L4A models used to generate AGBD predictions that are required as input to the GEDI04_B algorithm to generate 1-km gridded products. Read our doc on how to get AWS Credentials to retrieve this data: https://data.ornldaac.earthdata.nasa.gov/s3credentialsREADME

Geosnap Data, Center for Geospatial Sciences

This bucket contains multiple datasets (as Quilt packages) created by the Center for Geospatial Sciences (CGS) at the University of California-Riverside. The data in this bucket contains the following:

Tabular and geographic data from the US Census
Land Cover imagery collected from Multi-Resolution Land Characteristics Consortium
Road network data processed from OpenStreetMap

Global Biodiversity Information Facility (GBIF) Species Occurrences

The Global Biodiversity Information Facility (GBIF) is an international network and data infrastructure funded by the world's governments providing global data that document the occurrence of species. GBIF currently integrates datasets documenting over 1.6 billion species occurrences, growing daily. The GBIF occurrence dataset combines data from a wide array of sources including specimen-related data from natural history museums, observations from citizen science networks and environment recording schemes. While these data are constantly changing at GBIF.org, periodic snapshots are taken and made available on AWS.

HLS Landsat Operational Land Imager Surface Reflectance and TOA Brightness Daily Global 30m v2.0

The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance (SR) and top of atmosphere (TOA) brightness data from a virtual constellation of satellite sensors. The Operational Land Imager (OLI) is housed aboard the joint NASA/USGS Landsat 8 and Landsat 9 satellites, while the Multi-Spectral Instrument (MSI) is mounted aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment.The HLSL30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Landsat 8/9 OLI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS) tiling system and thus are “stackable” for time series analysis.The HLSL30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate file. There are 11 bands included in the HLSL30 product along with one quality assessment (QA) band and four angle bands. See the User Guide for a more detailed description of the individual bands provided in the HLSL30 product.Known Issues

Unrealistically high aerosol and low surface reflectance over bright areas: The atmospheric correction over bright targets occasionally retrieves unrealistically high aerosol and thus makes the surface reflectance too low. High aerosol retrievals, both false high aerosol and realistically high aerosol, are masked when quality bits 6 and 7 are both set to 1 (see Table 9 in the User Guide); the corresponding spectral data should be discarded from analysis.
Issues over high latitudes: For scenes greater than or equal to 80 degrees north, multiple overpasses can be gridded into a single MGRS tile resulting in an L30 granule with data sensed at two different times. In this same area, it is also possible that Landsat overpasses that should be gridded into a single MGRS tile are actually written as separate data files. Finally, for scenes with a latitude greater than or equal to 65 degrees north, ascending Landsat scenes may have a slightly higher error in the BRDF correction because the algorithm is calibrated using descending scenes.
Fmask omission errors: There are known issues regarding the Fmask band of this data product that impacts HLSL30 data prior to April of 2022. The HLS Fmask data band may have omission errors in water detection for cases where water detection using spectral data alone is difficult, and omission and commission errors in cloud shadow detection for areas with great topographic relief. This issue does not impact other bands in the dataset.
Inconsistent snow surface reflectance between Landsat and Sentinel-2: The HLS snow surface reflectance can be highly inconsistent between Landsat and Sentinel-2. When assessed on same-day acquisitions from Landsat and Sentinel-2, Landsat reflectance is generally higher than Sentinel-2 reflectance in the visible bands.
Unrealistically high snow surface reflectance in the visible bands: By design, the Land Surface Reflectance Code (LaSRC) atmospheric correction does not attempt aerosol retrieval over snow; instead, a default aerosol optical thickness (AOT) is used to drive the snow surface reflectance. If the snow detection fails, the full LaSRC is used in both AOT retrieval and surface reflectance derivation over snow, which produces surface reflectance values as high as 1.6 in the visible bands. This is a common problem for spring images at high latitudes.
Unrealistically low surface reflectance surrounding snow/ice: Related to the above, the AOT retrieval over snow/ice is generally too high. When this artificially high AOT is used to derive the surface reflectance of the neighboring non-snow pixels, very low surface reflectance will result. These pixels will appear very dark in the visible bands. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used.
Unrealistically low reflectance surrounding clouds: Like for snow, the HLS atmospheric correction does not attempt aerosol retrieval over clouds and a default AOT is used instead. But if the cloud detection fails, an artificially high AOT will be retrieved over clouds. If the high AOT is used to derive the surface reflectance of the neighboring cloud-free pixels, very low surface reflectance values will result. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used.
Unusually low reflectance around other bright land targets: While the HLS atmospheric correction retrieves AOT over non-cloud, non-snow bright pixels, the retrieved AOT over bright targets can be unrealistically high in some cases, similar to cloud or snow. If this unrealistically high AOT is used to derive the surface reflectance of the neighboring pixels, very low surface reflectance values can result as shown in Figure 2. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. These types of bright targets are mostly man-made, such as buildings, parking lots, and roads.
Dark plumes over water: The HLS atmospheric correction does not attempt aerosol retrieval over water. For water pixels, the AOT retrieved from the nearest land pixels is used to derive the surface reflectance, but if the retrieval is incorrect, e.g. from a cloud pixel, this high AOT will create dark stripes over water, as shown in Figure 3. This happens more often over large water bodies, such as lakes and bays, than over narrow rivers.
Landsat WRS-2 Path/Row boundary in L30 reflectance: HLS performs atmospheric correction on Landsat Level 1 images in the original Worldwide Reference System 2 (WRS2) path/row before the derived surface reflectance is reprojected into Military Grid Reference System (MGRS) tiles. If a WRS-2 Landsat image is very cloudy, the AOT from a few remaining clear pixels might be used for the atmospheric correction of the entire image. The AOT that is used can be quite different from the value for the adjacent row in the same path, which results in an artificial abrupt change from one row to the next, as shown in Figure 4. This occurrence is very rare.
Landsat WRS2 path/row boundary in cloud masks: The cloud mask algorithm Fmask creates mask labels by applying thresholds to the histograms of some metrics for each path/row independently. If two adjacent rows in the same path have distinct distributions within the metrics, abrupt changes in masking patterns can appear across the row boundary, as shown in Figure 5. This occurrence is very rare.
Fmask configuration was deficient for 2-3 months in 2021: The HLS installation of Fmask failed to include auxiliary digital elevation model (DEM) and European Space Agency (ESA) Global Surface Water Occurrence data for a 2-3 month run in 2021. This impacted the masking results over water and in mountainous regions.
The reflectance “scale_factor” and “offset” for some L30 and S30 bands were not set: The HLS reflectance scaling factor is 0.0001 and offset is 0. However, this information was not set in the Cloud Optimized GeoTIFF (COG) files of some bands for a small number of granules. The lack of this information creates a problem for automatic conversion of the reflectance data, requiring explicit scaling in applications. The problem has been corrected, but the affected granules have not been reprocessed.
Incomplete map projection information: For a time, HLS imagery was produced with an incomplete coordinate reference system (CRS). The metadata contains the Universal Transverse Mercator (UTM) zone and coordinates necessary to geolocate pixels within the image but might not be in a standard form, especially for granules produced early in the HLS mission. As a result, an error will occur in certain image processing packages due to the incomplete CRS. The simplest solution is to update to the latest version of Geospatial Data Abstraction Library (GDAL) and/or rasterio, which use the available information without error.
False northing of 10^7 for the L30 angle data: The L30 and S30 products do not use a false northing for the UTM projection, and the angle data are supposed to follow the same convention. However, the L30 angle data incorrectly uses a false northing of 10^7. There is no problem with the angle data itself, but the false northing needs to be set to 0 for it to be aligned with the reflectance.
L30 from Landsat L1GT scenes: Landsat L1GT scenes were not intended for HLS due to their poor geolocation. However, some scenes made it through screening for a short period of HLS production. L1GT L30 scenes mainly consist of extensive cloud or snow that can be eliminated using the Fmask quality bits layer. Users can also identify an L1GT-originated L30 granule by examining the HLS cmr.xml metadata file.
The UTC dates in the L30/S30 filenames may not be the local dates: UTC dates are used by ESA and the U.S. Geological Survey (USGS) in naming their Level 1 images, and HLS processing retains this information to name the L30 and S30 products. Landsat and Sentinel-2 overpass eastern Australia and New Zealand around 10AM local solar time, but this area is in either UTC+10:00 or +11:00 zone; therefore, the UTC time for some orbits is in fact near the end of the preceding UTC day. For example, HLS.S30.T59HQS.2016117T221552.v2.0 was acquired in the 22nd hour of day 117 of year 2016 in UTC, but the time was 10:15:52 of day 118 locally. Approximately 100 minutes later HLS.S30.T56JML.2016117T235252.v2.0 was acquired in the next orbit in eastern Australia. This issue also occurs for Landsat. For example, HLS.L30.T59HQS.2016117T221209.v2.0 was acquired on the same day as the first S30 example given above, but both on day 118 of 2016 locally. Adding to the confusion for L30, in the same region, Landsat 8 and 9 can each overpass once in one of the two adjacent WRS-2 Paths (91/92/93) over a two-day period on a local calendar, but based on UTC time, the two overpasses can appear to be on the same day. For example, in the following seemingly same-day pair, the second L30 is actually for day 168 locally:
HLS.L30.T55GCN.2023167T000407.v2.0
HLS.L30.T55GCN.2023167T235747.v2.0
Bear in mind, the date peculiarity for the data occurs when the overpass time is during the late hours of a UTC day.
The atmospheric ancillary data from the wrong date was used for LaSRC: Related to the above, for eastern Australia and New Zealand, L30 and S30 surface reflectance on certain days was created using the atmospheric ancillary data from a date that was one day too early. The exact geographic extent of the affected HLS products and the impact on the surface reflectance quality are under investigation. Practice caution when using data with overpass times during the late hours of a UTC day.
Duplicates in L30: The Landsat 9 acquisitions from October 2021 to March 2023 in Landsat Collection 2 were reprocessed by USGS in March 2023. This reprocessing updated the overpass time by a fraction of a second for some scenes. Since HLS uses overpass time as part of the L30 filename, the older L30 granules were not automatically overwritten due to the different filenames. For example, the first L30 granule in the following pair originated from an older version of L1TP of Landsat 9 with the second granule originating from the reprocessed version.
HLS.L30.T11SLC.2022166T182646.v2.0
HLS.L30.T11SLC.2022166T182645.v2.0
There are other causes of duplicate L30 granules, but the overall number of duplicates is very small.
Poor Geolocation: A large amount of granules that were processed for May through July 2023 were created with L1GT input scenes which were deemed undesirable due to a poor geolocation issue. These granules were removed from the archive. (see the full list of removed granules)

Improvements/Changes from Previous Versions

Aerosol QA bits from the USGS Land Surface Reflectance Code (LaSRC) model output have been added into the Function of Mask (Fmask) data layer. The added two bits indicate the aerosol levels: high, medium, low, and climatology aerosol. Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

HLS Sentinel-2 Multi-spectral Instrument Surface Reflectance Daily Global 30m v2.0

The Harmonized Landsat Sentinel-2 (HLS) project provides consistent surface reflectance data from the Operational Land Imager (OLI) aboard the joint NASA/USGS Landsat 8 satellite and the Multi-Spectral Instrument (MSI) aboard Europe’s Copernicus Sentinel-2A, Sentinel-2B, and Sentinel-2C satellites. The combined measurement enables global observations of the land every 2–3 days at 30-meter (m) spatial resolution. The HLS project uses a set of algorithms to obtain seamless products from OLI and MSI that include atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, illumination and view angle normalization, and spectral bandpass adjustment. The HLSS30 product provides 30-m Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) and is derived from Sentinel-2A, Sentinel-2B, and Sentinel-2C MSI data products. The HLSS30 and HLSL30 products are gridded to the same resolution and Military Grid Reference System (MGRS) tiling system and thus are “stackable” for time series analysis.The HLSS30 product is provided in Cloud Optimized GeoTIFF (COG) format, and each band is distributed as a separate COG. There are 13 bands included in the HLSS30 product along with four angle bands and a quality assessment (QA) band. See the User Guide for a more detailed description of the individual bands provided in the HLSS30 product.Known Issues

Unrealistically high aerosol and low surface reflectance over bright areas: The atmospheric correction over bright targets occasionally retrieves unrealistically high aerosol and thus makes the surface reflectance too low. High aerosol retrievals, both false high aerosol and realistically high aerosol, are masked when quality bits 6 and 7 are both set to 1 (see Table 9 in the User Guide); the corresponding spectral data should be discarded from analysis.
Issues over high latitudes: For scenes greater than or equal to 80 degrees north, multiple overpasses can be gridded into a single MGRS tile resulting in an L30 granule with data sensed at two different times. In this same area, it is also possible that Landsat overpasses that should be gridded into a single MGRS tile are actually written as separate data files. Finally, for scenes with a latitude greater than or equal to 65 degrees north, ascending Landsat scenes may have a slightly higher error in the BRDF correction because the algorithm is calibrated using descending scenes.
Fmask omission errors: There are known issues regarding the Fmask band of this data product that impacts HLSL30 data prior to April of 2022. The HLS Fmask data band may have omission errors in water detection for cases where water detection using spectral data alone is difficult, and omission and commission errors in cloud shadow detection for areas with great topographic relief. This issue does not impact other bands in the dataset.
Inconsistent snow surface reflectance between Landsat and Sentinel-2: The HLS snow surface reflectance can be highly inconsistent between Landsat and Sentinel-2. When assessed on same-day acquisitions from Landsat and Sentinel-2, Landsat reflectance is generally higher than Sentinel-2 reflectance in the visible bands.
Unrealistically high snow surface reflectance in the visible bands: By design, the Land Surface Reflectance Code (LaSRC) atmospheric correction does not attempt aerosol retrieval over snow; instead, a default aerosol optical thickness (AOT) is used to drive the snow surface reflectance. If the snow detection fails, the full LaSRC is used in both AOT retrieval and surface reflectance derivation over snow, which produces surface reflectance values as high as 1.6 in the visible bands. This is a common problem for spring images at high latitudes.
Unrealistically low surface reflectance surrounding snow/ice: Related to the above, the AOT retrieval over snow/ice is generally too high. When this artificially high AOT is used to derive the surface reflectance of the neighboring non-snow pixels, very low surface reflectance will result. These pixels will appear very dark in the visible bands. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. In Figure 1, the pixels in front of the glaciers have surface reflectance values that are too low.
Unrealistically low reflectance surrounding clouds: Like for snow, the HLS atmospheric correction does not attempt aerosol retrieval over clouds and a default AOT is used instead. But if the cloud detection fails, an artificially high AOT will be retrieved over clouds. If the high AOT is used to derive the surface reflectance of the neighboring cloud-free pixels, very low surface reflectance values will result. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used.
Unusually low reflectance around other bright land targets: While the HLS atmospheric correction retrieves AOT over non-cloud, non-snow bright pixels, the retrieved AOT over bright targets can be unrealistically high in some cases, similar to cloud or snow. If this unrealistically high AOT is used to derive the surface reflectance of the neighboring pixels, very low surface reflectance values can result as shown in Figure 2. If the surface reflectance value of a pixel is below -0.2, a NO_DATA value of -9999 is used. These types of bright targets are mostly man-made, such as buildings, parking lots, and roads.
Dark plumes over water: The HLS atmospheric correction does not attempt aerosol retrieval over water. For water pixels, the AOT retrieved from the nearest land pixels is used to derive the surface reflectance, but if the retrieval is incorrect, e.g. from a cloud pixel, this high AOT will create dark stripes over water, as shown in Figure 3. This happens more often over large water bodies, such as lakes and bays, than over narrow rivers.
Landsat WRS-2 Path/Row boundary in L30 reflectance: HLS performs atmospheric correction on Landsat Level 1 images in the original Worldwide Reference System 2 (WRS2) path/row before the derived surface reflectance is reprojected into Military Grid Reference System (MGRS) tiles. If a WRS-2 Landsat image is very cloudy, the AOT from a few remaining clear pixels might be used for the atmospheric correction of the entire image. The AOT that is used can be quite different from the value for the adjacent row in the same path, which results in an artificial abrupt change from one row to the next, as shown in Figure 4. This occurrence is very rare.
Landsat WRS2 path/row boundary in cloud masks: The cloud mask algorithm Fmask creates mask labels by applying thresholds to the histograms of some metrics for each path/row independently. If two adjacent rows in the same path have distinct distributions within the metrics, abrupt changes in masking patterns can appear across the row boundary, as shown in Figure 5. This occurrence is very rare.
Fmask configuration was deficient for 2-3 months in 2021: The HLS installation of Fmask failed to include auxiliary digital elevation model (DEM) and European Space Agency (ESA) Global Surface Water Occurrence data for a 2-3 month run in 2021. This impacted the masking results over water and in mountainous regions.
The reflectance “scale_factor” and “offset” for some L30 and S30 bands were not set: The HLS reflectance scaling factor is 0.0001 and offset is 0. However, this information was not set in the Cloud Optimized GeoTIFF (COG) files of some bands for a small number of granules. The lack of this information creates a problem for automatic conversion of the reflectance data, requiring explicit scaling in applications. The problem has been corrected, but the affected granules have not been reprocessed.
Incomplete map projection information: For a time, HLS imagery was produced with an incomplete coordinate reference system (CRS). The metadata contains the Universal Transverse Mercator (UTM) zone and coordinates necessary to geolocate pixels within the image but might not be in a standard form, especially for granules produced early in the HLS mission. As a result, an error will occur in certain image processing packages due to the incomplete CRS. The simplest solution is to update to the latest version of Geospatial Data Abstraction Library (GDAL) and/or rasterio, which use the available information without error.
False northing of 10^7 for the L30 angle data: The L30 and S30 products do not use a false northing for the UTM projection, and the angle data are supposed to follow the same convention. However, the L30 angle data incorrectly uses a false northing of 10^7. There is no problem with the angle data itself, but the false northing needs to be set to 0 for it to be aligned with the reflectance.
L30 from Landsat L1GT scenes: Landsat L1GT scenes were not intended for HLS due to their poor geolocation. However, some scenes made it through screening for a short period of HLS production. L1GT L30 scenes mainly consist of extensive cloud or snow that can be eliminated using the Fmask quality bits layer. Users can also identify an L1GT-originated L30 granule by examining the HLS cmr.xml metadata file.
The UTC dates in the L30/S30 filenames may not be the local dates: UTC dates are used by ESA and the U.S. Geological Survey (USGS) in naming their Level 1 images, and HLS processing retains this information to name the L30 and S30 products. Landsat and Sentinel-2 overpass eastern Australia and New Zealand around 10AM local solar time, but this area is in either UTC+10:00 or +11:00 zone; therefore, the UTC time for some orbits is in fact near the end of the preceding UTC day. For example, HLS.S30.T59HQS.2016117T221552.v2.0 was acquired in the 22nd hour of day 117 of year 2016 in UTC, but the time was 10:15:52 of day 118 locally. Approximately 100 minutes later HLS.S30.T56JML.2016117T235252.v2.0 was acquired in the next orbit in eastern Australia. This issue also occurs for Landsat. For example, HLS.L30.T59HQS.2016117T221209.v2.0 was acquired on the same day as the first S30 example given above, but both on day 118 of 2016 locally. Adding to the confusion for L30, in the same region, Landsat 8 and 9 can each overpass once in one of the two adjacent WRS-2 Paths (91/92/93) over a two-day period on a local calendar, but based on UTC time, the two overpasses can appear to be on the same day. For example, in the following seemingly same-day pair, the second L30 is actually for day 168 locally:
HLS.L30.T55GCN.2023167T000407.v2.0
HLS.L30.T55GCN.2023167T235747.v2.0
Bear in mind, the date peculiarity for the data occurs when the overpass time is during the late hours of a UTC day.
The atmospheric ancillary data from the wrong date was used for LaSRC: Related to the above, for eastern Australia and New Zealand, L30 and S30 surface reflectance on certain days was created using the atmospheric ancillary data from a date that was one day too early. The exact geographic extent of the affected HLS products and the impact on the surface reflectance quality are under investigation. Practice caution when using data with overpass times during the late hours of a UTC day.
Duplicates in L30: The Landsat 9 acquisitions from October 2021 to March 2023 in Landsat Collection 2 were reprocessed by USGS in March 2023. This reprocessing updated the overpass time by a fraction of a second for some scenes. Since HLS uses overpass time as part of the L30 filename, the older L30 granules were not automatically overwritten due to the different filenames. For example, the first L30 granule in the following pair originated from an older version of L1TP of Landsat 9 with the second granule originating from the reprocessed version.
HLS.L30.T11SLC.2022166T182646.v2.0
HLS.L30.T11SLC.2022166T182645.v2.0
There are other causes of duplicate L30 granules, but the overall number of duplicates is very small.
Poor Geolocation: A large amount of granules that were processed for May through July 2023 were created with L1GT input scenes which were deemed undesirable due to a poor geolocation issue. These granules were removed from the archive. (see the full list of removed granules)

Improvements/Changes from Previous Versions

Aerosol QA bits from the USGS Land Surface Reflectance Code (LaSRC) model output have been added into the Function of Mask (Fmask) data layer. The added two bits indicate the aerosol levels: high, medium, low, and climatology aerosol. Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

Hawai'i Downscaled Climate Projections CMIP6

The NEX-HIDCP-CMIP6 dataset contains high-resolution daily downscaled CMIP6 projections over Hawai'i for maximum temperature, minimum temperature, and precipitation. This dataset compliments the NEX-DCP30-CMIP6 collection of downscaled projections over CONUS.

High Resolution Population Density Maps + Demographic Estimates by CIESIN and Meta

Population data for a selection of countries, allocated to 1 arcsecond blocks and provided in a combination of CSV and Cloud-optimized GeoTIFF files. This refines CIESIN’s Gridded Population of the World using machine learning models on high-resolution worldwide Maxar satellite imagery. CIESIN population counts aggregated from worldwide census data are allocated to blocks where imagery appears to contain buildings.

High-Order Accurate Direct Numerical Simulation of Flow over a MTU-T161 Low Pressure Turbine Blade

The archive comprises snapshot, point-probe, and time-average data produced via a high-fidelity computational simulation of turbulent air flow over a low pressure turbine blade, which is an important component in a jet engine. The simulation was undertaken using the open source PyFR flow solver on over 5000 Nvidia K20X GPUs of the Titan supercomputer at Oak Ridge National Laboratory under an INCITE award from the US DOE. The data can be used to develop an enhanced understanding of the complex three-dimensional unsteady air flow patterns over turbine blades in jet engines. This could in turn lead to design of greener more fuel efficient aircraft. It could also be used to train a next-generation of Reynolds Averaged Navier-Stokes turbulence models via a machine learning approach, which would have broad applicability to a wide range of science and engineering problems.

Human Cancer Models Initiative (HCMI) Cancer Model Development Center

The Human Cancer Models Initiative (HCMI) is an international consortium that is generating novel, next-generation, tumor-derived culture models annotated with genomic and clinical data. HCMI-developed models and related data are available as a community resource. The NCI is contributing to the initiative by supporting four Cancer Model Development Centers (CMDCs). CMDCs are tasked with producing next-generation cancer models from clinical samples. The cancer models include tumor types that are rare, originate from patients from underrepresented populations, lack precision therapy, or lack cancer model tools. Throughout the development process, the CMDCs utilize stringent internal QC measures to ensure both clinical and molecular integrity. These models are then annotated with clinical and genomic data and are available as a community resource.

Human PanGenomics Project

This dataset includes sequencing data, assemblies, and analyses for the offspring of ten parent-offspring trios.

ICEYE Synthetic Aperture Radar (SAR) Open Dataset

ICEYE operates the world’s largest constellation of synthetic aperture radar (SAR) satellites, delivering unmatched access to persistent, high-resolution Earth observation data regardless of time of day or weather conditions. The ICEYE Open Dataset makes a curated selection of SAR imagery publicly available to promote research, innovation, and education in the geospatial community. ICEYE’s constellation enables rapid revisit rates and flexible imaging modes, unlocking insights into natural disasters, climate monitoring, infrastructure, and more.Learn more at www.iceye.com.

IGP Brick Kilns Bangladesh

This dataset includes detailed information about brick kilns, their locations, capacities, emissions, and other relevant attributes around the Indian Gangetic Plain.

IGP Brick Kilns India

This dataset includes detailed information about brick kilns, their locations, capacities, emissions, and other relevant attributes around the Indian Gangetic Plain.

IGP Brick Kilns Pakistan

This dataset includes detailed information about brick kilns, their locations, capacities, emissions, and other relevant attributes around the Pakistann Gangetic Plain.

IGP Cement Plants

This dataset includes detailed information about cement plants, their locations, capacities, emissions, and other relevant attributes around the Indian Gangetic Plain.

IGP Paper and Pulp Plant

This dataset includes detailed information about paper and pulp plants, their locations, capacities, emissions, and other relevant attributes around the Indian Gangetic Plain.

IGP Power Generation Plant

This dataset includes detailed information about power generation plants, their locations, capacities, emissions, and other relevant attributes around the Indian Gangetic Plain.

IGP Steel Plants

This dataset includes detailed information about steel plants, their locations, capacities, emissions, and other relevant attributes around the Indian Gangetic Plain.

IGP Waste Management Data

This dataset includes detailed information about waste management sites, their locations, capacities, emissions, and other relevant attributes around the Indian Gangetic Plain.

IWMI DIWASA Rainfed and Irrigated Cropland Map for Africa

A framework integrating the Budyko model has been developed to distinguish between rainfed and irrigated cropland areas across Africa. This expands on remote sensing land cover products available for agricultural water studies in Africa and thereby helps address the need for deeper insights into cropland patterns. Validation against an independent dataset revealed an overall accuracy of 73% with high precision and specificity scores. These results validate the framework’s effectiveness in identifying irrigated areas while minimizing errors in misclassifying rainfed areas as irrigated.

Image classification - fast.ai datasets

Some of the most important datasets for image classification research, including CIFAR 10 and 100, Caltech 101, MNIST, Food-101, Oxford-102-Flowers, Oxford-IIIT-Pets, and Stanford-Cars. This is part of the fast.ai datasets collection hosted by AWS for convenience of fast.ai students. See documentation link for citation and license details for each dataset.

Japan Prefectures, 3D Point Cloud Data

This dataset comprises high-precision 3D point cloud data that covers all prefectures throughout Japan. The data is produced through aerial laser surveys, airborne laser bathymetry, and mobile mapping systems, representing the culmination of many years of dedicated effort. This data will be visualized and analyzed for use in infrastructure maintenance, disaster prevention measures, and autonomous vehicle driving.

Kanagawa, 3D Point Cloud Data

This dataset comprises high-precision 3D point cloud data that encompasses the entire Kanagawa prefecture in Japan. The data is produced through aerial laser survey, airborne laser bathymetry and mobile mapping systems, the culmination of many years of dedicated effort. This data will be visualized and analyzed for use in infrastructure maintenance, disaster prevention measures and autonomous vehicle driving.

Knowledge Portal Network Bottom-line Genetic Associations

At the Knowledge Portal Network, we aggregate and analyze genetic association results for a wide range of diseases and traits. For any given disease, a large number of individual genetic association datasets may have been generated. To make these results more interpretable, we meta-analyze all datasets for each phenotype, using a method that we term "bottom-line integrative analysis". Here we provide the bottom-line summary statistic files for public download.

Korea Meteorological Administration (KMA) GK-2A Satellite Data

The Geo-KOMPSAT-2A (GK2A) is the new generation geostationary meteorological satellite (located in 128.2°E) of the Korea Meteorological Administration (KMA). The main mission of the GK2A is to observe the atmospheric phenomena over the Asia-Pacific region. The Advance Meteorological Imager (AMI) on GK2A scan the Earth full disk every 10 minutes and the Korean Peninsula area every 2 minutes with a high spatial resolution of 4 visible channels and 12 infrared channels. In addition, the AMI has an ability of flexible target area scanning useful for monitoring severe weather events such as typhoon and volcanic eruption and so on. And for space weather mission, the Korea Space wEather Monitor (KSEM) on the GK2A observes the space environment with the particle detector, magnetometer and charging monitor. For questions regarding GK2A imagery specifications, visit the GK2A site at https://nmsc.kma.go.kr/enhome/html/base/cmm/selectPage.do?page=satellite.gk2a.intro. To view the GK2A Fact Sheet please visit https://nmsc.kma.go.kr/enhome/html/base/cmm/selectPage.do?page=satellite.gk2a.fact.

NOAA provides access to GK2A data on AWS in coordination with the Korean Meteorlogical Administration.

LGND Clay v1.5 Sentinel-2

A global dataset of Clay v1.5 embeddings for Sentinel2.

LOFAR ELAIS-N1 cycle 2 observations on AWS

These data correspond to the International LOFAR Telescope observations of the sky field ELAIS-N1 (16:10:01 +54:30:36) during the cycle 2 of observations. There are 11 runs of about 8 hours each plus the corresponding observation of the calibration targets before and after the target field. The data are measurement sets (MS) containing the cross-correlated data and metadata divided in 371 frequency sub-bands per target centred at ~150 MHz.

Land/Sea static mask relevant to IMERG precipitation 0.1x0.1 degree V2 (GPM_IMERG_LandSeaMask) at GES DISC

Version 2 is the current version of the data set. Older versions will no longer be available and have been superseded by Version 2.This land sea mask originated from the NOAA group at SSEC in the 1980s. It was originally produced at 1/6 deg resolution, and then regridded for the purposes of GPCP, TMPA, and IMERG precipitation products. NASA code 610.2, Terrestrial Information Systems Laboratory, restructured this land sea mask to match the IMERG grid, and converted the file to CF-compliant netCDF4. Version 2 was created in May, 2019 to resolve detected inaccuracies in coastal regions.Users should be aware that this is a static mask, i.e. there is no seasonal or annual variability, and it is due for update. It is not recommended to be used outside of the aforementioned precipitation data. Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

Legal Entity Identifier (LEI) and Legal Entity Reference Data (LE-RD)

The Legal Entity Identifier (LEI) is a 20-character, alpha-numeric code based on the ISO 17442 standard developed by the International Organization for Standardization (ISO). It connects to key reference information that enables clear and unique identification of legal entities participating in financial transactions. Each LEI contains information about an entity’s ownership structure and thus answers the questions of 'who is who’ and ‘who owns whom’. Simply put, the publicly available LEI data pool can be regarded as a global directory, which greatly enhances transparency in the global marketplace. The Financial Stability Board (FSB) has reiterated that global LEI adoption underpins “multiple financial stability objectives” such as improved risk management in firms as well as better assessment of micro and macro prudential risks. As a result, it promotes market integrity while containing market abuse and financial fraud. Last but not least, LEI rollout “supports higher quality and accuracy of financial data overall”. The publicly available LEI data pool is a unique key to standardized information on legal entities globally. The data is registered and regularly verified according to protocols and procedures established by the Regulatory Oversight Committee. In cooperation with its partners in the Global LEI System, the Global Legal Entity Identifier Foundation (GLEIF) continues to focus on further optimizing the quality, reliability and usability of LEI data, empowering market participants to benefit from the wealth of information available with the LEI population. The drivers of the LEI initiative, i.e. the Group of 20, the FSB and many regulators around the world, have emphasized the need to make the LEI a broad public good. The Global LEI Index, made available by GLEIF, greatly contributes to meeting this objective. It puts the complete LEI data at the disposal of any interested party, conveniently and free of charge. The benefits for the wider business community to be generated with the Global LEI Index grow in line with the rate of LEI adoption. To maximize the benefits of entity identification across financial markets and beyond, firms are therefore encouraged to engage in the process and get their own LEI. Obtaining an LEI is easy. Registrants simply contact their preferred business partner from the list of LEI issuing organizations available on the GLEIF website.

LongBench - cross-platform reference dataset profiling cancer cell lines with bulk and single-cell approaches

LongBench is a comprehensive benchmark dataset of the latest long-read transcriptomics technologies from Oxford Nanopore (ON) and Pacific Biosciences, alongside a comparison with next-generation sequencing from Illumina. We generated bulk and single-cell libraries from lung cancer cell lines which include different cancer subtypes to capture real biological variation. To further compare and assess sequencing platform performance, Sequins and SIRVs (Set 4) synthetic spike-ins have been included.

Longitudinal Nutrient Deficiency

Dataset associated with the 2021 AAAI Paper- Detection and Prediction of Nutrient Deficiency Stress using Longitudinal Aerial Imagery. The dataset contains 3 image sequences of aerial imagery from 386 farm parcels which have been annotated for nutrient deficiency stress.

MAN TruckScenes

A large scale multimodal dataset for Autonomous Trucking. Sensor data was recorded with a heavy truck from MAN equipped with 6 lidars, 6 radars, 4 cameras and a high-precision GNSS. MAN TruckScenes allows the research community to come into contact with truck-specific challenges, such as trailer occlusions, novel sensor perspectives, and terminal environments for the first time. It comprises more than 740 scenes of 20s each within a multitude of different environmental conditions. Bounding boxes are available for 27 object classes, 15 attributes, and a range of more than 230m. The scenes are tagged according to 34 distinct scene tags, and all objects are tracked throughout the scene to promote a wide range of applications.

MODIS MYD13A1, MOD13A1, MYD11A1, MOD11A1, MCD43A4

Data from the Moderate Resolution Imaging Spectroradiometer (MODIS), managed by the U.S. Geological Survey and NASA. Five products are included: MCD43A4 (MODIS/Terra and Aqua Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid), MOD11A1 (MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid), MYD11A1 (MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid), MOD13A1 (MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid), and MYD13A1 (MODIS/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid). MCD43A4 has global coverage, all time (~21 years). The other products have ~11 years of global coverage. All data files are in single-band cloud-optimized GeoTIFF (COG) format.

MODIS/Aqua Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V061

Prior to the Aqua MODIS launch, Band 6 exhibited several anomalous detectors. Band 6 performance degraded seriously after launch and presently a majority of the Band 6 detectors are non-functional. Science users should read and use the non-functional detector flags and decide for themselves the optimum manner to handle non-functional detector "gaps" for their products. For complete information please refer to the MODIS Characterization Support Team (MCST) website.
For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvments/Changes from Previous Version

The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

MODIS/Aqua Surface Reflectance Daily L2G Global 250m SIN Grid V061

Prior to the Aqua MODIS launch, Band 6 exhibited several anomalous detectors. Band 6 performance degraded seriously after launch and presently a majority of the Band 6 detectors are non-functional. Science users should read and use the non-functional detector flags and decide for themselves the optimum manner to handle non-functional detector "gaps" for their products. For complete information please refer to the MODIS Characterization Support Team (MCST) website.
For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvments/Changes from Previous Version

The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

MODIS/Terra Calibrated Radiances 5-Min L1B Swath 500m

The MODIS/Terra Calibrated Radiances 5Min L1B Swath 500m data set contains calibrated and geolocated at-aperture radiances for 7 discrete bands located in the 0.45 to 2.20 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data.Visible, shortwave infrared, and near infrared measurements are only made during the daytime (except band 26), while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously.Channels 1 and 2 have 250 m resolution, channels 3 through 7 have 500 m resolution. However, for the MODIS L1B 500 m product, the 250 m band radiance data and their associated uncertainties have been aggregated to 500m resolution. Thus the entire channel data set has been co-registered to the same spatial scale in the 500 m product. Separate L1B products are available for the 250 m resolution channels (MOD02QKM) and 1 km resolution channels (MOD021KM). For the latter product, the 250 m and 500 m channel data (bands 1 through 7) have been aggregated into equivalent 1 km pixel values.Spatial resolution for pixels at nadir is 500 km, degrading to 2.4 km in the along-scan direction at the scan extremes. However, thanks to the overlapping of consecutive swaths and respectively pixels there, the resulting resolution at the scan extremes is about 1 km. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 500 m granule will contain a scene built from 203 scans sampled 2708 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 20 along-track spatial elements for the 500 m channels, the scene will be composed of (2708 x 4060) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 20 degrees scan angle. To summarize, the MODIS L1B 500 m data product consists of:

Calibrated radiances, uncertainties and number of samples for (2) 250 m reflected solar bands aggregated to 500 m resolution
Calibrated radiances and uncertainties for (5) 500 m reflected solar bands
Geolocation for 1km pixels, that must be interpolated to get 500 m pixel locations. For the relationship of 1km pixels to 500m pixels, see the Geolocation ATBD https://modis.gsfc.nasa.gov/data/atbd/atbd_mod28_v3.pdf.
Calibration data for all channels (scale and offset)
Comprehensive set of file-level metadata summarizing the spatial, temporal and parameter attributes of the data, as well as auxiliary information pertaining to instrument status and data quality characterization users requiring all geolocation and solar/satellite geometry fields at 1km resolution can obtain the separate MODIS Level 1 Geolocation product (MOD03) from LAADS https://ladsweb.modaps.eosdis.nasa.gov/ .

The shortname for this product is MOD02HKM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical MOD02HKM file size is approximately 135 MB.Environmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world.See the MODIS Characterization Support Team webpage for more C6 product information at:https://mcst.gsfc.nasa.gov/l1b/product-informationor visit Science Team homepage at: https://modis.gsfc.nasa.gov/data/dataprod/ Read our doc on how to get AWS Credentials to retrieve this data: https://data.laadsdaac.earthdatacloud.nasa.gov/s3credentialsREADME

MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid V061

The MOD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The algorithm used for the MOD16 data product collection is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. Provided in the MOD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MOD16A2 granule.The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period, depending on the year.Known Issues

Operational and uncertainty issues are provided under Section 3 in the User Guide.
For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvements/Changes from Previous Versions

The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
A polarization correction has been applied to the L1B Reflective Solar Bands (RSB).
The product uses Climatology LAI/FPAR as back up to the operational LAI/FPAR. Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

MODIS/Terra Surface Reflectance 8-Day L3 Global 500m SIN Grid V061

The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra MOD09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of Terra MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are two quality layers and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. Known Issues

For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvements/Changes from Previous Versions

The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

MODIS/Terra Surface Reflectance Daily L2G Global 1km and 500m SIN Grid V061

The MOD09GA Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 kilometer (km) observation bands and geolocation flags. The reflectance layers from the MOD09GA are used as the source data for many of the MODIS land products. Known Issues

For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvements/Changes from Previous Versions

The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

MODIS/Terra Surface Reflectance Daily L2G Global 250m SIN Grid V061

The MOD09GQ Version 6.1 product provides an estimate of the surface spectral reflectance of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m surface reflectance bands are the Quality Assurance (QA) layer and five observation layers. This product is intended to be used in conjunction with the quality and viewing geometry information of the 500 m product (MOD09GA). Known Issues

For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvements/Changes from Previous Versions

The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid V061

The Terra Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MOD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MOD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value.Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues

For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvements/Changes from Previous Versions

The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

MODIS/Terra+Aqua BRDF/Albedo Albedo Daily L3 Global - 500m V061

The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A3 Version 6.1 Albedo Model dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the 16 day which is reflected in the Julian date in the file name.Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide.The MCD43A3 provides black-sky albedo (directional hemispherical reflectance) and white-sky albedo (bihemispherical reflectance) data at local solar noon for MODIS bands 1 through 7 and the visible, near infrared (NIR), and shortwave bands. Along with the albedo layers are the simplified mandatory quality layers for each of the 10 bands. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.Known Issues

For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvements/Changes from Previous Versions

The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

MODIS/Terra+Aqua BRDF/Albedo Model Parameters Daily L3 Global - 500m V061

The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A1 Version 6.1 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. Data are temporally weighted to the ninth day of the retrieval period which is reflected in the Julian date in the file name. MCD43A1 provides the three model weighting parameters (isotropic, volumetric, and geometric) used to derive the Albedo (MCD43A3) and Nadir BRDF-Adjusted Reflectance (NBAR) (MCD43A4) products.Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide.The MCD43A1 provides the three model weighting parameters for MODIS spectral bands 1 through 7 as well as the visible, near infrared (NIR), and shortwave bands. Along with the three-dimensional parameter layers for these bands are the simplified mandatory quality layers for each of the 10 bands. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product. Known Issues

For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvements/Changes from Previous Versions

The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

MODIS/Terra+Aqua BRDF/Albedo Nadir BRDF-Adjusted Ref Daily L3 Global - 500m V061

The Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 Version 6.1 Nadir Bidirectional Reflectance Distribution Function (BRDF)-Adjusted Reflectance (NBAR) dataset is produced daily using 16 days of Terra and Aqua MODIS data at 500 meter (m) resolution. The view angle effects are removed from the directional reflectances, resulting in a stable and consistent NBAR product. Data are temporally weighted to the ninth day which is reflected in the Julian date in the file name.Users are urged to use the band specific quality flags to isolate the highest quality full inversion results for their own science applications as described in the User Guide.The MCD43A4 provides NBAR and simplified mandatory quality layers for MODIS bands 1 through 7. Essential quality information provided in the corresponding MCD43A2 data file should be consulted when using this product.Known Issues

For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

Improvements/Changes from Previous Versions

The Version 6.1 Level-1B (L1B) products have been improved by undergoing various calibration changes that include: changes to the response-versus-scan angle (RVS) approach that affects reflectance bands for Aqua and Terra MODIS, corrections to adjust for the optical crosstalk in Terra MODIS infrared (IR) bands, and corrections to the Terra MODIS forward look-up table (LUT) update for the period 2012 - 2017.
A polarization correction has been applied to the L1B Reflective Solar Bands (RSB). Read our doc on how to get AWS Credentials to retrieve this data: https://data.lpdaac.earthdatacloud.nasa.gov/s3credentialsREADME

Marginal Build Emissions Rates (MBERs) for Electricity

The Climate TRACE coalition has developed and maintains free global hourly Build Margin data, also known as MBERs, that are compliant with the Greenhouse Gas Protocol's Project Protocol electricity sector guidance, Guidelines for Grid-Connected Electricity Projects ("GHGP Guidelines").

Mars Spectrometry 2: Gas Chromatography for the Sample Analysis at Mars Data (SAM) Instrument

NASA missions like the Curiosity and Perseverance rovers carry a rich array of instruments suited to collect data and build evidence towards answering if Mars ever had livable environmental conditions. These rovers can collect rock and soil samples and can take measurements that can be used to determine their chemical makeup.

Because communication between rovers and Earth is severely constrained, with limited transfer rates and short daily communication windows, scientists have a limited time to analyze the data and make difficult inferences about the chemistry in order to prioritize the next operations and send those instructions back to the rover.

This project aimed at building a model to automatically analyze gas chromatography mass spectrometry (GCMS) data collected for Mars exploration in order to help the scientists in their analysis of understanding the past habitability of Mars.

More information are available at https://mars.nasa.gov/msl/spacecraft/instruments/sam/ and the data from Mars are available and described at https://pds-geosciences.wustl.edu/missions/msl/sam.htm.

We request that you cite the following ackowledgement when using the data provided from this NASA Project: "NASA provided support for the development of SAM. The datasets for these 2 challenges were provided by NASA Goddard Space Flight Center and NASA Johnson Space Center. They have been collected by the Sample Analysis at Mars (SAM) scientists and specifically processed for these challenges with the help of the scientists from NASA: Doug Archer, Charles Malespin, Caroline Freissinet, Stephanie Getty, Luoth Chou, Eric Lyness, and Victoria Da Poian, and the DrivenData team. Data from all SAM experiments are archived in the Planetary Data System (pds.nasa.gov)."

Mars Spectrometry: Detect Evidence for Past Habitability

NASA missions like the Curiosity and Perseverance rovers carry a rich array of instruments suited to collect data and build evidence towards answering if Mars ever had livable environmental conditions. These rovers can collect rock and soil samples and can take measurements that can be used to determine their chemical makeup.

Because communication between rovers and Earth is severely constrained, with limited transfer rates and short daily communication windows, scientists have a limited time to analyze the data and make difficult inferences about the chemistry in order to prioritize the next operations and send those instructions back to the rover.

This project aimed at building a model to automatically analyze evolved gas analysis mass spectrometry (EGA-MS) data collected for Mars exploration in order to help the scientists in their analysis of understanding the past habitability of Mars.

More information are available at https://mars.nasa.gov/msl/spacecraft/instruments/sam/ and the data from Mars are available and described at https://pds-geosciences.wustl.edu/missions/msl/sam.htm.

We request that you cite the following ackowledgement when using the data provided from this NASA Project: "NASA provided support for the development of SAM. The datasets for these 2 challenges were provided by NASA Goddard Space Flight Center and NASA Johnson Space Center. They have been collected by the Sample Analysis at Mars (SAM) scientists and specifically processed for these challenges with the help of the scientists from NASA: Doug Archer, Charles Malespin, Caroline Freissinet, Stephanie Getty, Luoth Chou, Eric Lyness, and Victoria Da Poian, and the DrivenData team. Data from all SAM experiments are archived in the Planetary Data System (pds.nasa.gov)."

Met Office UK Earth System Model (UKESM1) ARISE-SAI geoengineering experiment data

Data from the UK Earth System Model (UKESM1) ARISE-SAI experiment. The UKESM1 ARISE-SAI experiment explores the impacts of geoengineering via the injection of sulphur dioxide (SO2) into the stratosphere in order to keep global mean surface air temperature near 1.5 C above the pre-industrial climate. Data includes a five member ensemble of simulations with SO2 injection plus a five member ensemble of SSP2-4.5 simulations from CMIP6 to serve as a reference data set

Met Office UK Land Surface Observations

Land surface weather observations for 31 parameters from over 250 locations across the Met Office UK land observation network. The data is available as CSV files. You can use it to monitor the latest weather affecting a specific location so you can plan for your business or operations.

The observations are produced every minute and transmitted to the Amazon Registry of Open Data every hour. They’re available for a rolling 7-day period (168 hours).
All locations in the observation network are within the bounding box:

-15 (West)
48 (South)
5 (East)
62 (North) On average they are about 40km apart, distributed uniformly to detect as many weather features as possible.

31 meteorological parameters are measured across the observation network. But not all locations record all parameters. Check the documentation for the full list of parameters.

Met Office UK Marine Observations

Marine surface weather observations for 32 parameters from 69 locations across the Met Office marine observation network. Observations are available for a rolling 7-day period (168 hours). The data is available as CSV files.

The data comes from moored buoys, light vessels and ships with automatic weather stations onboard. Buoys and light vessels are static and you can view their locations on the Met Office Marine Observations page. You can use the data to monitor the latest weather affecting a specific marine location so you can plan for your business or operations.

Join the Met Office research panel to help us understand how people interact with weather and climate data, uncover challenges and explore opportunities.

Met Office UK Radar Observations on a 2-year rolling archive

The United Kingdom Composite, Surface Rain Rate Estimate is an international radar composite produced by Met Office (UK). This is a composite, radar reflectivity derived, surface rain rate estimate product in HDF5 code from stations covering the United Kingdom.

Multi-robot, Multi-Sensor, Multi-Environment Event Dataset (M3ED)

M3ED is the first multi-sensor event camera (EC) dataset focused on high-speed dynamic motions in robotics applications. M3ED provides high-quality synchronized data from multiple platforms (car, legged robot, UAV), operating in challenging conditions such as off-road trails, dense forests, and performing aggressive flight maneuvers. M3ED also covers demanding operational scenarios for EC, such as high egomotion and multiple independently moving objects. M3ED includes high-resolution stereo EC (1280×720), grayscale and RGB cameras, a high-quality IMU, a 64-beam LiDAR, and RTK localization.

MultiCoNER Datasets

MultiCoNER 1 is a large multilingual dataset (11 languages) for Named Entity Recognition. It is designed to represent some of the contemporary challenges in NER, including low-context scenarios (short and uncased text), syntactically complex entities such as movie titles, and long-tail entity distributions. MultiCoNER 2 is a large multilingual dataset (12 languages) for fine grained Named Entity Recognition. Its fine-grained taxonomy contains 36 NE classes, representing real-world challenges for NER, where named entities, apart from the surface form, context represents a critical role in distinguishing between the different fine-grained types (e.g. Scientist vs. Athlete). Furthermore, the test data of MultiCoNER 2 contains noisy instances, where the noise has been applied to both context tokens as well as the entity tokens. The noise includes typing errors at character level based on keyboard layouts in the the different languages.

My School Today

This database provides estimates of walking travel time of school-aged populations to schools recorded in OpenStreetMap. Population counts of male and female students are sorted into 3 groups of travel time - under 30 minutes, 30-60 minutes, and over 60 minutes. It covers the African continent and is aggregated by first-level administrative divisions.

NCBI SRA Gene Feature RNA-Seq counts

The NIH Sequence Read Archive (SRA), hosted by the [National Center for Biotechnology Information (NCBI) at the National Library of Medicine (NLM) stores sequencing data and alignment information from high-throughput next-generation sequencing platforms. SRA has conducted gene expression analysis of publicly released human and mouse RNA-Seq experiments to process raw RNA-seq reads into concise formats that summarize the expression results. The un-normalized feature counts for each SRA record are available in tab-delimited (*.tsv) format. The tsv files include two columns, the gene id and count. These counts facilitate differential gene expression analyses, particularly across studies and the entirety of the SRA corpus.

NCEP/CPC L3 Half Hourly 4km Global (60S - 60N) Merged IR V1 (GPM_MERGIR) at GES DISC

These data originate from NOAA/NCEP.The NOAA Climate Prediction Center/NCEP/NWS is making the data available originally in binary format, in a weekly rotating archive. The NASA GES DISC is acquiring the binary files as they become available, converts them into CF (Climate and Forecast) -convention compliant netCDF-4 format, and stores the product in a permanent archive. The original record started from February, 2000, but in June, 2025 it was extended back to January, 1998.The leading edge of data availability is delayed by about 24 hours from real-time to abide by international data exchange agreements between NOAA and EUMETSAT (the METEOSAT data providers).The data contain globally-merged (60°S-60°N) 4-km pixel-resolution IR brightness temperature data (equivalent blackbody temps), merged from the European, Japanese, and U.S. geostationary satellites over the period of record (GOES-8/9/10/11/12/13/14/15/16/17/18/19, METEOSAT-5/7/8/9/10/11, and GMS-5/MTSat-1R/2/Himawari-8/9).The global geo-IR are dynamically calibrated to GOES East, using a 35 day trailing inter-calibration using time/space-matched IR Tb’s at the mid-point between sub-satellite positions. In the event of duplicate data in a grid box, the value with the smaller zenith angle is taken. The data have been corrected for "zenith angle dependence", in which IR temperatures for locations far from satellite nadir are erroneously cold due to a combination of geometric effects and radiometric path extinction effects (Joyce et al. 2001). Finally, the data are re-navigated for parallax, which shifts the geo-location of the GEO-IR footprints to approximately account for the cloud tops that the IR “sees” being displaced away from their actual geographic location when viewed along a slanted path. These corrections allow for the merging of the IR data from the various GEO-satellites with greatly reduced discontinuities at GEO-satellite data boundaries. In the event of duplicate data in a grid box, the value with the smaller zenith angle is taken.The NASA GES DISC is curating these data in a self-documenting, CF-compliant, netCDF-4 format, which allows a broad range of applications to access the data directly, without the need to cope with the original binary data format. In addition to the direct download of netCDF-4 data, the GES DISC provides data download in binary, ASCII, and netCDF-3 formats using the OPeNDAP interface.

Similarities with the original

As in the original binaries, every netCDF-4 file covers one hour, and contains two half-hourly grids, at 4-km grid cell resolution.

Differences from the original

The data in the netCDF-4 files are already converted to real (float) values of Brightness Temperatures in Kelvin. There is no need to further scale these data. The netCDF-4 format is machine-independent and users need not worry about the endian-ness of their machines.
To meet the requirements of collection spatial metadata, the grid is re-ordered from the original and now goes from -180 (West) to 180 (East). It is also starting from -60 (South).

The data and time units are reflected in the corresponding "units" attributes, and grid dimensions are described by longitude ("lon"), latitude ("lat") and "time" vectors. Thus, any CF-compliant tool should automatically understand the setup in the data files and the starting time for each half-hourly grid. Even without such tools, simple "ncdump" or "h5dump" command line tools will easily disclose the netCDF-4 files configuration.

Acknowledgements

The creation of the original data at NOAA/NCEP is supported by funding from the NOAA Office of Global Programs for the Global Precipitation Climatology Project (GPCP) and by NASA via the Tropical Rainfall Measuring Mission (TRMM). The permanent archive at GES DISC is supported by NASA's HQ Earth Science Data Systems (ESDS) Program. Read our doc on how to get AWS Credentials to retrieve this data: https://data.gesdisc.earthdata.nasa.gov/s3credentialsREADME

NEOWISE Post-Cryo Data | Wide-field Infrared Survey Explorer (WISE)

The Wide-field Infrared Survey Explorer (WISE) was a NASA Medium Explorer satellite in low-Earth orbit that conducted an all-sky astronomical imaging survey over four infrared bands from 2010-2011. The NEOWISE Post-Cryo Data Release contains 3.4 and 4.6 micron (W1 and W2) imaging data that were acquired between 29 September 2010 and 1 February 2011 following the exhaustion of the inner and outer cryogen tanks.

NEOWISE Reactivation Data | Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE)

The Near-Earth Object Wide-field Infrared Survey Explorer (NEOWISE) is a NASA Medium-class Explorer satellite in low-Earth orbit conducting an all-sky astronomical imaging survey over two infrared bands. The NEOWISE Reactivation mission began in 2013 when the original WISE satellite was brought out of hibernation to learn more about the population of near-Earth objects and comets that could pose an impact hazard to the Earth. The data is also used to study a wide range of astrophysical phenomena in the time domain including brown dwarfs, supernovae and active galactic nuclei.

NOAA Coastal Lidar Data

Lidar (light detection and ranging) is a technology that can measure the 3-dimentional location of objects, including the solid earth surface. The data consists of a point cloud of the positions of solid objects that reflected a laser pulse, typically from an airborne platform. In addition to the position, each point may also be attributed by the type of object it reflected from, the intensity of the reflection, and other system dependent metadata. The NOAA Coastal Lidar Data is a collection of lidar projects from many different sources and agencies, geographically focused on the coastal areas of the United States of America. The data is provided in Entwine Point Tiles (EPT; https://entwine.io) format, which is a lossless streamable octree of the point cloud, and in LAZ format. Datasets are maintained in their original projects and care should be taken when merging projects. The coordinate reference system for the data is The NAD83(2011) UTM zone appropriate for the center of each data set for EPT and geographic coordinates for LAZ. Vertically they are in the orthometric datum appropriate for that area (for example, NAVD88 in the mainland United States, PRVD02 in Puerto Rico, or GUVD03 in Guam). The geoid model used is reflected in the data set resource name.
The data are organized under directories entwine and laz for the EPT and LAZ versions respectively. Some datasets are not in EPT format, either because the dataset is already in EPT on the USGS public lidar site, they failed to build or their content does not work well in EPT format. Topobathy lidar datasets using the topobathy domain profile do not translate well to EPT format.

NOAA Global Surface Summary of Day

Global Surface Summary of the Day is derived from The Integrated Surface Hourly (ISH) dataset. The ISH dataset includes global data obtained from the USAF Climatology Center, located in the Federal Climate Complex with NCDC. The latest daily summary data are normally available 1-2 days after the date-time of the observations used in the daily summaries. The online data files begin with 1929 and are at the time of this writing at the Version 8 software level. Over 9000 stations' data are typically available. The daily elements included in the dataset (as available from each station) are:
Mean temperature (.1 Fahrenheit)
Mean dew point (.1 Fahrenheit)
Mean sea level pressure (.1 mb)
Mean station pressure (.1 mb)
Mean visibility (.1 miles)
Mean wind speed (.1 knots)
Maximum sustained wind speed (.1 knots)
Maximum wind gust (.1 knots)
Maximum temperature (.1 Fahrenheit)
Minimum temperature (.1 Fahrenheit)
Precipitation amount (.01 inches)
Snow depth (.1 inches)
Indicator for occurrence of: Fog, Rain or Drizzle, Snow or Ice Pellets, Hail, Thunder, Tornado/Funnel Cloud.

Global summary of day data for 18 surface meteorological elements are derived from the synoptic/hourly observations contained in USAF DATSAV3 Surface data and Federal Climate Complex Integrated Surface Hourly (ISH). Historical data are generally available for 1929 to the present, with data from 1973 to the present being the most complete. For some periods, one or more countries' data may not be available due to data restrictions or communications problems. In deriving the summary of day data, a minimum of 4 observations for the day must be present (allows for stations which report 4 synoptic observations/day). Since the data are converted to constant units (e.g, knots), slight rounding error from the originally reported values may occur (e.g, 9.9 instead of 10.0). The mean daily values described below are based on the hours of operation for the station. For some stations/countries, the visibility will sometimes 'cluster' around a value (such as 10 miles) due to the practice of not reporting visibilities greater than certain distances. The daily extremes and totals--maximum wind gust, precipitation amount, and snow depth--will only appear if the station reports the data sufficiently to provide a valid value. Therefore, these three elements will appear less frequently than other values. Also, these elements are derived from the stations' reports during the day, and may comprise a 24-hour period which includes a portion of the previous day. The data are reported and summarized based on Greenwich Mean Time (GMT, 0000Z - 2359Z) since the original synoptic/hourly data are reported and based on GMT.

NOAA HYSPLIT-compatible meteorological data archives

The HYSPLIT model is a complete system for computing simple air parcel trajectories, as well as complex transport, dispersion, chemical transformation, and deposition simulations. HYSPLIT continues to be one of the most extensively used atmospheric transport and dispersion models in the atmospheric sciences community. A common application is a back trajectory analysis to determine the origin of air masses and establish source-receptor relationships. HYSPLIT has also been used in a variety of simulations describing the atmospheric transport, dispersion, and deposition of pollutants and hazardous materials. Some examples of the applications include tracking and forecasting the release of radioactive material, wildfire smoke, windblown dust, pollutants from various stationary and mobile emission sources, allergens and volcanic ash. The National Weather Service's National Centers for Environmental Prediction (NCEP) runs a series of computer analyses and forecasts operationally. NOAA's Air Resources Laboratory (ARL) routinely uses NCEP model data for use in air quality transport and dispersion modeling calculations. In 1989 ARL began to archive some of these datasets for future research studies. ARL has in the past, or is presently archiving the following NCEP datasets that are compatible with HYSPLIT. A few datasets that are created outside NOAA are also included. HYSPLIT-compatible meteorological datasets are required to run HYSPLIT for trajectory or dispersion simulations. The HYSPLIT-compatible meteorological datasets can also be used with HYSPLIT utilities to display and/or extract meteorological data from the datasets.

NOAA Integrated Surface Database (ISD)

The Integrated Surface Database (ISD) consists of global hourly and synoptic observations compiled from numerous sources into a gzipped fixed width format. ISD was developed as a joint activity within Asheville's Federal Climate Complex. The database includes over 35,000 stations worldwide, with some having data as far back as 1901, though the data show a substantial increase in volume in the 1940s and again in the early 1970s. Currently, there are over 14,000 "active" stations updated daily in the database. The total uncompressed data volume is around 600 gigabytes; however, it continues to grow as more data are added. ISD includes numerous parameters such as wind speed and direction, wind gust, temperature, dew point, cloud data, sea level pressure, altimeter setting, station pressure, present weather, visibility, precipitation amounts for various time periods, snow depth, and various other elements as observed by each station.

NOAA MRMS - dynamical.org Icechunk Zarr

The NOAA Multi-Radar/Multi-Sensor System (MRMS) integrates data from multiple radars and radar networks, surface observations, numerical weather prediction (NWP) models, and climatology to generate seamless, high spatio-temporal resolution mosaics at low latency focused on hail, wind, tornado, quantitative precipitation estimations, convection, icing, and turbulence.

These datasets have been translated to cloud-optimized Icechunk Zarr format by dynamical.org.

NOAA MRMS CONUS analysis, hourly - Hourly precipitation analysis from the Multi-Radar Multi-Sensor (MRMS) system operated by NOAA NWS NCEP.

NOAA Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS)

The Multi-Year Reanalysis of Remotely Sensed Storms (MYRORSS) consists of radar reflectivity data run through the Multi-Radar, Multi-Sensor (MRMS) framework to create a three-dimensional radar volume on a quasi-Cartesian latitude-longitude grid across the entire contiguous United States. The radar reflectivity grid is also combined with hourly forecast model analyses to produce derived products such as echo top heights and hail size estimates. Radar Doppler velocity data was also processed into two azimuthal shear layer products. The source radar data was from the NEXRAD Level-II archive and the model analyses came from NOAA's Rapid Update Cycle model. Radar reflectivity was quality controlled to remove non-weather echoes and the data set was manually quality contolled to remove errors as revealed through inspection of daily accumulations of the hail size product and the azimuthal shear products. MYRORSS contains data from April 1998 through December 2011. The horizontal resolution is 0.01° by 0.01° and the vertical spacing is stretched where at the lowest levels the spacing is 250-m and at the top of the domain 1000-m. The radar data was merged at imperfect timesteps, though in general the temporal spacing is around 5-min.

NOAA National Digital Forecast Database (NDFD)

The National Digital Forecast Database (NDFD) is a suite of gridded forecasts of sensible weather elements (e.g., cloud cover, maximum temperature). Forecasts prepared by NWS field offices working in collaboration with the National Centers for Environmental Prediction (NCEP) are combined in the NDFD to create a seamless mosaic of digital forecasts from which operational NWS products are generated. The most recent data is under the opnl and expr prefixes. A copy is also placed under the wmo prefix. The wmo prefix is structured like so: wmo/<parameter>/<year>/<month>/<day>/<wmo-file-name> The wmo filename codes can be deciphered using the spreadsheet in the root of the bucket.

NOAA National Water Model Short-Range Forecast

The National Water Model (NWM) is a water resources model that simulates and forecasts water budget variables, including snowpack, evapotranspiration, soil moisture and streamflow, over the entire continental United States (CONUS). The model, launched in August 2016, is designed to improve the ability of NOAA to meet the needs of its stakeholders (forecasters, emergency managers, reservoir operators, first responders, recreationists, farmers, barge operators, and ecosystem and floodplain managers) by providing expanded accuracy, detail, and frequency of water information. It is operated by NOAA’s Office of Water Prediction. This bucket contains a four-week rollover of the Short Range Forecast model output and the corresponding forcing data for the model. The model is forced with meteorological data from the High Resolution Rapid Refresh (HRRR) and the Rapid Refresh (RAP) models. The Short Range Forecast configuration cycles hourly and produces hourly deterministic forecasts of streamflow and hydrologic states out to 18 hours.

NOAA S-111 Surface Water Currents Data

S-111 is a data and metadata encoding specification that is part of the S-100 Universal Hydrographic Data Model, an international standard for hydrographic data. This collection of data contains surface water currents forecast guidance from NOAA/NOS Operational Forecast Systems, a set of operational hydrodynamic nowcast and forecast modeling systems, for various U.S. coastal waters and the great lakes. The collection also contains surface current forecast guidance output from the NCEP Global Real-Time Ocean Forecast System (GRTOFS) for some offshore areas. These datasets are encoded as HDF-5 files conforming to the S-111 specification, and are geospatially subset into individual tiles conforming to the NOAA/OCS Nautical Product Tiling Scheme, with filenames indicating the corresponding NOAA Electronic Navigational Chart (ENC) Cell Identifier. A full set of S-111 tiles is created for each new model run cycle, which occurs four times per day for all models except for RTOFS, which updates only once per day. Files are organized using a path naming convention that includes the OFS identifier (e.g. 'cbofs' corresponding with output from the Chesapeake Bay Operational Forecast System) as well as the year, month, day, and hour corresponding with each model run initialization time. Each individual S-111 (HDF-5) file contains all forecast projections from a single model run for that geographic area. In other words, a single S-111 file will contain multiple gridded arrays each containing a forecast valid at a distinct time in the future, out to the forecast horizon of the underlying modeling system. All surface currents forecasts in this collection are computed at a depth of 4.5 meters below water surface, or half the water column depth, whichever is shallower.

NOAA U.S. Climate Normals

The U.S. Climate Normals are a large suite of data products that provide information about typical climate conditions for thousands of locations across the United States. Normals act both as a ruler to compare today’s weather and tomorrow’s forecast, and as a predictor of conditions in the near future. The official normals are calculated for a uniform 30 year period, and consist of annual/seasonal, monthly, daily, and hourly averages and statistics of temperature, precipitation, and other climatological variables from almost 15,000 U.S. weather stations.

NCEI generates the official U.S. normals every 10 years in keeping with the needs of our user community and the requirements of the World Meteorological Organization (WMO) and National Weather Service (NWS). The 1991–2020 U.S. Climate Normals are the latest in a series of decadal normals first produced in the 1950s. These data allow travelers to pack the right clothes, farmers to plant the best crop varieties, and utilities to plan for seasonal energy usage. Many other important economic decisions that are made beyond the predictive range of standard weather forecasts are either based on or influenced by climate normals.

NOAA Wave Ensemble Reforecast

This is a 20-year global wave reforecast generated by WAVEWATCH III model (https://github.com/NOAA-EMC/WW3) forced by GEFSv12 winds (https://noaa-gefs-retrospective.s3.amazonaws.com/index.html). The wave ensemble was run with one cycle per day (at 03Z), spatial resolution of 0.25°X0.25° and temporal resolution of 3 hours. There are five ensemble members (control plus four perturbed members) and, once a week (Wednesdays), the ensemble is expanded to eleven members. The forecast range is 16 days and, once a week (Wednesdays), it extends to 35 days. More information about the wave modeling, wave grids and calibration can be found in the WAVEWATCH III regtest ww3_ufs1.3 (https://github.com/NOAA-EMC/WW3/tree/develop/regtests/ww3_ufs1.3).

The 20 years of reforecast results were analyzed and quality-controlled. Three output types are available

Global wave fields, in grib2 format, with several variables including significant wave height, period, direction, and partitions;
Point output tables, in netcdf format, containing time-series of significant wave height, period and direction, for 658 points (latitude/longitude informed) at the positions of wave buoys; and,
For the same positions, spectral outputs are available, in netcdf format, containing the full spectra (2D directional spectrum).
Each file refers to one forecast cycle with date (year, month, day) written in the file name.

This effort has been funded by a NOAA OAR/NWS Service Level Agreement (SLA) whereby NOAA/OAR supports R&D and transition needed for mission delivery of climate services within NWS. The SLA project is led by NWS/NCEP/Ocean Prediction Center in collaboration with NWS/NCEP/Environmental Modeling Center and NWS/NCEP/Climate Prediction Center. This is a project also in cooperation with NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML) and the University of Miami’s Cooperative Institute for Marine and Atmospheric Studies (CIMAS).

The following github repository has examples and scripts to support users to download, visualize, and process the WAVEWATCH III output files

https://github.com/ricampos/gefswaves_reforecast

The reforecast simulations were generated on the supercomputer Orion, which is funded via a grant from NOAA to support research activities in environmental modeling, including weather modeling and simulation

https://www.noaa.gov/organization/information-technology/orion

NOAA nClimGrid and Livneh Gridded Historical Climate Observation Thresholds

Livneh and nClimGrid are gridded observed historical climatology data that were used in the LOCA2 and STAR-ESDM downscaling process of global climate models as part of the 5th National Climate Assessment. The original Livneh and nClimGrid daily temperature and precipitation observations have been converted to a series of decision-relevant thresholds as part of the (U.S. Climate Resilience Information System (CRIS)). These thresholds, such as days with extreme heat or precipitation, have been calculated to match the future projections from LOCA2 and STAR, also available in CRIS.

NOAA/PMEL Ocean Climate Stations Moorings

The mission of the Ocean Climate Stations (OCS) Project is to make meteorological and oceanic measurements from autonomous platforms. Calibrated, quality-controlled, and well-documented climatological measurements are available on the OCS webpage and the OceanSITES Global Data Assembly Centers (GDACs), with near-realtime data available prior to release of the complete, downloaded datasets.

OCS measurements served through the Big Data Program come from OCS high-latitude moored buoys located in the Kuroshio Extension (32°N 145°E) and the Gulf of Alaska (50°N 145°W). Initiated in 2004 and 2007, the respective moored buoys, KEO and Papa, measure a suite of surface and subsurface essential ocean variables. The surface suite includes air temperature, relative humidity, shortwave and longwave radiation, barometric pressure, winds, and rain, while subsurface instrumentation includes temperature, salinity, and ocean currents. Individual buoy deployments are stitched together into a continuous time-series, which is synced to the OceanSITES GDACs, and subsequently, to BDP.

NSF NCAR Curated ECMWF Reanalysis 5 (ERA5)

NSF NCAR is providing a NetCDF-4 structured version of the 0.25 degree atmospheric ECMWF Reanalysis 5 (ERA5) to the AWS ODSP. ERA5 is produced using high-resolution forecasts (HRES) at 31 kilometer resolution (one fourth the spatial resolution of the operational model) and a 62 kilometer resolution ten member 4D-Var ensemble of data assimilation (EDA) in CY41r2 of ECMWF's Integrated Forecast System (IFS) with 137 hybrid sigma-pressure (model) levels in the vertical, up to a top level of 0.01 hPa. Atmospheric data on these levels are interpolated to 37 pressure levels (the same levels as in ERA-Interim). Surface or single level data are also available, containing 2D parameters such as precipitation, 2 meter temperature, top of atmosphere radiation and vertical integrals over the entire atmosphere. The IFS is coupled to a soil model, the parameters of which are also designated as surface parameters, and an ocean wave model. Generally, the data is available at an hourly frequency and consists of analyses and short (12 hour) forecasts, initialized twice daily from analyses at 06 and 18 UTC. Most analyses parameters are also available from the forecasts. The data are provided in CF-compliant NetCDF4 format files. ERA5 products are used to train ML/AI based weather forecast models and support retrospective climate research use cases, including where to locate solar and wind farms.

NYUMets Brain Dataset

This dataset contains 8,000+ brain MRIs of 2,000+ patients with brain metastases.

National Herbarium of Israel

Our collection encompasses approximately one million vascular plant specimens from the Mediterranean and Middle East biodiversity hotspot, representing flora from Israel, Jordan, Hermon, Sinai, Egypt, the Caucasus, Arabia, North Africa, and throughout the Mediterranean basin. This scientifically significant repository includes published voucher specimens, original specimens used for "Flora Palaestina" illustrations, and critical references for the Israeli gene bank collections. The ongoing digitization process captures high-resolution images of each specimen while systematically incorporating label information into our computerized catalog. This virtual herbarium will democratize access to these valuable botanical resources, enabling global researchers to examine specimens in exceptional detail from anywhere in the world. Beyond preservation, this digital transformation unlocks new research possibilities through computational analysis of both visual specimen characteristics and associated metadata. The dataset will serve as a foundational resource for advancing botanical research, ecological modeling, taxonomic investigation, historical analysis, and numerous other scientific disciplines concerned with plant biodiversity in this ecologically and historically significant region.

Natural Earth

Natural Earth is a public domain map dataset available at 1:10m, 1:50m, and 1:110 million scales. Featuring tightly integrated vector and raster data, with Natural Earth you can make a variety of visually pleasing, well-crafted maps with cartography or GIS software.

New Jersey Statewide Digital Aerial Imagery Catalog

The New Jersey Office of GIS, NJ Office of Information Technology manages a series of 11 digital orthophotography and scanned aerial photo maps collected at various years ranging from 1930 to 2017. Each year’s worth of imagery are available as Cloud Optimized GeoTIFF (COG) files and some years are available as compressed MrSID and/or JP2 files. Additionally, each year of imagery is organized into a tile grid scheme covering the entire geography of New Jersey. Many years share the same tiling grid while others have unique grids as defined by the project at the time.

New Jersey Statewide LiDAR

Elevation datasets in New Jersey have been collected over several years as several discrete projects. Each project covers a geographic area, which is a subsection of the entire state, and has differing specifications based on the available technology at the time and project budget. The geographic extent of one project may overlap that of a neighboring project. Each of the 18 projects contains deliverable products such as LAS (Lidar point cloud) files, unclassified/classified, tiled to cover project area; relevant metadata records or documents, most adhering to the Federal Geographic Data Committee’s (FGDC) Content Standard for Digital Geospatial Metadata (CSDGM); tiling index feature class or shapefile; flights lines feature class or shapefile; Digital Elevation Model in image format or Esri grid format; other derivative data products such as contour lines feature class or shapefile.

OPERA Surface Displacement from Sentinel-1 validated product (Version 1)

The Level-3 OPERA Sentinel-1 Surface Displacement (DISP) product is generated through interferometric time-series analysis of Level-2 Coregistered Sentinel-1 Single Look Complex (CSLC) datasets. Using a hybrid Persistent Scatterer (PS) and Distributed Scatterer (DS) approach, this product quantifies Earth's surface displacement in the radar line-of-sight. The DISP products enable the detection of anthropogenic and natural surface changes, including subsidence, tectonic deformation, and landslides. The OPERA DISP suite comprises complementary datasets derived from Sentinel-1 and NISAR inputs, designated as DISP-S1 and DISP-NI, respectively. Each product, created per acquisition, adheres to a consistent structure, HDF5 file format, file-naming convention, and a 30 m spatial posting. This collection specifically includes DISP-S1 products, derived from Sentinel-1 data. DISP-S1 products provide spatial coverage across North America, encompassing the United States, U.S. territories within 200 km of the U.S. border, Canada, and mainland countries from the southern U.S. border to Panama. These products are generated from Sentinel-1 Interferometric Wide (IW) swath mode acquisitions starting in mid-2016. The OPERA DISP-S1 product contains modified Copernicus Sentinel data (2016-2025). Read our doc on how to get AWS Credentials to retrieve this data: https://cumulus.asf.alaska.edu/s3credentialsREADME

Ohio State Cardiac MRI Raw Data (OCMR)

OCMR is an open-access repository that provides multi-coil k-space data for cardiac cine. The fully sampled MRI datasets are intended for quantitative comparison and evaluation of image reconstruction methods. The free-breathing, prospectively undersampled datasets are intended to evaluate their performance and generalizability qualitatively.

OpenNeuro

OpenNeuro is a database of openly-available brain imaging data. The data are shared according to a Creative Commons CC0 license, providing a broad range of brain imaging data to researchers and citizen scientists alike. The database primarily focuses on functional magnetic resonance imaging (fMRI) data, but also includes other imaging modalities including structural and diffusion MRI, electroencephalography (EEG), and magnetoencephalograpy (MEG). OpenfMRI is a project of the Center for Reproducible Neuroscience at Stanford University. Development of the OpenNeuro resource has been funded by the National Science Foundation, National Institute of Mental Health, National Institute on Drug Abuse, and the Laura and John Arnold Foundation.

OpenSurfaces

A large database of annotated surfaces created from real-world consumer photographs.

OpenUniverse 2024 Simulated Roman & Rubin Images

This release consists of simulated data products designed to mimic observations of the same region of the sky as seen by two astronomical facilities: the Nancy Grace Roman Telescope and the Vera C. Rubin Observatory.

Orcasound - bioacoustic data for marine conservation

Live-streamed and archived audio data (~2018-present) from underwater microphones (hydrophones) containing marine biological signals as well as ambient ocean noise. Hydrophone placement and passive acoustic monitoring effort prioritizes detection of orca sounds (calls, clicks, whistles) and potentially harmful noise. Geographic focus is on the US/Canada critical habitat of Southern Resident killer whales (northern CA to central BC) with initial focus on inland waters of WA. In addition to the raw lossy or lossless compressed data, we provide a growing archive of annotated bioacoustic bouts.

Oxford Nanopore Technologies Benchmark Datasets

The ont-open-data registry provides reference sequencing data from Oxford Nanopore Technologies to support, 1) Exploration of the characteristics of nanopore sequence data. 2) Assessment and reproduction of performance benchmarks 3) Development of tools and methods. The data deposited showcases DNA sequences from a representative subset of sequencing chemistries. The datasets correspond to publicly-available reference samples (e.g. Genome In A Bottle reference cell lines). Raw data are provided with metadata and scripts to describe sample and data provenance.

PALSAR-2 ScanSAR CARD4L (L2.2)

The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broad area observation mode with observation width of 350 km. The SAR imagery was ortho-rectificatied and slope corrected using the ALOS World 3D - 30 m (AW3D30) Digital Surface Model. Polarization data are stored as 16-bit digital numbers (DN). The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation: γ0 = 10*log10(DN2) - 83.0 dB CARD4L stands for CEOS Analysis Ready Data for Land (Level 2.2) data are ortho-rectified and radiometrically terrain-corrected. This dataset is compatible with the Committee on Earth Observation (CEOS) Analysis Ready Data for LAND (CARD4L) standard.

PALSAR-2 ScanSAR Flooding in Rwanda (L2.1)

Torrential rainfall triggered flooding and landslides in many parts of Rwanda. The hardest-hit districts were Ngororero, Rubavu, Nyabihu, Rutsiro and Karongi. According to reports, 14 people have died in Karongi, 26 in Rutsiro, 18 in Rubavu, 19 in Nyabihu and 18 in Ngororero.Rwanda National Police reported that the Mukamira-Ngororero and Rubavu-Rutsiro roads are impassable due to flooding and landslide debris. UNITAR on behalf of United Nations Office for the Coordination of Humanitarian Affairs (OCHA) / Regional Office for Southern & Eastern Africa in cooperation with Rwanda Space Agency (RSA) was activated International Disaster Charter. JAXA has responded to the flood event in Rwanda by conducting emergency disaster observations and providing data as requested by OCHA and RSA through the International Disaster Charter. The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broad area observation mode with observation width of 350 km. Polarization data are stored as 16-bit digital numbers (DN). The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation sigma zero = 10*log10(DN2) - 83.0 dB. Included in this dataset are ALOS-2 PALSAR-2 ScanSAR 2.1 data. Level 2.1 data is orthorectified from level 1.1 data by using digital elevation model. Pixel spacing is selectable depending on observation modes. Image coordinate in map projection is geocoded.

PALSAR-2 ScanSAR Tropical Cycolne Mocha (L2.1)

Tropical Cyclone Mocha began to form in the Bay of Bengal on 11 May 2023 and continues to intensify as it moves towards Myanmar and Bangladesh.Cyclone Mocha is the first storm to form in the Bay of Bengal this year and is expected to hit several coastal areas in Bangladesh on 14 May with wind speeds of up to 175 km/h.After made its landfall in the coast between Cox’s Bazar (Bangladesh) and Kyaukphyu (Myanmar) near Sittwe (Myanmar). At most, Catastrophic Damage-causing winds was possible especially in the areas of Rakhine State and Chin State, and Severe Damage-causing winds is possible in the areas of Rakhine, Chin, Magway, and Sagaing ([source] TAOS Model, DisasterAWARE). Bangladesh were preparing to evacuate over 500,000 people as the World Meteorological Organisation (WMO) has warned of big humanitarian impacts once the storm makes landfall. Due to its intensity, Cyclone Mocha is expected to inundate several low-lying areas of the delta nation of Bangladesh which could consequently cause landslides.576 cyclone shelters are ready to provide refuge to those evacuated however damage to infrastructure and agriculture would be devastating.Myanmar ? POTENTIAL OF A CATASTROPHIC DISASTER. An estimated 8.7 Million people, 1.9M households, and $35.3 Billion (USD) of infrastructure (total replacement value) were potentially exposed to moderate to severe damaging winds in accordance with AHA Centre.JAXA has responded to the Tropical Cyclone MOCHA by conducting emergency disaster observations and providing data as requested through the International Disaster Charter and Sentinel Asia. The 25 m PALSAR-2 ScanSAR is normalized backscatter data of PALSAR-2 broad area observation mode with observation width of 350 km. Polarization data are stored as 16-bit digital numbers (DN). The DN values can be converted to gamma naught values in decibel unit (dB) using the following equation sigma zero = 10*log10(DN2) - 83.0 dB. Included in this dataset are ALOS-2 PALSAR-2 ScanSAR 2.1 data. Level 2.1 data is orthorectified from level 1.1 data by using digital elevation model. Pixel spacing is selectable depending on observation modes. Image coordinate in map projection is geocoded.

Quoref

24K Question/Answer (QA) pairs over 4.7K paragraphs, split between train (19K QAs), development (2.4K QAs) and a hidden test partition (2.5K QAs).

RSNA Abdominal Trauma Detection (RSNA-ABT)

Blunt force abdominal trauma is among the most common types of traumatic injury, with the most frequent cause being motor vehicle accidents. Abdominal trauma may result in damage and internal bleeding of the internal organs, including the liver, spleen, kidneys, and bowel. Detection and classification of injuries are key to effective treatment and favorable outcomes. A large proportion of patients with abdominal trauma require urgent surgery. Abdominal trauma often cannot be diagnosed clinically by physical exam, patient symptoms, or laboratory tests. Prompt diagnosis of abdominal trauma using medical imaging is thus critical to patient care. AI tools that assist and expedite diagnosis of abdominal trauma have the potential to substantially improve patient care and health outcomes in the emergency setting. To create the ground truth dataset, RSNA collected imaging data sourced from 23 sites in 14 countries on six continents, including more than 4,000 CT exams with various abdominal injuries and a roughly equal number of cases without injury.

RSNA Abdominal Traumatic Injury CT (RATIC)

RSNA Cervical Spine Fracture Detection (RSNA-CSF) Dataset

Over 1.5 million spine fractures occur annually in the United States alone resulting in over 17,730 spinal cord injuries annually. The most common site of spine fracture is the cervical spine. There has been a rise in the incidence of spinal fractures in the elderly and in this population, fractures can be more difficult to detect on imaging due to degenerative disease and osteoporosis. Imaging diagnosis of adult spine fractures is now almost exclusively performed with computed tomography (CT). Quickly detecting and determining the location of any vertebral fractures is essential to prevent neurologic deterioration and paralysis after trauma. RSNA has teamed with the American Society of Neuroradiology (ASNR) and the American Society of Spine Radiology (ASSR) to create this ground truth dataset, collecting imaging data from twelve sites on six continents, including approximately 2,000 CT studies. Spine radiology specialists from the ASNR and ASSR provided expert image level annotations these studies to indicate the presence, vertebral level and location of any cervical spine fractures.

RSNA Intracranial Aneurysm Detection Dataset (RSNA-ICA)

The Radiological Society of North America Intracranial Aneurysm Detection (RSNA-ICA) dataset is a collection of over 4,000 CT brain scans annotated by a cohort of over 40 volunteer radiologists from RSNA and the American Society of Neuroradiology to show the presence and location of intracranial aneurysms. It also includes a set of about 200 imaging studies that are annotated with AI-generated segmentations highlighting abnormalities. The imaging data was provided by 18 institutions. Initially compiled in 2025 for the RSNA Intracranial Aneurysm Detection AI Challenge hosted on Kaggle competition platform (https://www.kaggle.com/competitions/rsna-intracranial-aneurysm-detection), it represents the largest publicly available collection of its kind. Additional information on the dataset and how to make use of it is provided in a forthcoming Data Resource Publication listed below, as well as on the Kaggle competition website, which also provides access to models developed during the competition.

RSNA Intracranial Hemorrhage Detection

RSNA assembled this dataset in 2019 for the RSNA Intracranial Hemorrhage Detection AI Challenge (https://www.kaggle.com/c/rsna-intracranial-hemorrhage-detection/). De-identified head CT studies were provided by four research institutions. A group of over 60 volunteer expert radiologists recruited by RSNA and the American Society of Neuroradiology labeled over 25,000 exams for the presence and subtype classification of acute intracranial hemorrhage.

RSNA Lumbar Spine Degenerative Classification Dataset (RSNA-LSDD)

The Radiological Society of North America Lumbar Spine Degenerative Classification dataset (RSNA-LSDD) is a collection of over 2,600 magnetic resonance imaging (MR) scans of the lumbar spine annotated by a cohort of about 60 volunteer radiologists recruited by the RSNA, the American Society for Spine Radiology and the American Society of Neuroradiology to identify the location and severity of five degenerative conditions across the five intervertebral disc levels (L1/L2, L2/L3, L3/L4, L4/L5, and L5/S1). The imaging data, comprising over 8,500 image series (Sagittal “T2”, Axial T2 and Sagittal T1), was provided by twelve institutions from across the globe. Initially compiled in 2024 for the RSNA Lumbar Spine Degenerative Classification AI Challenge hosted on Kaggle competition platform (https://www.kaggle.com/competitions/rsna-2024-lumbar-spine-degenerative-classification), it represents the largest publicly available collection of its kind. Additional information on the dataset and how to make use of it is provided in the Data Resource Publication listed below, as well as on the Kaggle competition website, which also provides access to models developed during the competition.

RSNA Pulmonary Embolism Detection

RSNA assembled this dataset in 2020 for the RSNA STR Pulmonary Embolism Detection AI Challenge (https://www.kaggle.com/c/rsna-str-pulmonary-embolism-detection/). With more than 12,000 CT pulmonary angiography (CTPA) studies contributed by five international research centers, it is the largest publicly available annotated PE dataset. RSNA collaborated with the Society of Thoracic Radiology to recruit more than 80 expert thoracic radiologists who labeled the dataset with detailed clinical annotations.

Reasoning Over Paragraph Effects in Situations (ROPES)

14k QA pairs over 1.7K paragraphs, split between train (10k QAs), development (1.6k QAs) and a hidden test partition (1.7k QAs).

SENTINEL-1A_DUAL_POL_GRD_HIGH_RES

Sentinel-1A Dual-pol ground projected high and full resolution images Read our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME

SENTINEL-1A_SLC

Sentinel-1A slant-range product Read our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME

SENTINEL-1B_DUAL_POL_GRD_HIGH_RES

Sentinel-1B Dual-pol ground projected high and full resolution images Read our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME

SENTINEL-1B_SLC

Sentinel-1B slant-range product Read our doc on how to get AWS Credentials to retrieve this data: https://sentinel1.asf.alaska.edu/s3credentialsREADME

SILAM Air Quality

Air Quality is a global SILAM atmospheric composition and air quality forecast performed on a daily basis for > 100 species and covering the troposphere and the stratosphere. The output produces 3D concentration fields and aerosol optical thickness. The data are unique: 20km resolution for global AQ models is unseen worldwide.

SPHEREx Quick Release (QR): An All-Sky Spectral Survey

The Spectro-Photometer for the History of the Universe, Epoch of Reionization, and Ices Explorer (SPHEREx) is a NASA Astrophysics Medium-class Explorer (MIDEX) mission launched in March 2025. During its planned two-year mission, SPHEREx will perform the first ever all-sky spectral survey in the optical to near-infrared (0.75-5 microns). SPHEREx data will be used to probe inflation and the early universe, trace the history of galactic light production, and investigate the origin of planetary systems and biogenic ices, in addition to contributing to many other astrophysics research topics. IRSA began releasing SPHEREx QR2 data on a weekly basis in October 2025. QR2 features substantially improved calibrations and supersedes QR1.

Safecast

An ongoing collection of radiation and air quality measurements taken by devices involved in the Safecast project.

Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD)

The Seattle Alzheimer's Disease Brain Cell Atlas (SEA-AD) consortium strives to gain a deep molecular and cellular understanding of the early pathogenesis of Alzheimer's disease and is funded by the National Institutes on Aging (NIA U19AG060909). The SEA-AD datasets available here comprise single cell profiling (transcriptomics and epigenomics) and quantitative neuropathology. To explore gene expression and chromatin accessibility information, the single-cell profiling data includes: snRNAseq and snATAC-seq data from the SEA-AD donor cohort (aged brains which span the spectrum of Alzheimer's Disease pathology) and neurotypical reference brains. To explore key pathological proteins and cell types of interest to Alzheimer's disease, the neuropathology data includes: full resolution brightfield images, images processed and segmented in HALO image analysis software, image annotations, and quantification summary files for the relevant stains including Abeta (6E10), IBA1, a-Synuclein, GFAP, H&E-LFB, NeuN, pTau(AT8), and pTDP43.

Sentinel-1 SLC dataset for Germany

The Sentinel1 Single Look Complex (SLC) unzipped dataset contains Synthetic Aperture Radar (SAR) data from the European Space Agency’s Sentinel-1 mission. Different from the zipped data provided by ESA, this dataset allows direct access to individual swaths required for a given study area, thus drastically minimizing the storage and downloading time requirements of a project. Since the data is stored on S3, users can utilize the boto3 library and s3 get_object method to read the entire content of the object into the memory for processing, without actually having to download it. The Sentinel-1 constellation consists of two satellites equipped with SAR sensors and a combined revisit time of six days. SAR imagery gets recorded regardless of weather conditions and daylight, which makes it ideally suited for monitoring land-use changes, surface deformations, land applications, oil spills, sea-ice, natural hazards, and for emergency response. In its current first stage, the dataset covers the entirety of Germany and is being updated continuously. As a next stage, the dataset will provide up-to-date coverage of the sentinel-1 SLC data over Europe. This dataset is retrieved from Alaska Satellite Facility (ASF) and consists of all Sentinel1-SLC imagery from the beginning (2014) to present.

Single-Cell Atlas of Human Blood During Healthy Aging

Comprehensive, large-scale single-cell profiling of healthy human blood at different ages is one of the critical pending tasks required to establish a framework for systematic understanding of human aging. Here, using single-cell RNA/TCR/BCR-seq with protein feature barcoding (20 antibodies), we profiled 317 samples from 166 healthy individuals aged 25 to 85 years old drawn over 3-year period. Dataset spanning ~2 million cells describes 50 subpopulations of blood immune cells, with 14 subpopulations changing with age, including a novel NKG2C+ CD8 Tcm population that decreases with age. We describe age-associated accumulation of Th2 and HLA-DR+ memory CD4 T cells, CCR4+ CD8 Tcm cells and GZMK+ CD8 Tem cells. We validate key findings using 30-plex spectral cytometry panel. We characterize patterns of antigen receptor clonality across subpopulations of T and B cells and describe their age-dependence. Our work provides novel insights into healthy human aging and unique annotated resource of unprecedented depth.

SpaceEye-T VVHR EO Open Data

SpaceEye-T satellite collects the highest resolution optical imagery among the commercial satellites, 25 cm resolution. The Open Data features various satellite images around the world for end users to experience the power of VVHR optical data.

Spitzer Enhanced Imaging Products (SEIP) Super Mosaics

Spitzer was an infrared astronomy space telescope with imaging from 3 to 160 microns and spectroscopy from 5 to 37 microns, launched into an Earth-trailing solar orbit as the last of NASA's Great Observatories. The SEIP Super Mosaics include data from the four channels of IRAC (3.6, 4.5, 5.8, 8 microns) and the 24 micron channel of MIPS. Data from multiple programs are combined where appropriate. Cryogenic Release v3.0 includes Spitzer data taken during commissioning and cryogenic operations, including calibration data.

Sup3rCC

Released to the public as part of the Department of Energy's Open Energy Data Initiative, these data represent a serially complete collection of hourly 4km wind, solar, temperature, humidity, and pressure fields for the Continental United States under climate change scenarios.Sup3rCC is downscaled Global Climate Model (GCM) data. For example, the initial file set tagged "sup3rcc_conus_mriesm20_ssp585_r1i1p1f1" is downscaled from MRI ESM 2.0 for climate change scenario SSP5 8.5 and variant label r1i1p1f1. The downscaling process is performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data. The data includes both historical and future weather years, although the historical years represent the historical average climate, not true historical weather. The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar power generation. Please note that all climate change data is only a representation of the possible future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty.

Swiss Public Transport Stops

The basic geo-data set for public transport stops comprises public transport stops in Switzerland and additional selected geo-referenced public transport locations that are of operational or structural importance (operating points).

Synthea Coherent Data Set

This is a synthetic data set that includes FHIR resources, DICOM images, genomic data, physiological data (i.e., ECGs), and simple clinical notes. FHIR links all the data types together.

Tabula Muris

Tabula Muris is a compendium of single cell transcriptomic data from the model organism Mus musculus comprising more than 100,000 cells from 20 organs and tissues. These data represent a new resource for cell biology, reveal gene expression in poorly characterized cell populations, and allow for direct and controlled comparison of gene expression in cell types shared between tissues, such as T-lymphocytes and endothelial cells from different anatomical locations. Two distinct technical approaches were used for most organs: one approach, microfluidic droplet-based 3’-end counting, enabled the survey of thousands of cells at relatively low coverage, while the other, FACS-based full length transcript analysis, enabled characterization of cell types with high sensitivity and coverage. The cumulative data provide the foundation for an atlas of transcriptomic cell biology. See: https://www.nature.com/articles/s41586-018-0590-4

Tabula Muris Senis

Tabula Muris Senis is a comprehensive compendium of single cell transcriptomic data from the model organism Mus musculus comprising more than 500,000 cells from 18 organs and tissues across the mouse lifespan. We discovered cell-specific changes occurring across multiple cell types and organs, as well as age related changes in the cellular composition of different organs. Using single-cell transcriptomic data we were able to assess cell type specific manifestations of different hallmarks of aging, such as senescence, changes in the activity of metabolic pathways, depletion of stem-cell populations, genomic instability and the role of inflammation as well as other changes in the organism’s immune system. Tabula Muris Senis provides a wealth of new molecular information about how the most significant hallmarks of aging are reflected in a broad range of tissues and cell types.See: https://www.biorxiv.org/content/10.1101/661728v1

The JWST Advanced Extragalactic Survey JADES

JADES is an infrared imaging and multi-object spectroscopy survey focused on two deep fields: the Hubble Deep Field (GOODS-N) and Hubble Ultra Deep Field (GOODS-S). JADES conducted NIRCam imaging in 8-10 bands, covering about 42 square arcminutes to very deep limits (fainter than 30th magnitude) with an average of about 100 hours of total exposure time, and then another 167 square arcminutes to a typical exposure time of 25 hrs. Coordinated parallels with the MIRI instrument extend this imaging further into the infrared in smaller regions. JADES then performed extensive NIRSpec spectroscopy with over 5000 targets on 31 separate pointings, including two deep exposures of 55 hrs spread across 5 dispersers. The JADES team added additional imaging and spectroscopy in Cycles 1, 2, and 3 from a number of affiliated guest observer programs.

U.S. Census ACS PUMS

U.S. Census Bureau American Community Survey (ACS) Public Use Microdata Sample (PUMS) available in a linked data format using the Resource Description Framework (RDF) data model.

UCSF Primary Central Nervous System Lymphoma MRI Dataset

This BIDS-formatted dataset provides multimodal brain MRI data from 150 patients with primary central nervous system lymphoma (PCNSL), including T1-weighted, contrast-enhanced T1-weighted, FLAIR, and ADC sequences. The dataset includes expert-annotated lesion segmentations with radiomic features, along with anonymized clinical data including demographics, diagnosis history, and medications.

UK Biobank Pharma Proteomics Project (UKB-PPP)

The UKB-PPP is a collaboration between the UK Biobank (UKB) and thirteen biopharmaceutical companies characterising the plasma proteomic profiles of 54,219 UKB participants. As part of a collaborative analysis across the thirteen UKB-PPP partners, we conducted comprehensive protein quantitative trait loci (pQTL) mapping of 2,923 proteins that identifies 14,287 primary genetic associations, of which 85% are newly discovered, in addition to ancestry-specific pQTL mapping in non-Europeans. We identify independent secondary associations in 87% of cis and 30% of trans loci, expanding the catalogue of genetic instruments for downstream analyses.

Unblurred Coadds of the Wide-field Infrared Survey Explorer (unWISE)

unWISE is a reprocessing of Wide-field Infrared Survey Explorer (WISE) data which preserves the native angular resolution and is optimized for forced photometry. WISE was a NASA satellite producing all-sky imaging in four infrared bands centered at 3.4, 4.6, 12 and 22 microns (W1, W2, W3, and W4) starting in 2010 until the coolant was exhausted in 2011. It was reactivated in 2013 as NEOWISE and continued imaging in W1 and W2 until 2024.

Virtual Shizuoka, 3D Point Cloud Data

This dataset comprises high-precision 3D point cloud data that encompasses the entire Shizuoka prefecture in Japan, covering 7,200 out of its 7,777 square kilometers. The data is produced through aerial laser survey, airborne laser bathymetry and mobile mapping systems, the culmination of many years of dedicated effort.This data will be visualized and analyzed for use in infrastructure maintenance, disaster prevention measures and autonomous vehicle driving.

VitalDB

VitalDB, a high-fidelity multi-parameter vital signs database in surgical patients.

Voices Obscured in Complex Environmental Settings (VOiCES)

VOiCES is a speech corpus recorded in acoustically challenging settings, using distant microphone recording. Speech was recorded in real rooms with various acoustic features (reverb, echo, HVAC systems, outside noise, etc.). Adversarial noise, either television, music, or babble, was concurrently played with clean speech. Data was recorded using multiple microphones strategically placed throughout the room. The corpus includes audio recordings, orthographic transcriptions, and speaker labels.

World Bank Climate Change Knowledge Portal (CCKP)

CCKP provides open access to a comprehensive suite of climate and climate change resources derived from the latest generation of climate data archives. Products are based on a consistent and transparent approach with a systematic way of pre-processing the raw observed and model-based projection data to enable inter-comparable use across a broad range of applications. Climate products consist of basic climate variables as well as a large collection (70+) of more specialized, application-orientated variables and indices across different scenarios. Precomputed data can be extracted per specified variables, select timeframes, climate projection scenarios, across ensembles or individual models, etc. CCKP adheres to data distributions standards defined under the Coupled Model Intercomparison Project (CMIP) and its contributions to the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports and latest scientific methodologies identified by the World Meteorological Organization and climate science community. Climate products are available for the following collections. Downscaled CMIP6 global 0.25-degree – 1950-2100; ERA5 global 0.25-degree – 1950-2022; CRU global 0.50-degree – 1901-2022; Population global 0.25-degree – 1995-2100 (GPW v4).

Xiph.Org Test Media

Uncompressed video used for video compression and video processing research.

ZINC Database

3D models for molecular docking screens.

iHART Whole Genome Sequencing Data Set

iHART is the Hartwell Foundation’s Autism Research and Technology Initiative. This release contains whole genome data from over 1000 families with 2 or more children with autism, of which biomaterials were provided by the Autism Genetic Resource Exchange (AGRE).

recount3

recount3 is an online resource consisting of RNA-seq gene, exon, and exon-exon junction counts as well as coverage bigWig files for 8,679 and 10,088 different studies for human and mouse respectively. It is the third generation of the ReCount project and part of recount.bio. recount2 is also included for historical purposes. The pipeline used to generate the data in recount3 (but not recount2) is available here.

(EXPERIMENTAL) NOAA FourCastNet Global Forecast System (FourCastNetGFS) (EXPERIMENTAL)

The FourCastNet Global Forecast System (FourCastNetGFS) is an experimental system set up by the National Centers for Environmental Prediction (NCEP) to produce medium range global forecasts. The model runs on a 0.25 degree latitude-longitude grid (about 28 km) and 13 pressure levels. The model produces forecasts 4 times a day at 00Z, 06Z, 12Z and 18Z cycles. Major atmospheric and surface fields including temperature, wind components, geopotential height, relative humidity and 2 meter temperature and 10 meter winds are available. The products are 6 hourly forecasts up to 10 days. The data format is GRIB2.

The FourCastNetGFS system is an experimental weather forecast model built upon the pre-trained Nvidia’s FourCastNet Machine Learning Weather Prediction (MLWP) model version 2. The FourCastNet (Bonev et al, 2023) was developed by Nvidia using Adaptive Fourier Neural Operators. It uses a Fourier transform-based token-mixing scheme with the vision transformer architecture. This model is pre-trained with ECMWF’s ERA5 reanalysis data. The FourCastNetGFS takes one model state as initial condition from NCEP 0.25 degree GDAS analysis data and runs FourCastNet with weights from the pretrained FourCastNet by Nvidia. Unit conversion to the GDAS data is conducted to match the input data required by FourCastNet and to generate forecast products consistent to GFS.

The input data generated from the GDAS data as FourCastNet input is provided under the forecast data directory. Example of file names is:

input_2024022000.npy

There are 40 files under each directory covering a 10 day forecast. An example of file name is listed below

fcngfs.t00z.pgrb2.0p25.f006

Please note that this NOAA GFS machine learning Model was produced using a code package released by ECMWF’s ai_models_fourcastnetv2 plugin. For information on ai_models_fourcastnetv2 plugin, please visit their github page listed in the documentation and license sections of this page.

2020 Redistricting Data File Least Squares Estimates

The 2020 Redistricting Data File Least Squares Estimates data product provides count estimates, and their standard deviations, for each tabulation that was published as part of the persons universe of the 2020 Redistricting Data File for the US, state, county, and tract geographic levels. These estimates are computed using the generalized least squares (GLS) estimator using as input the publicly available 2020 Census persons universe noisy measurement files for both the Redistricting Data File and the Demographic and Housing Characteristics File. The algorithm used to compute this estimate is described in more detail in Least Squares Estimation For Hierarchical Data. As described in more detail in this paper and the README below, the primary goal of this data product is to provide the data required to compute confidence intervals for the 2020 Census Redistricting Data File published counts that account for the uncertainty in these published counts due to the disclosure avoidance methods applied to 2020 Census tabulations. In other words, these confidence intervals do not account for other sources of error, such as those due to enumeration errors.

A Realistic Cyber Defense Dataset (CSE-CIC-IDS2018)

This dataset is the result of a collaborative project between the Communications Security Establishment (CSE) and The Canadian Institute for Cybersecurity (CIC) that use the notion of profiles to generate cybersecurity dataset in a systematic manner. It incluides a detailed description of intrusions along with abstract distribution models for applications, protocols, or lower level network entities. The dataset includes seven different attack scenarios, namely Brute-force, Heartbleed, Botnet, DoS, DDoS, Web attacks, and infiltration of the network from inside. The attacking infrastructure includes 50 machines and the victim organization has 5 departments includes 420 PCs and 30 servers. This dataset includes the network traffic and log files of each machine from the victim side, along with 80 network traffic features extracted from captured traffic using CICFlowMeter-V3. For more information on the creation of this dataset, see this paper by researchers at the Canadian Institute for Cybersecurity (CIC) and the University of New Brunswick (UNB): Toward Generating a New Intrusion Detection Dataset and Intrusion Traffic Characterization.

AI2 TabMCQ: Multiple Choice Questions aligned with the Aristo Tablestore

9092 crowd-sourced science questions and 68 tables of curated facts

AI2 Tablestore (November 2015 Snapshot)

68 tables of curated facts

Aristo Mini Corpus

1,197,377 science-relevant sentences

Aristo Tuple KB

294,000 science-relevant tuples

Australasian Genomes

Australasian Genomes is the genomic data repository for the Threatened Species Initiative (TSI) and the ARC Centre for Innovations in Peptide and Protein Science (CIPPS). This repository contains reference genomes, transcriptomes, resequenced genomes and reduced representation sequencing data from Australasian species. Australasian Genomes is managed by the Australasian Wildlife Genomics Group (AWGG) at the University of Sydney on behalf of our collaborators within TSI and CIPPS.

Baby Open Brains (BOBs) Repository on AWS

Manually curated and reviewed infant brain segmentations and accompanying T1w and T2w images for a range of 1-9 month old participants from the Baby Connectome Project (BCP)

BioLiP

BioLiP is a semi-manually curated database for high-quality, biologically relevant ligand-protein binding interactions. The structure data are collected primarily from the Protein Data Bank (PDB), with biological insights mined from literature and other specific databases. BioLiP aims to construct the most comprehensive and accurate database for serving the needs of ligand-protein docking, virtual ligand screening and protein function annotation.

Brain Data Science Database 1

This collection unifies multiple brain datasets spanning critical care, sleep medicine, cardiopulmonary health, infectious diseases, and other aspects of clinical neuroscience. It includes a variety of types of clinical neuroscience data including electroencephalography (EEG) and polysomnography (PSG) recordings, and supporting data to enable research in diverse areas of clinical neuroscience such as epilepsy, delirium, coma, and sleep medicine. All data is de-identified and includes code to reproduce results in accompanying research publications. The data is available for non-commercial research use and is open access.

Brain Data Science Database 2

This collection unifies multiple brain datasets spanning critical care, sleep medicine, cardiopulmonary health, infectious diseases, and other aspects of clinical neuroscience. It includes large-scale electroencephalography (EEG) and polysomnography (PSG) recordings, brain imaging data (MRI, CT, PET), and supporting data to enable research in diverse areas of clinical neuroscience such as epilepsy, delirium, coma, sleep depth, sleep-related breathing disorders, meditation, subarachnoid hemorrhage, cardiac arrest, neuroinfectious diseases, and audiology. All data is de-identified and includes algorithmic tools and reproducible code. Access requires signing a data use agreement that prohibits re-identification, requires data security, restricts sharing, limits use to non-commercial research, and mandates reporting any potentially identifying information.

Brain Data Science Database 3

This collection unifies multiple brain datasets spanning critical care, sleep medicine, cardiopulmonary health, infectious diseases, and other aspects of clinical neuroscience. It includes large-scale electroencephalography (EEG) and polysomnography (PSG) recordings, brain imaging data (MRI, CT, PET), and electronic health records (EHR) data supporting research in areas such as epilepsy, delirium, coma, sleep depth, sleep-related breathing disorders, burst suppression, meditation, subarachnoid hemorrhage, cardiac arrest, and neuroinfectious diseases. All data is de-identified and includes algorithmic tools and reproducible code. Access requires (1) submitting proof of CITI training certification (research ethics training for human subjects research - see https://bdsp.io/about/citi-course/); (2) signing a data use agreement that prohibits re-identification, requires data security, restricts sharing, limits use to non-commercial research, and mandates reporting any potentially identifying information.

CAFE60 reanalysis

The CSIRO Climate retrospective Analysis and Forecast Ensemble system: version 1 (CAFE60v1) provides a large ensemble retrospective analysis of the global climate system from 1960 to present with sufficiently many realizations and at spatio-temporal resolutions suitable to enable probabilistic climate studies. Using a variant of the ensemble Kalman filter, 96 climate state estimates are generated over the most recent six decades. These state estimates are constrained by monthly mean ocean, atmosphere and sea ice observations such that their trajectories track the observed state while enabling estimation of the uncertainties in the approximations to the retrospective mean climate over recent decades. Strongly coupled data assimilation (SCDA) is implemented via an ensemble transform Kalman filter in order to constrain a general circulation climate model to observations. Satellite (altimetry, sea surface temperature, sea ice concentration) and in situ ocean temperature and salinity profiles are directly assimilated each month, whereas atmospheric observations are sub-sampled from the JRA55 atmospheric reanalysis. Strong coupling is implemented via explicit cross domain covariances between ocean, atmosphere, sea ice and ocean biogeochemistry. Atmospheric and surface ocean fields are available at daily resolution and monthly resolution for the land, subsurface ocean and sea ice. The system also produces a complete data archive of initial conditions potentially enabling individual forecasts for all members each month over the 60 year period. The size of the ensemble and application of strongly coupled data assimilation lead to new insights for future reanalyses. CAFE60v1 has been validated in comparison to empirical indices of the major climate teleconnections and blocking from various reanalysis products (ERA5, JRA55, NCEP NR1). Estimates of the large scale ocean structure and transports have been compared to those derived from gridded observational products (WOA18, HadISST, ERSSTv5) and climate model projections (CMIP). Sea ice (extent, concentration and variability) and land surface (precipitation and surface air temperatures) are also compared to a variety of model (ERA5, CMIP) and observational (GPCP, AWAP, HadCRU4, GIOMAS, NSIDC, HadISST) products. This analysis shows that CAFE60v1 is a useful, comprehensive and unique data resource for studying internal climate variability and predictability, including the recent climate response to anthropogenic forcing on multi-year to decadal time scales.

CCAFS-Climate Data

High resolution climate data to help assess the impacts of climate change primarily on agriculture. These open access datasets of climate projections will help researchers make climate change impact assessments.

COCO - Common Objects in Context - fast.ai datasets

COCO is a large-scale object detection, segmentation, and captioning dataset. This is part of the fast.ai datasets collection hosted by AWS for convenience of fast.ai students. If you use this dataset in your research please cite arXiv:1405.0312 [cs.CV].

COVID-19 Molecular Structure and Therapeutics Hub

Aggregating critical information to accelerate drug discovery for the molecular modeling and simulation community. A community-driven data repository and curation service for molecular structures, models, therapeutics, and simulations related to computational research related to therapeutic opportunities for COVID-19 (caused by the SARS-CoV-2 coronavirus).

CRC-SAS/SISSA historical seasonal and subseasonal forecast database

En el marco del Sistema de Información de Sequías del Sur de Sudamérica (SISSA) se ha desarrollado una base de predicciones en escala subestacional y estacional con datos corregidos y sin corregir, con el propósito que permita estudiar predictibilidad en distintas escalas y también que sirva para alimentar modelos de sectores como agricultura e hidrología.

La base contiene datos en escala diaria entre 2000-2019 (sin corregir) y 2010-2019 (corregidos) para diversas variables incluyendo: temperatura media, máxima y mínima, así como también lluvia, viento medio y otras variables pensadas para alimentar modelos hidrológicos y de cultivo.

La base de datos abarca toda el área del Centro Regional del Clima para el sur de sudamérica (CRC-SAS), abarcando desde Bolivia y centro-sur de Brasil hasta la Patagonia incluyendo los países miembros como Chile, Argentina, Brasil, Paraguay, Uruguay y Bolivia.

La base fue generada a partir de datos de GEFSv12 para escala subestacional (GEFS) y CFS2 para escala estacional (CFS2). Para la generación de los datos corregidos se utilizaron los datos del reanálisis de ERA5 (ERA5).

Within the framework of the Southern South American Drought Information System (SISSA), a base of sub-seasonal and seasonal scale predictions has been developed with corrected and uncorrected data, with the purpose of studying predictability at different scales and also to be used to feed models for sectors such as agriculture and hydrology.

The database contains daily scale data between 2000-2019 (uncorrected) and 2010-2019 (corrected) for several variables including: mean, maximum and minimum temperature, as well as rainfall, mean wind and other variables intended to feed hydrological and crop models.

The database covers the entire area of the Regional Climate Center for Southern South America (CRC-SAS), from Bolivia and south-central Brazil to Patagonia, including member countries such as Chile, Argentina, Brazil, Paraguay, Uruguay and Bolivia.

The base was generated from GEFSv12 data for subseasonal scale (GEFS) and CFS2 for seasonal scale (CFS2). Data from the ERA5 reanalysis (ERA5) were used to generate the corrected data.

CarbonPDF

A carbon question-answering (QA) dataset specifically designed to facilitate the extraction and analysis of data from real-world carbon reports of computing products. The dataset features annotated metadata, a variety of numerical reasoning tasks, and structured derivations to ensure accurate processing of fragmented and inconsistent information.

CartoStore

Cross-Platform Repository for High-resolution Spatial Transcriptomics Datasets.

Central Weather Administration OpenData

Various kinds of weather raw data and charts from Central Weather Administration.

Central Weather Bureau OpenData

Various kinds of weather raw data and charts from Central Weather Bureau.

Clinical Ultrasound Image Repository

Generic Clinical Ultrasound Data from Random Subjects acquired for Clinical Reasons, to be used for Developing Artificial Intelligence Applications. This dataset is complete with 2000 studies from 2000 subjects (one third each from abdominal, cardiac, and OB/GYN cases)

Cloud to Street - Microsoft Flood and Clouds Dataset

This dataset consists of chips of Sentinel-1 and Sentinel-2 satellite data. Each Sentinel-1 chip contains a corresponding label for water and each Sentinel-2 chip contains a corresponding label for water and clouds. Data is stored in folders by a unique event identifier as the folder name. Within each event folder there are subfolders for Sentinel-1 (s1) and Sentinel-2 (s2) data. Each chip is contained in its own sub-folder with the folder name being the source image id, followed by a unique chip identifier consisting of a hyphenated set of 5 numbers. All bands of the satellite data, as well as the labels, and overview images are contained within the chip folder.

Community Multiscale Air Quality (CMAQ) 2019 3D Gridded and Column data from the EPA's Air Quality Time Series (EQUATES) Project

The data are part of EPA’s Air Quality Time Series (EQUATES) Project. The data consist of hourly gridded pollutant concentrations estimates by the Community Multiscale Air Quality (CMAQ) model version 5.3.2 (https://doi.org/10.15139/S3/F2KJSK) for January 1 – December 31, 2019. Model data is provided for two spatial domains : the Northern Hemisphere (108 km x 108km horizontal grid spacing) and the Contiguous United States including parts of Canada and Mexico (12km x 12km horizontal grid spacing). Two types of hourly data are provided: three-dimensional air pollutant concentrations and vertical column pollutant totals. Previous studies have used this type of CMAQ 3D and vertical column air quality data to evaluate the modeling system, created model-observed ‘fused’ surfaces, and to analyze spatial and temporal changes in air quality in the upper atmosphere, e.g., https://doi.org/10.1016/j.envint.2019.104909; https://doi.org/10.5194/acp-17-12449-2017; https://doi.org/10.1029/2006JD008085; https://doi.org/10.5194/acp-15-9997-2015.

DARPA Invisible Headlights Dataset

"The DARPA Invisible Headlights Dataset is a large-scale multi-sensor dataset annotated for autonomous, off-road navigation in challenging off-road environments. It features simultaneously collected off-road imagery from multispectral, hyperspectral, polarimetric, and broadband sensors spanning wave-lengths from the visible spectrum to long-wave infrared and provides aligned LIDAR data for ground-truth shape. Camera calibrations, LiDAR registrations, and traversability annotations for a subset of the data are available."

DHARANI Developing Human-Brain Atlas

We introduce DHARANI, the first online platform with three-dimensional (3D) histological reconstructions of the developing human brain from 14 to 24 gestational weeks (GW) across the five fetal brains. DHARANI features 5132 Nissl, hematoxylin and eosin stained, 20 µm coronal and sagittal sections, postmortem MRI, and a neuroanatomical atlas with 466 annotated sections covering ∼500 brain structures. It is accessible online at https://brainportal.humanbrain.in/publicview/index.html. The 3D reconstruction enables a volumetric view of the fetal brain, allowing visualization in all three planes akin to MRI, previously unachievable with histological datasets from the fetal brain. This allowed qualitative assessment of the growth of brain regions and layers throughout the second trimester. “DHARANI” documents the initiation of sulci, with the lateral fissure, calcarine, parieto-occipital, and cingulate sulci, at 14 GW. The central and postcentral sulci appear by 24 GW; however, cytoarchitectonic boundaries become visible before sulcal patterns. Cortical plate (CP) lamination begins at 24 GW in the parietal and occipital cortices. The frontal cortex lacks lamination at 24 GW, although putative Betz cells are already visible and show early patterning in the intermediate zone. The cell-sparse layer between the CP and subplate, containing late migratory neurons, begins in the orbital cortex at 14 GW and reaches the frontal cortex by 17 GW. The appearance of the honeycomb pattern in the occipital and parietal cortex occurs after 14 GW. Additionally, we describe the development of the thalamic pregeniculate with the rotation of the lateral geniculate nucleus. Cerebellar nuclei and an early Purkinje cell layer appear by 21 GW in the already foliated cerebellar cortex. The collection of > 650,000 images have been made available in this Open Data bucket to enable efficient access and analysis of the this dataset.

Department of Energy's Marine Energy Data Lake

Data released from projects funded by the Department of Energy's Water Power Technologies Office (DOE WPTO) that are too large or complex to be conveniently accessed by traditional means. The Marine Energy data lake aims to improve and automate access of high-value MHK data sets, making data actionable and discoverable by researchers and industry to accelerate analysis and advance innovation. This data lake is a sister-data lake to the Department of Energy’s Open Energy Data Initiative (OEDI) data lake.

District of Columbia - Classified Point Cloud LiDAR

LiDAR point cloud data for Washington, DC is available for anyone to use on Amazon S3. This dataset, managed by the Office of the Chief Technology Officer (OCTO), through the direction of the District of Columbia GIS program, contains tiled point cloud data for the entire District along with associated metadata.

ENHANCE.PET 1.6k - Whole-/Total-Body [18F]FDG-PET/CT with CT-Derived Segmentations

Open, multi-center dataset of 1,597 whole-/total-body FDG-PET/CT studies with 130 CT-derived, expert-verified anatomical segmentations per scan (~250 GB). Provided as anonymized NIfTI (PET, CT, labels) with spreadsheet metadata. Designed for segmentation benchmarking, multi-organ analysis, radiomics, and PET/CT AI research.

EPA Dynamically Downscaled Ensemble (EDDE) Version 1

The data are a subset of the EPA Dynamically Downscaled Ensemble (EDDE), Version 1. EDDE is a collection of physics-based modeled data that represent 3D atmospheric conditions for historical and future periods under different scenarios. The EDDE Version 1 datasets cover the contiguous United States at a horizontal grid spacing of 36 kilometers at hourly increments. EDDE Version 1 includes simulations that have been dynamically downscaled from multiple global climate models (GCMs) under both mid- and high-emission scenarios from the Fifth Coupled Model Intercomparison Project (CMIP5) using the Weather Research and Forecasting (WRF) model. Scenarios were downscaled from the Community Earth System Model (CESM) and the Geophysical Fluid Dynamics Laboratory (GFDL) Coupled Model version 3 (CM3). Simulations followed the historical periods 1975-2005 (CESM only) and 1995-2005 (both CESM and CM3), and Representative Concentration Pathways (RCP) 4.5 for 2025-2100 (CESM only), RCP6.0 for 2025-2055 (CESM only), and RCP8.5 (both CESM and CM3). Data are in Network Common Data Form (netCDF) version 4, which is used in atmospheric modeling. The EDDE data in netCDF are further written to adhere to principles of Climate and Forecasting System (CF) Compliance to enhance portability and interoperability with data from other sources. The files are self-describing with metadata included in the netCDF header. Data reference: https://doi.org/10.23719/1530964 Some peer-reviewed publications that describe EDDE (without the name "EDDE"): https://doi.org/10.1080/10962247.2021.1970048 https://doi.org/10.5194/acp-18-15471-2018 https://doi.org/10.1080/10962247.2014.996270 https://doi.org/10.1175/JCLI-D-15-0233.1 https://doi.org/10.1002/2014JD021785

EPA Dynamically Downscaled Ensemble (EDDE) Version 2

The data are a subset of the EPA Dynamically Downscaled Ensemble (EDDE), Version 2. EDDE is a collection of physics-based modeled data that represent 3D atmospheric conditions for historical and future periods under different scenarios. The EDDE Version 2 datasets cover the contiguous United States at a horizontal grid spacing of 12 kilometers at hourly increments. EDDE Version 2 will include simulations that have been dynamically downscaled from multiple global climate models (GCMs) under multiple emission scenarios from the Sixth Coupled Model Intercomparison Project (CMIP6) using the Weather Research and Forecasting (WRF) model. Scenarios were downscaled from the Max Planck Institute (MPI) Earth System Model (ESM) version 1.2 High Resolution (MPI-ESM1-2-HR), with plans to expand into additional GCMs in the future. Simulations cover the historical period 1985-2014 and a future period of 2025-2100 under Shared Socioeconomic Pathway (SSP) 3-7.0. Data are in Network Common Data Form (netCDF) version 4, which is used in atmospheric modeling. The EDDE data in netCDF are further written to adhere to principles of Climate and Forecasting System (CF) Compliance to enhance portability and interoperability with data from other sources. The files are self-describing with metadata included in the netCDF header. Data reference: https://doi.org/10.23719/1531941 Some peer-reviewed publications that describe EDDE (without the name "EDDE"): https://doi.org/10.1080/10962247.2021.1970048 https://doi.org/10.5194/acp-18-15471-2018 https://doi.org/10.1080/10962247.2014.996270 https://doi.org/10.1175/JCLI-D-15-0233.1 https://doi.org/10.1002/2014JD021785

EPA Hourly Prognostic Meteorological Data

The data are hourly outputs from the Weather Research and Forecasting (WRF) model generated by the EPA's Office of State Air Partnerships (OSAP), Air Quality Assessment Division, Air Quality Modeling Branch. These data were generated at a 12-km resolution over the Continental United States (12US), beginning for the year 2021 and continuing annually through 2023. These files are intended for use in a broad range of air quality applications, but specifically may be used in dispersion modeling applications that would benefit from the use of the Mesoscale Model Interface (MMIF) tool (https://www.epa.gov/scram/air-quality-dispersion-modeling-related-model-support-programs#mmif) which translates prognostic meteorological data into formats suitable for use with AERMOD, CALPUFF, or SCICHEM. The individual files are less than 1GB in size, which allows for the use of the MMIF tool in a Windows environment. These data are anticipated to be updated annually so the 3 most-recent years are available for use. Additionally, model-observation paired files are included to aid in the performance evaluation that is necessary for use of these data in regulatory applications per Appendix W to 40 CFR Part 51.

EPA Risk-Screening Environmental Indicators

Detailed air model results from EPA’s Risk-Screening Environmental Indicators (RSEI) model.

Epoch of Reionization Dataset

The data are from observations with the Murchison Widefield Array (MWA) which is a Square Kilometer Array (SKA) precursor in Western Australia. This particular dataset is from the Epoch of Reionization project which is a key science driver of the SKA. Nearly 2PB of such observations have been recorded to date, this is a small subset of that which has been exported from the MWA data archive in Perth and made available to the public on AWS. The data were taken to detect signatures of the first stars and galaxies forming and the effect of these early stars and galaxies on the evolution of the universe.

GATK Test Data

The GATK test data resource bundle is a collection of files for resequencing human genomic data with the Broad Institute's Genome Analysis Toolkit (GATK).

GLAD Landsat ARD

The Landsat Analysis Ready Data (ARD) created by the Global Land Analysis and Discovery Lab (GLAD) at the University of Maryland serves as a spatially and temporally consistent input for land cover mapping and change detection at global to local scales. The GLAD ARD represents a 16-day time series of globally consistent, tiled Landsat normalized surface reflectance from 1997 to the present operationally updated every 16 days. Only data from 2020 to present available on the AWS, older data is available through the UMD API.

GX database for NCBI Foreign Contamination Screen (FCS) Tool Suite

Sequence database used by FCS-GX (Foreign Contamination Screen - Genome Cross-species aligner) to detect contamination from foreign organisms in genome sequences.

Galaxy Evolution Explorer Satellite (GALEX)

The Galaxy Evolution Explorer Satellite (GALEX) was a NASA mission led by the California Institute of Technology, whose primary goal was to investigate how star formation in galaxies evolved from the early universe up to the present. GALEX used microchannel plate detectors to obtain direct images in the near-UV (NUV) and far-UV (FUV), and a grism to disperse light for low resolution spectroscopy.

Genome Ark

The Genome Ark hosts genomic information for the Vertebrate Genomes Project (VGP) and other related projects. The VGP is an international collaboration that aims to generate complete and near error-free reference genomes for all extant vertebrate species. These genomes will be used to address fundamental questions in biology and disease, to identify species most genetically at risk for extinction, and to preserve genetic information of life.

Google Satellite Embedding V1

COG (Cloud-Optimized GeoTIFF) files that together contain the AlphaEarth Foundations annual Satellite Embedding dataset. It contains the annual embeddings for the years from 2018 to 2024, inclusive.

Gretel Synthetic Safety Alignment Dataset

A comprehensive dataset designed for aligning language models with safety and ethical guidelines. Contains 8,361 curated triplets of prompts, responses, and safe responses across various risk categories. Each entry includes safety scores, judge reasoning, and harm probability assessments, making it valuable for model alignment, testing, and benchmarking.

Grid Algorithms and Data Analytics Library (GADAL)

The aim of this project is to create an easy-to-use platform where various types of analytics can be performed on a wide range of electrical grid datasets. The aim is to establish an open-source library of algorithms that universities, national labs and other developers can contribute to which can be used on both open-source and proprietary grid data to improve the analysis of electrical distribution systems for the grid modeling community. OEDI Systems Integration (SI) is a grid algorithms and data analytics API created to standardize how data is sent between different modules that are run as part of a co-simulation.

Gulfwide Avian Colony Monitoring Survey Photos

For this project, The Water Institute (the Institute) and subcontractor Colibri Ecological Consulting, LLC (Colibri) utilized established methods and protocols capable of assessing changes of colonial waterbird populations and their important habitats within individual states and the broader northern Gulf of Mexico region. Data collection activities included: Aerial Photographic Nest Surveys: Implementation of fixed-wing aircraft surveys intended to assess waterbird colonies and document associated nesting within select portions of the northern Gulf of Mexico. Additional detail is provided on the Survey Protocols page of this portal. Nest Dotting Analyses: Review and analysis of aerial photographic nest surveys (2010-2013, 2015, 2018, and 2021) with the intention of documenting the breeding population size and associated nesting for each species at each colony. Additional detail is provided on the Dotting Protocols page of this portal.

Guy's Breast Cancer Lymph Nodes (GRAPE)

This is a retrospective dataset of 1523 H&E-stained whole slide images (WSI) of lymph nodes from breast cancer patients. The cohort consisted of 177 patients (122 LN-positive - metastasis was reported in at least 1 LN - and 55 LN-negative patients) with invasive breast carcinoma treated between 1984 and 2002 at Guy’s Hospital London, UK. Slides were scanned and digitised at 40x magnification (0.23 µm/pixel), NanoZoomer H.T2.0 2.0-HT (Hamamatsu Photonics UK, Ltd, Welwyn Garden City, UK). WSIs are in .ndpi format.

HIRLAM Weather Model

HIRLAM (High Resolution Limited Area Model) is an operational synoptic and mesoscale weather prediction model managed by the Finnish Meteorological Institute.

High Resolution Downscaled Climate Data for Southeast Alaska

This dataset contains historical and projected dynamically downscaled climate data for the Southeast region of the State of Alaska at 1 and 4km spatial resolution and hourly temporal resolution. Select variables are also summarized into daily resolutions. This data was produced using the Weather Research and Forecasting (WRF) model (Version 4.0). We downscaled both Climate Forecast System Reanalysis (CFSR) historical reanalysis data (1980-2019) and both historical and projected runs from two GCM’s from the Coupled Model Inter-comparison Project 5 (CMIP5): GFDL-CM3 and NCAR-CCSM4 (historical run: 1980-2010 and RCP 8.5: 2030-2060).

Homeland Security and Infrastructure US Cities

The U.S. Cities elevation data collection program supported the US Department of Homeland Security Homeland Security and Infrastructure Program (HSIP). As part of the HSIP Program, there were 133+ U.S. cities that had imagery and LiDAR collected to provide the Homeland Security, Homeland Defense, and Emergency Preparedness, Response and Recovery (EPR&R) community with common operational, geospatially enabled baseline data needed to analyze threat, support critical infrastructure protection and expedite readiness, response and recovery in the event of a man-made or natural disaster. As a part of that, for some time, recurring LiDAR data was also being collected by a joint agreement of NGA and other federal agencies and the HIFLDS Working Group. The publicly released data excluded US Military Installation coverage, but it is provided in as is. These collects were acquired by contract using commercial collection companies. Some metadata information about the collection can be found at USGS at https://rockyweb.usgs.gov/vdelivery/Datasets/Staged/Elevation/Non_Standard_Contributed/NGA_US_Cities/Topeka_KS/NGA%20133%20US%20Cities%20Data%20Disclaimer%20and%20Explanation%20Readme.pdf

Hubble Space Telescope

The Hubble Space Telescope (HST) is one of the most productive scientific instruments ever created. This dataset contains calibrated and raw data for all currently active instruments on HST: ACS, COS, STIS, WFC3, and FGS.

Hybrid statistical-dynamic downscaling based on multi-model ensembles in Southeast Asia

GCMs under CMIP6 have been widely used to investigate climate change impacts and put forward associated adaptation and mitigation strategies. However, the relatively coarse spatial resolutions (usually 100~300km) preclude their direct applications at regional scales, which are exactly where the analysis (e.g., hydrological model simulation) is performed. To bridge this gap, a typical approach is to ‘refine’ the information from GCMs through regional climate downscaling experiments, which can be conducted statistically, dynamically, or a combination thereof. Statistical downscaling establishes relationships between large-scale climate indicators and small-scale climate variables in the reference (historical) period. Subsequently, these relationships are kept unchanged in the future and used to predict the future variables. On the other hand, dynamical downscaling operates based on the physical processes and the associated interactions in the climate systems and thus can produce a full set of regional climate simulations (e.g., temperature and precipitation fields) that are dynamically consistent. However, traditional dynamical downscaling contains significant biases that are transferred from GCMs and may be enhanced during the process of downscaling, thus degrading the downscaled results. One promising approach to remove these biases is the hybrid statistical-dynamical downscaling method, where GCMs are firstly bias-corrected, and subsequently used as lower and lateral boundary conditions to drive the regional climate models (RCMs).In this work, we apply a hybrid statistical-dynamical downscaling method, following the approach of Xu et al. 2021. The bias-corrected dataset is adjusted to resemble ERA5-based mean climate and interannual variance, and with a non-linear trend from the ensemble mean of the 14 CMIP6 models. The dataset spans a historical period of 1979–2014 and future scenarios (SSP585) of 2015–2100, with a temporal scale of six-hour.The main contributions of this dataset are twofold. First, we provide the open-source and high-resolution (12.5km: Southeast Asia; 2.5km:Southern Malay Peninsula; 500m: Singapore, as shown in the following Figures) datasets, including precipitation, wind, temperature, radiation, etc. Second, through our experiment, this bias-corrected and downscaled dataset is of exceptional quality compared to that of the existing dynamical scaling work (e.g., CORDEX) in southeast Asia in terms of its ability to reproduce regional climate extremes, spatial patterns, etc. This dataset will be useful for policy-makers and researchers to establish the necessary pathways for resilient planning in order to mitigate the dire impacts of climate change.

ISERV

ISS SERVIR Environmental Research and Visualization System (ISERV) was a fully-automated prototype camera aboard the International Space Station that was tasked to capture high-resolution Earth imagery of specific locations at 3-7 frames per second. In the course of its regular operations during 2013 and 2014, ISERV's camera acquired images that can be used primaliry in use is environmental and disaster management.

Image localization - fast.ai datasets

Some of the most important datasets for image localization research, including Camvid and PASCAL VOC (2007 and 2012). This is part of the fast.ai datasets collection hosted by AWS for convenience of fast.ai students. See documentation link for citation and license details for each dataset.

Imaging BSD licensed data and models

InRad COVID-19 X-Ray and CT Scans

This dataset is a collection of anonymized thoracic radiographs (X-Rays) and computed tomography (CT) scans of patients with suspected COVID-19. Images are acommpanied by a positive or negative diagnosis for SARS-CoV2 infection via RT-PCR. These images were provided by Hospital das Clínicas da Universidade de São Paulo, Hospital Sirio-Libanes, and by Laboratory Fleury.

K2 Mission Data

The K2 mission observed 100 square degrees for 80 days each across 20 different pointings along the ecliptic, collecting high-precision photometry for a selection of targets within each field. The mission began when the original Kepler mission ended due to loss of the second reaction wheel in 2013.

KITTI Vision Benchmark Suite

Dataset and benchmarks for computer vision research in the context of autonomous driving. The dataset has been recorded in and around the city of Karlsruhe, Germany using the mobile platform AnnieWay (VW station wagon) which has been equipped with several RGB and monochrome cameras, a Velodyne HDL 64 laser scanner as well as an accurate RTK corrected GPS/IMU localization unit. The dataset has been created for computer vision and machine learning research on stereo, optical flow, visual odometry, semantic segmentation, semantic instance segmentation, road segmentation, single image depth prediction, depth map completion, 2D and 3D object detection and object tracking. In addition, several raw data recordings are provided. The datasets are captured by driving around the mid-size city of Karlsruhe, in rural areas and on highways. Up to 15 cars and 30 pedestrians are visible per image.

Kepler Mission Data

The Kepler mission observed the brightness of more than 180,000 stars near the Cygnus constellation at a 30 minute cadence for 4 years in order to find transiting exoplanets, study variable stars, and find eclipsing binaries.

MIMIC-IV Clinical Database Demo

The Medical Information Mart for Intensive Care (MIMIC)-IV database is comprised of deidentified electronic health records for patients admitted to the Beth Israel Deaconess Medical Center. Access to MIMIC-IV is limited to credentialed users. Here, we have provided an openly-available demo of MIMIC-IV containing a subset of 100 patients. The dataset includes similar content to MIMIC-IV, but excludes free-text clinical notes. The demo may be useful for running workshops and for assessing whether the MIMIC-IV is appropriate for a study before making an access request.

MIMIC-IV-ECG: Diagnostic Electrocardiogram Matched Subset

The MIMIC-IV-ECG module contains approximately 800,000 diagnostic electrocardiograms across nearly 160,000 unique patients. These diagnostic ECGs use 12 leads and are 10 seconds in length. They are sampled at 500 Hz. This subset contains all of the ECGs for patients who appear in the MIMIC-IV Clinical Database. When a cardiologist report is available for a given ECG, we provide the needed information to link the waveform to the report. The patients in MIMIC-IV-ECG have been matched against the MIMIC-IV Clinical Database, making it possible to link to information across the MIMIC-IV modules.

MISR Level 1B2 Ellipsoid Data V004

MI1B2E_004 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B2 Ellipsoid Data Version 4 product. It contains Ellipsoid-projected Top-of-Atmosphere (TOA) Radiance, resampled at the surface and topographically corrected, as well as geometrically corrected by PGE22. Data collection for this product is ongoing.MISR itself is an instrument designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all 9 cameras, in each of 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the affects of sunlight on Earth, as well as distinguish different types of clouds, particles and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure. Read our doc on how to get AWS Credentials to retrieve this data: https://data.asdc.earthdata.nasa.gov/s3credentialsREADME

MegaScenes

The MegaScenes Dataset is an extensive collection of around 430k scenes, featuring over 100k structure-from-motion reconstructions and over 2 million registered images. MegaScenes includes a diverse array of scenes, such as minarets, building interiors, statues, bridges, towers, religious buildings, and natural landscapes. The images of these scenes are captured under varying conditions, including different times of day, various weather and illumination, and from different devices with distinct camera intrinsics.

MetaGraph Sequence Indexes

The MetaGraph Sequence Indexes dataset comprises full-text searchable index files for raw sequencing data hosted in major public repositories. These include the European Nucleotide Archive (ENA) managed by the European Bioinformatics Institute (EMBL-EBI), the Sequence Read Archive (SRA) maintained by the National Center for Biotechnology Information (NCBI), and the DNA Data Bank of Japan (DDBJ) Sequence Read Archive (DRA).All index files can be used with the MetaGraph framework for sequence search. Indexes can be jointly used for aggregated search in the cloud or can be individually downloaded for search using local hardware.

Metagenomic reference libraries for Slacken

Metagenomic indexes for use with the Slacken taxonomic classification tool

Model Benchmarking

This dataset includes data and models relevant to benchmarking multimodal biological models. The data has been sourced and curated by a team of experts at Biohub and is provided as part of these datasets only when it is not publicly available or requires transformation to support effective model benchmarking.

Multimedia Commons

The Multimedia Commons is a collection of audio and visual features computed for the nearly 100 million Creative Commons-licensed Flickr images and videos in the YFCC100M dataset from Yahoo! Labs, along with ground-truth annotations for selected subsets. The International Computer Science Institute (ICSI) and Lawrence Livermore National Laboratory are producing and distributing a core set of derived feature sets and annotations as part of an effort to enable large-scale video search capabilities. They have released this feature corpus into the public domain, under Creative Commons License 0, so it is free for anyone to use for any purpose.

NASA 1993_AN_NASA Project

This data set contains spot elevation measurements of Arctic, Greenland, Antarctic, and Patagonia sea ice and ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation.

BLATM2

This data set contains resampled and smoothed elevation measurements of Arctic and Antarctic sea ice, as well as Greenland, Arctic, Patagonia, and Antarctic region land ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 1993_GR_NASA Project

This data set contains depth sounder measurements of ice elevation, ice surface, ice bottom, and ice thickness over Greenland and Antarctica, acquired by the Multichannel Coherent Radar Depth Sounder (MCoRDS).

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2007_GR_NASA Project

This data set contains surface elevation data over Greenland measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter.

Data Discovery

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Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2008_AN_UTIG Project

This data set contains vertical acceleration values for Antarctica using the BGM-3 Gravimeter. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.

IGBGM2

This data set contains free air anomaly measurements taken over Antarctica using the BGM-3 Gravimeter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.

IR1HI1B

This data set contains Antarctica radar sounder echo strength profiles from the Hi-Capability Radar Sounder (HiCARS) Version 1 instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which was funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.

IR1HI2

This data set contains ice thickness, surface and bed elevation, and echo strength measurements taken over Antarctica using the Hi-Capability Airborne Radar Sounder (HiCARS) instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.

IR2HI1B

This data set contains Antarctica radar sounder echo strength profiles from the Hi-Capability Radar Sounder (HiCARS) Version 2 instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which was funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.

IR2HI2

This data set contains ice thickness, surface and bed elevation, and echo strength measurements taken over Antarctica using the Hi-Capability Airborne Radar Sounder (HiCARS) instrument. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which was funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.

IAPRS1B

This data set contains static pressure values for Antarctica using the Paroscientific Digiquartz Transmitter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.

ILUTP1B

This data set contains laser ranges, returned pulses, and deviation for returned pulses in Antarctica and Greenland using the Riegl Laser Altimeter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA's Operation IceBridge.

ILUTP2

This data set contains surface range values for Antarctica and Greenland derived from measurements captured by the Riegl Laser Altimeter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.

IMFGM1B

This data set contains time-registered Level-1B field readings taken over Antarctica using the Watson-Gyro Fluxgate Magnetometer instrument. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.

IGZLS1B

This data set contains vertical, cross body, and along body acceleration values for geophysical survey flights in Antarctica using the ZLS Dynamic Gravity Meter. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2009_AK_NASA Project

This data set represents a collection of orthorectified images that were created using the NASA Ames Stereo Pipeline. The final images were obtained by processing stereo images from the IceBridge DMS L0 Raw Imagery data set, along with NASA's Land, Vegetation, and Ice Sensor (LVIS) and Airborne Topographic Mapper (ATM) lidar data from the IceBridge LVIS L2 Geolocated Surface Elevation Product and IceBridge ATM L1B Elevation and Return Strength data sets, respectively. The closely related data set IceBridge DMS L3 Ames Stereo Pipeline Photogrammetric DEM provides the corresponding digital elevation models (DEMs) in GeoTIFF format.

IODEM3

This data set represents a collection of digital elevation models (DEMs) that were created using the NASA Ames Stereo Pipeline. The final DEMs were obtained by processing stereo images from the IceBridge DMS L0 Raw Imagery data set, along with NASA's Land, Vegetation, and Ice Sensor (LVIS) and Airborne Topographic Mapper (ATM) lidar data from the IceBridge LVIS L2 Geolocated Surface Elevation Product and IceBridge ATM L1B Elevation and Return Strength data sets, respectively. The closely related data set IceBridge DMS L3 Ames Stereo Pipeline Orthorectified Images provides the corresponding orthorectified images in GeoTIFF format.

IRWIS2

This data set contains depth sounder measurements of elevation, surface, bottom, and thickness for Alaska taken from the Warm Ice Sounding Explorer (WISE). The data were collected as part of Operation IceBridge funded aircraft survey campaigns.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2009_AK_UAF Project

This data set contains flight reports from NASA Operation IceBridge Greenland, Arctic, Antarctic, and Alaska missions. Flight reports contain information on region, mission, aircraft model, flight data, purpose of flight, and on-board sensors. The flight reports were collected as part of Operation IceBridge funded aircraft survey campaigns. The corresponding flight lines can be found in the IceBridge L1B Thinned Flight Lines (IPFLT1B) data set.

ILAKP1B

This data set contains surface profiles of Alaska Glaciers acquired using the airborne University of Alaska Fairbanks (UAF) Glacier Lidar system. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.

ILAKS1B

This data set contains scanning laser altimetry data points of Alaskan glaciers and parts of East and West Antarctica acquired by the airborne University of Alaska Fairbanks (UAF) Glacier Lidar system. The data were collected as part of NASA Operation IceBridge funded campaigns.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2009_AN_CRESIS Project

This data set contains depth sounder measurements of ice elevation, ice surface, ice bottom, and ice thickness for Greenland and Antarctica taken from the Multichannel Coherent Radar Depth Sounder (MCoRDS). The data were collected as part of Operation IceBridge funded aircraft survey campaigns.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2009_AN_NASA Project

This data set contains radar echograms taken over Greenland and Antarctica using the Center for Remote Sensing of Ice Sheets (CReSIS) Accumulation Radar instrument. The data were collected as part of Operation IceBridge funded campaigns.

ILATM1B

This data set contains spot elevation measurements of Arctic and Antarctic sea ice, and Greenland, Antarctic Peninsula, and West Antarctic region ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.

ILATM2

This data set contains resampled and smoothed elevation measurements of Arctic and Antarctic sea ice, and Greenland, Antarctic Peninsula, and West Antarctic region land ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.

IDHDT4

This data set contains surface elevation rate of change measurements derived from IceBridge and Pre-IceBridge Airborne Topographic Mapper (ATM) widescan elevation measurements data for Arctic and Antarctic missions flown under NASA's Operation IceBridge (OIB) and Arctic Ice Mapping (AIM) projects.

IOCAM1B

This data set contains images taken with the Continuous Airborne Mapping By Optical Translator (CAMBOT) over Antarctica and Greenland.

IODCC0

This data set contains camera calibration reports for IceBridge Digital Mapping System (DMS) missions flown over Antarctica and Greenland.

IODMS1B

This data set contains Level-1B imagery taken from the Digital Mapping System (DMS) over Greenland and Antarctica. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.

IODMS3

The IceBridge DMS L3 Photogrammetric DEM (IODMS3) data set contains gridded digital elevation models and orthorectified images of Greenland and Antarctica derived from the Digital Mapping System (DMS). The data were collected as part of Operation IceBridge funded campaigns.

IAKST1B

This data set contains surface temperature measurements of Arctic and Antarctic sea ice and land ice acquired by the Heitronics KT19.85 Series II Infrared Radiation Pyrometer. For flights with the NASA DC-8 aircraft, the National Suborbital Research Center (NSRC) operated the instrument and created the data product. For flights with the NASA P-3 and other aircraft, the instrument was operated by the Wallops Flight Facility (WFF) as part of the ATM instrument suite. The data were collected as part of the Operation IceBridge funded survey campaigns.

IRKUB1B

This data set contains elevation and surface measurements over Greenland, the Arctic, and Antarctica, as well as flight path charts and echogram images acquired using the Center for Remote Sensing of Ice Sheets (CReSIS) Ku-Band Radar Altimeter.

IDCSI4

This data set contains derived geophysical data products including sea ice freeboard, snow depth, and sea ice thickness measurements in Greenland and Antarctica retrieved from IceBridge Snow Radar, Digital Mapping System (DMS), Continuous Airborne Mapping By Optical Translator (CAMBOT), and Airborne Topographic Mapper (ATM) data sets. The data were collected as part of Operation IceBridge funded campaigns.

ILVIS0

This data set contains raw Inertial Measurement Unit (IMU), Global Positioning System (GPS), and camera data over Greenland, Antarctica, and Alaska measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of Operation IceBridge funded campaigns, including the Arctic Radiation - IceBridge Sea and Ice Experiment (ARISE).

IOLVIS1A

This data set contains geotagged images taken over Greenland and Antarctica by the NASA Digital Mapping Camera paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of Operation IceBridge funded campaigns.

ILVIS1B

This data set contains return energy waveform data measured over Greenland, Alaska, and Antarctica by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of Operation IceBridge funded campaigns.

ILVIS2

This data set contains surface elevation data over Greenland, Alaska, and Antarctica, measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA Operation IceBridge funded campaigns.

IGGRV1B

This data set contains gravity measurements, including acceleration data in three orthogonal directions, from the Sander Geophysics AIRGrav airborne gravity system. Gravity data include latitude and Eotvos-corrected values, as well as free air correction at various along-flight-line time filtering scales. The data were collected as part of Operation IceBridge funded campaigns.

IRSNO1B

This data set contains radar echograms taken from the Center for Remote Sensing of Ice Sheets (CReSIS) ultra wide-band snow radar over land and sea ice in the Arctic and Antarctic. In addition, airborne snow measurements were taken during 10 flights over Alaska mountains, ice fields, and glaciers at the end of May 2018 by a compact CReSIS FMCW radar system installed on a Single Otter aircraft. The data were collected as part of Operation IceBridge funded campaigns.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2009_GR_NASA Project

This data set contains contains Greenland ice thickness measurements acquired using the Pathfinder Advanced Radar Ice Sounder (PARIS). The data were collected as part of Operation IceBridge funded campaigns.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2010_AN_NASA Project

This data set contains Level-3 tomographic ice thickness measurements and ice thickness errors over areas of Greenland and Antarctica. Two of the data files additionally provide bed elevation measurements. The data were derived from measurements taken by the Center for Remote Sensing of Ice Sheets (CReSIS) Multichannel Coherent Radar Depth Sounder (MCoRDS) instrument and were collected as part of NASA Operation IceBridge funded campaigns.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2010_AN_UTIG Project

This data set contains geolocated surface elevation measurements captured over Antarctica using the Sigma Space Mapping Photon Counting Lidar and Riegl Laser Altimeter. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.

ILSNP1B

This data set contains nadir photon counting data captured over Antarctica using the Sigma Space photon counting lidar. Position and orientation data are included. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2010_GR_NASA Project

This data set contains reprocessed images depicting labels that indicate the sea ice surface category, created by processing IceBridge DMS L0 Raw Imagery with the Open Source Sea-ice Processing Algorithm. The images are provided as TIFF files (.tif). Additional metadata are provided as CSV text files (.csv), which are available as a single zip file named RDSISC4_metadata.zip. An orthorectified version of this data set is available as IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Orthorectified Images.

RDSISCO4

This data set contains reprocessed, orthorectified images depicting labels that indicate the sea ice surface category, created by processing IceBridge DMS L0 Raw Imagery (IODMS0) with the Open Source Sea-ice Processing Algorithm. Orthorectification was done using digital elevation models from the IceBridge DMS L3 Ames Stereo Pipeline Photogrammetric DEM (IODEM3) collection. The standard (non-orthorectified) images are available as IceBridge-Related DMS-Derived L4 Sea Ice Surface Cover Classification Images (RDSISC4).

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2011_AN_NASA Project

This data set contains surface temperature measurements of Arctic and Antarctic sea ice and land ice acquired by the Heitronics KT19.85 Series II Infrared Radiation Pyrometer. The instrument is operated by the Wallops Flight Facility (WFF) as part of the ATM instrument suite. The data were collected as part of the Operation IceBridge funded survey campaigns.

ILNSA1B

This data set contains spot elevation measurements of Greenland, Arctic, and Antarctic sea ice acquired using the NASA Airborne Topographic Mapper (ATM) 4CT3 narrow scan instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2011_AN_UTIG Project

This data set contains geolocated photon elevations captured over Antarctica using the Sigma Space photon counting lidar. The data were collected by scientists working on the International Collaborative Exploration of the Cryosphere through Airborne Profiling (ICECAP) project, which was funded by the National Science Foundation (NSF), the Antarctic Climate and Ecosystems Collaborative Research Center, and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2012_AN_NASA Project

This data set contains radar echograms taken from the Center for Remote Sensing of Ice Sheets (CReSIS) ultra Multichannel Coherent Radar Depth Sounder (MCoRDS) over land and sea ice in the Arctic and Antarctic.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2012_AN_UTIG Project

This data set contains vertical acceleration values for Antarctica using the CMG 1A dynamic gravity meter. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.

IGCMG2

This data set contains geolocated free air gravity disturbances derived from measurements taken over Antarctica using the GT-1A gravity meter S-019. The data were collected by scientists working on the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project, which is funded by the National Science Foundation (NSF) and the Natural Environment Research Council (NERC) with additional support from NASA Operation IceBridge.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2012_GR_GBMF Project

This data set contains Greenland and Antarctica gravity measurements taken from the Sander Geophysics AIRGrav airborne gravity system.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2013_AK_UAF Project

This data set contains radar echograms acquired by the University of Alaska Fairbanks High-Frequency Radar Sounder over select glaciers in Alaska.

IRUAFHF2

This data set contains measurements of glacier surface elevation, bed elevation, and ice thickness for Alaska and Northwestern Canada. Glacier surface elevation is derived from IceBridge UAF Lidar Scanner L1B Geolocated Surface Elevation Triplets (ILAKS1B). Radar bed returns are sourced from IceBridge UAF L1B HF Geolocated Radar Echo Strength Profiles (IRUAFHF1B) to provide glacier bed elevation and ice thickness. Radar-derived ice thickness and glacier bed elevation are provided as per-trace measurements from the corresponding L1B radar echo strength profile (referencing the WGS 84 ellipsoid).

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

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NASA 2013_AN_NASA Project

ILVGH1B

This data set contains energy waveform data measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter, aboard the Global Hawk Unmanned Aerial Vehicle. The data were collected as part of NASA Operation IceBridge funded campaigns.

ILVGH2

This data set contains surface elevation data measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter, aboard the Global Hawk Unmanned Aerial Vehicle. The data were collected as part of NASA Operation IceBridge funded campaigns.

ILNSA1B

This data set contains spot elevation measurements of Greenland, Arctic, and Antarctic sea ice acquired using the NASA Airborne Topographic Mapper (ATM) narrow-swath instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.

IMCS31B

This data set contains magnetic field readings taken over Antarctica using the Scintrex CS-3 Cesium Magnetometer instrument. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2015_AK_UAF Project

This data set contains radar echograms acquired by the Arizona Radio-Echo Sounder (ARES) over select glaciers in Alaska.

IRARES2

This data set contains glacier surface elevation, bed elevation, and ice thickness measurements for Alaska and Northwestern Canada. Glacier surface elevation is derived from IceBridge UAF Lidar Scanner L1B Geolocated Surface Elevation Triplets (ILAKS1B). Radar bed returns are taken from IceBridge ARES L1B Geolocated Radar Echo Strength Profiles (IRARES1B).

Data Discovery

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Data Access

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NASA 2015_AN_NASA Project

This data set contains geotagged images captured by NASA Digital Mapping Cameras, which were mounted alongside the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

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NASA 2017_AN_NASA Project

This data set contains spot elevation measurements with corresponding waveforms of Arctic and Antarctic sea ice, and Greenland, Antarctic Peninsula, and West Antarctic region ice surface acquired using the NASA Airborne Topographic Mapper (ATM) instrumentation. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.

ILATMGR

This data set reports surface grain size estimates of snow and ice using waveform measurements from NASA's Airborne Topographic Mapper (ATM) narrow-swath and wide-swath instrumentation over the Greenland ice sheet and surrounding sea ice.

ILNSAW1B

This data set contains spot elevation measurements with corresponding waveforms of Greenland, Arctic, and Antarctic sea ice. The data complement the IceBridge ATM L1B Near-Infrared Waveforms data, which are measured at near-infrared wavelength. The data were acquired as part of aircraft survey campaigns funded by Operation IceBridge.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2017_GR_NASA Project

This data set contains gravity measurements taken over Greenland and Antarctica by the Lamont-Doherty Earth Observatory (LDEO) Gravimeter Suite. The data were collected as part of Operation IceBridge funded campaigns.

ILVIS2

This data set contains surface elevation data over parts of Greenland, measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA Operation IceBridge funded campaigns.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA 2018_AN_NASA Project

This data set contains geolocated waveforms of Greenland, Arctic, and Antarctic sea ice measured by the Airborne Topographic Mapper (ATM) near-infrared (NIR) lidar. The data complement, and are intended to be used with, the IceBridge Narrow Swath ATM L1B Elevation and Return Strength with Waveforms data, which are measured at green wavelength. The data were acquired as part of aircraft survey campaigns funded by Operation IceBridge.

IOCAM0

This data set contains raw images and associated aircraft position and attitude data, taken over Antarctica and Greenland by the Continuous Airborne Mapping By Optical Translator (CAMBOT), part of the Airborne Topographic Mapper (ATM) instrument suite. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.

IOCAM1B

This data set contains high-resolution imagery taken with the Continuous Airborne Mapping By Optical Translator (CAMBOT) system over Antarctica and Greenland. The data were collected as part of Operation IceBridge funded aircraft survey campaigns.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

Access requires an Earthdata Login account. Read our guide on obtaining AWS credentials to retrieve this data from AWS.

NASA ABLE-2 Project

ABLE-2A_Aerosol_AircraftInSitu_Electra_Data is the in-situ aerosol data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.

ABLE-2A_TraceGas_AircraftInSitu_Electra_Data

ABLE-2A_TraceGas_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data using chemiluminescence, gas traps, cryogenic air samples, and IR lasers are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.

ABLE-2A_MetNav_AircraftInSitu_Electra_Data

ABLE-2A_MetNav_AircraftInSitu_Electra_Data is the in-situ meteorology and navigational data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.

ABLE-2A_Ground_Data

ABLE-2A_Ground_Data is the ground site data collected during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.

ABLE-2A_Merge_Data

ABLE-2A_Merge_Data is the merge data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.

ABLE-2A_Sondes_Data

ABLE-2A_Sondes_Data is the radiosonde and rawinsonde data collected during the Amazon Boundary Layer Experiment - 2A (ABLE-2A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.

ABLE-2B_Aerosol_AircraftInSitu_Electra_Data

ABLE-2B_Aerosol_AircraftInSitu_Electra_Data is the in-situ aerosol data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.

ABLE-2B_TraceGas_AircraftInSitu_Electra_Data

ABLE-2B_TraceGas_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data using chemiluminescence, gas traps, and grab samples are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.

ABLE-2B_MetNav_AircraftInSitu_Electra_Data

ABLE-2B_MetNav_AircraftInSitu_Electra_Data is the in-situ meteorology and navigational data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.

ABLE-2B_Ground_Data

ABLE-2B_Ground_Data is the ground data collected during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data using grab samples are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.

ABLE-2B_Merge_Data

ABLE-2B_Merge_Data is the merge data collected onboard the NASA Electra aircraft during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data using chemiluminescence, gas traps, and grab samples are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.

ABLE-2B_Sondes_Data

ABLE-2B_Sondes_Data is the rawinsonde and tethered balloon data collected during the Amazon Boundary Layer Experiment - 2B (ABLE-2B) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Amazon Boundary Layer Experiment (ABLE 2) campaigns. ABLE 2 was divided into two sub-campaigns, ABLE 2A (dry season) and ABLE 2B (wet season). ABLE 2A took place from July-August 1985, while ABLE 2B took place from April-May 1987. The goal of ABLE 2 was to better understand the role of tropics in global atmospheric chemistry and investigate processes which might lead to the enhanced concentrations of carbon monoxide (CO) in the tropical upper troposphere. ABLE 2 was a partnership with NASA and the Brazilian agency, Instituto Nacional de Pesquisas Espaciais (INPE), along with the Instituto Nacional de Pesquisas da Amazonia (INPA) providing facilities and logistical information. To accomplish its objectives, the ABLE 2 science team deployed the NASA Lockheed Electra aircraft, balloons, and free-flying sondes. Flights took place over the Amazon region in Brazil for both sub-campaigns. ABLE 2A consisted of 15 flights while ABLE 2B consisted of 21 flights with the fully equipped Electra. For most ABLE 2A flights, the data collected included in-situ measurements of CO2, CO, MHC, NO (nitric oxide), N2O, O3, DMS (dimethyl sulfide), aerosol composition, and meteorological parameters. ABLE 2B had the Electra instrumented with remote and in-situ techniques for measuring the atmospheric distribution of a variety of carbon, nitrogen, and sulfur gases; aerosol size and composition; and to measure ozone. The typical approach to flights involved high-altitude passes over research areas using the downward facing UV Differential Absorption Lidar (DIAL). The ABLE 2 campaign represents a modest advance in understanding the influence of the tropical rain forest ecosystem on the chemistry of the troposphere. Detailed descriptions related to the motivation, implementation, and instrument payloads are available in the ABLE 2A overview paper and the ABLE 2B overview paper. A collection of the publications based on ABLE 2A and 2B observation are available in the Journal of Geophysical Research special issues: Global Tropospheric Experiment/Chemical Instrumentation Test and Evaluation Results (GTE/ABLE 2A) and The Amazon Boundary Layer Experiment 2B.

Data Discovery

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Data Access

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NASA ABLE-3 Project

ABLE-3A_TraceGas_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3A (ABLE-3A) suborbital campaign. Data using grab samples, gas chromatography, and Laser Induced Fluorescence (LIF) are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.

ABLE-3A_MetNav_AircraftInSitu_Electra_Data

ABLE-3A_MetNav_AircraftInSitu_Electra_Data is the in-situ meteorology and navigational data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3A (ABLE-3A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.

ABLE-3A_AircraftRemoteSensing_Electra_DIAL_Data

ABLE-3A_AircraftRemoteSensing_Electra_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3A (ABLE-3A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.

ABLE-3A_Ground_Data

ABLE-3A_Ground_Data is the ground site data collected during the Arctic Boundary Layer Expedition - 3A (ABLE-3A) suborbital campaign. Data from the Harvard CO2 instrument and mist chambers are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.

ABLE-3A_Merge_Data

ABLE-3A_Merge_Data is the merge data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3A (ABLE-3A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.

ABLE-3A_Trajectory_Data

ABLE-3A_Trajectory_Data is the trajectory data collected during the Arctic Boundary Layer Expedition - 3A (ABLE-3A) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.

ABLE-3B_Aerosol_AircraftInSitu_Electra_Data

ABLE-3B_Aerosol_AircraftInSitu_Electra_Data is the in-situ aerosol data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3B (ABLE-3B) suborbital campaign. Data using mist chambers and teflon filters are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.

ABLE-3B_TraceGas_AircraftInSitu_Electra_Data

ABLE-3B_TraceGas_AircraftInSitu_Electra_Data is the in-situ trace gas data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3B (ABLE-3B) suborbital campaign. Data using grab samples, gas chromatography, Laser Induced Fluorescence (LIF), and the Differential Absorption CO, CH4, N2O Measurements (DACOM) instrument are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.

ABLE-3B_MetNav_AircraftInSitu_Electra_Data

ABLE-3B_MetNav_AircraftInSitu_Electra_Data is the in-situ meteorological and navigational data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3B (ABLE-3B) suborbital campaign. Data from the Turbulent Air Motion Measurement System (TAMMS) instrument are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.

ABLE-3B_AircraftRemoteSensing_Electra_DIAL_Data

ABLE-3B_AircraftRemoteSensing_Electra_DIAL_Data is the remotely sensed Differential Absorption Lidar (DIAL) data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3B (ABLE-3B) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.

ABLE-3B_Ground_Data

ABLE-3B_Ground_Data is the ground site data collected during the Arctic Boundary Layer Expedition - 3B (ABLE-3B) suborbital campaign. Data from the High-Altitude Fast-Response CO2 Analyzer (Harvard CO2) instrument are featured in this collection. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.

ABLE-3B_Merge_Data

ABLE-3B_Merge_Data is the merge data collected onboard the NASA Electra aircraft during the Arctic Boundary Layer Expedition - 3B (ABLE-3B) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.

ABLE-3B_Trajectory_Data

ABLE-3B_Trajectory_Data is the trajectory data collected during the Arctic Boundary Layer Expedition - 3B (ABLE-3B) suborbital campaign. Data collection for this product is complete. From 1983-2001, NASA conducted a collection of field campaigns as part of the Global Tropospheric Experiment (GTE). Among those were the Arctic Boundary Layer Expedition (ABLE 3) campaigns. ABLE 3 was broken into two sub-campaigns: ABLE 3A and ABLE 3B. ABLE 3A took place in the Arctic and subarctic regions of North America and Greenland, while ABLE 3B took place in the north central and northeastern regions of Canada. ABLE 3 was focused on understanding the “early warning” response of the near-surface, organic, active layer to climate variability with a special emphasis placed on identifying the range of variables which might have a significant influence on the tropospheric O3 budged in the Barrow region. The ABLE 3 campaigns took place in the summer since the summer is critical to an assessment of the full impact of accumulated winter/spring pollutant loadings. Observations in the summer months could also determine if significant long-range transport and injection of pollutants occur. ABLE 3A took place July-August 1988, and ABLE 3B took place July-August 1990. To accomplish its objectives, the ABLE 3 science team deployed the NASA Lockheed Electra aircraft on both sub-campaigns. With over 50 total flights, ABLE 3 aimed to utilize the Electra aircraft to take measurements of methane (CH4), carbon monoxide (CO), carbon dioxide (CO2), nonmethane hydrocarbons (NMHC), acetic acid (HA), formic acid (HFo), nitric oxide (NO), nitrogen dioxide (NO2), total “reactive” nitrogen gas (NOy), nitric acid (HNO3), peroxyacetyl nitrate (PAN), peroxypropionyl nitrate (PPN), ozone (O3), and aerosol chemical composition and size distribution. Onboard the Electra, airborne UV Differential Absorption Lidar (DIAL) provided remotely sense data on the two-dimensional distribution of aerosols and O3. The results of ABLE 3 indicate that atmospheric chemical changes in arctic and subarctic regions may serve as unique early warning indicators of global changes.

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NASA ABoVE Project

This document presents the Concise Experiment Plan for NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) to serve as a guide to the Program as it identifies the research to be conducted under this study. Research for ABoVE will link field-based, process-level studies with geospatial data products derived from airborne and satellite remote sensing, providing a foundation for improving the analysis and modeling capabilities needed to understand and predict ecosystem responses and societal implications. The ABoVE Concise Experiment Plan (ACEP) outlines the conceptual basis for the Field Campaign and expresses the compelling rationale explaining the scientific and societal importance of the study. It presents both the science questions driving ABoVE research as well as the top-level requirements for a study design to address them.

Arctic_Boreal_CO2_Flux_V2_2448

This dataset is a synthesis of terrestrial and freshwater CO2 and CH4 fluxes from the Arctic-Boreal region aggregated to monthly timesteps. The dataset, known as ABCFlux v2, includes 1,028 unique sites and spans 1984-2024 with the majority of observations occurring after 1999. ABCFlux v2 includes surface-atmosphere CO2 fluxes of net ecosystem exchange (NEE), gross primary production (GPP), and ecosystem respiration (Reco) alongside CH4 fluxes. For aquatic ecosystems, CH4 fluxes were split into diffusive and ebullitive flux pathways and included potential emissions from transient storage in the water column, alongside CO2 and CH4 concentrations dissolved in the surface water. Fluxes were measured through a variety of methods including chamber and eddy covariance techniques alongside bubble traps, ice-surveys, and concentration-based turbulence-driven modelling in aquatic ecosystems. Supporting variables include methodological metadata (e.g., gap-filling methods, number of chamber measurement days), environmental measurements (e.g., air, water, and soil temperatures), and site-level attributes (e.g., permafrost thaw status, disturbance history). Compared with version 1, this v2 dataset includes additional variables to represent detailed descriptions of plant functional types, deep soil temperatures (<10 cm), and permafrost thaw presence or absence in the top two meters. Data pertaining to CH4 fluxes were also added that were not included in previous version. The data are provided in comma separated values (CSV) format.

ABOLVIS1A

This data set contains geotagged images collected over Alaska and Western Canada. The images were taken by the NASA Digital Mapping Camera, paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA's Terrestrial Ecology Program campaign, the Arctic-Boreal Vulnerability Experiment (ABoVE).

ABLVIS1B

This data set contains return energy waveform data over Alaska and Western Canada measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA's Terrestrial Ecology Program campaign, the Arctic-Boreal Vulnerability Experiment (ABoVE).

ABLVIS2

This data set contains surface elevation data over Alaska and Western Canada measured by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of NASA's Terrestrial Ecology Program campaign, the Arctic-Boreal Vulnerability Experiment (ABoVE).

ABoVE_ASCENDS_XCO2_2050

This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2), methane (CH4), and water vapor concentrations, plus air temperature, pressure, relative humidity, and wind speed values over Alaska and the Yukon and Northwest Territories of Canada during 2017-07-20 to 2017-08-08. Measurements were taken onboard a DC-8 aircraft during this Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) airborne deployment over portions of the Arctic-Boreal Vulnerability Experiment (ABoVE) domain. CO2 and CH4 were measured with NASA's Atmospheric Vertical Observations of CO2 in the Earth's Troposphere (AVOCET) instrument. Water vapor and relative humidity were measured with Diode Laser Hydrometer. Measurements of column-averaged dry-air mixing ratio CO2 measurements (XCO2) were taken with the CO2 Sounder Lidar instrument. The airborne CO2 Sounder is a pulsed, multi-wavelength Integrated Path Differential Absorption lidar. It estimates XCO2 in the nadir path from the aircraft to the scattering surface by measuring the shape of the 1572.33 nm CO2 absorption line. The data were collected in order to capture the spatial and temporal dynamics of the northern high latitude carbon cycle as part of ABoVE and are provided in ICARTT file format.

ABoVE_ASCENDS_Backscatter_2051

This dataset provides atmospheric backscattering coefficient profiles collected during Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) deployments from 2017-07-20 to 2017-08-08 over Alaska, U.S., and the Yukon and Northwest Territories of Canada. These profiles were measured by the CO2 Sounder Lidar instrument carried on a DC-8 aircraft. The airborne CO2 Sounder is a pulsed, multi-wavelength Integrated Path Differential Absorption lidar that estimates column-averaged dry-air CO2 mixing ratio (XCO2) in the nadir path from the aircraft to the scattering surface. In addition to XCO2, the lidar receiver recorded the time-resolved atmospheric backscatter signal strength as the laser pulses propagated through the atmosphere. Raw lidar data were converted to the atmospheric backscatter cross-section product and the two-way atmosphere transmission, also known as attenuated backscatter profiles. These ASCENDS flights were coordinated with the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) campaign and are provided in ICARTT format.

ABoVE_ASCENDS_Merge_2114

This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2), methane (CH4), water vapor concentrations, air temperature, pressure, and wind speed and direction as well as airborne remote sensing measurements of column average CO2 collected during Active Sensing of CO2 Emissions over Nights, Days, and Seasons (ASCENDS) deployments from 2017-07-20 to 2017-08-08 over Alaska, US, and the Yukon and Northwest Territories of Canada. CO2 and CH4 were measured with NASA's Atmospheric Vertical Observations of CO2 in the Earth's Troposphere (AVOCET) instrument. Water vapor and relative humidity were measured with Diode Laser Hydrometer. Measurements were taken onboard a DC-8 aircraft. The ASCENDS flights were coordinated with the 2017 Arctic-Boreal Vulnerability Experiment (ABoVE) campaign. The data are provided in ICARTT format along with an archive of flight videos.

ABoVE_PBand_SAR_1657

This dataset provides estimates of soil geophysical properties derived from Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) P-band polarimetric synthetic aperture radar (PolSAR) data collected in August and October of 2014, 2015, and 2017 over 12 study sites (with some exceptions) across Northern Alaska. Soil properties reported include the active layer thickness (ALT), dielectric constant, soil moisture profile, surface roughness, and their respective uncertainty estimates at 30-m spatial resolution over the 12 flight transects. Most of the study sites are located within the continuous permafrost zone and where the aboveground vegetation consisting mainly of dwarf shrub and tussock/sedge/moss tundra has a minimal impact on P-band radar backscatter.

Soil_ActiveLayer_Properties_AK_2315

This dataset provides soil active layer characteristics from nine locations across Alaska. Soil samples were collected in 2016 except for one site which was sampled in 2018. Soil cores were collected from each site using a steel barrel and plastic sample tube attached to a hand drill. At the majority of sites, samples were taken from each end of three 30-m transects (i.e. samples collected at the 0 m and 30 m location of each transect). The entire thawed horizon (active layer) was sampled where possible, and the length of cores varies among sites. Cores were kept frozen until analysis in the lab. Samples were sectioned by horizon (organic and mineral), and the organic horizon was split into subsections so that no section was longer than approximately 10 cm. Coarse roots were removed, dried and weighed. Soils were measured for gravimetric water content, percent soil organic matter (SOM), pH, and bulk density. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma-separated values (CSV) format.

Permafrost_ActiveLayer_NSlope_1759

This dataset provides in situ soil measurements including soil dielectric properties, temperature, and moisture profiles, active layer thickness (ALT), and measurements of soil organic matter, bulk density, porosity, texture, and coarse root biomass. Samples were collected from the surface to permafrost table in soil pits at selected sites along the Dalton Highway in Northern Alaska. From North to South, the study sites include Franklin Bluffs, Sagwon, Happy Valley, Ice Cut, and Imnavait Creek. Measurements were made from August 22 to August 26, 2018. The purpose of the field campaign was to characterize the dielectric properties of permafrost active layer soils in support of the NASA Arctic and Boreal Vulnerability Experiment (ABoVE) Airborne Campaign.

ABoVE_ReSALT_InSAR_PolSAR_V3_2004

This dataset provides estimates of seasonal subsidence, active layer thickness (ALT), the vertical soil moisture profile, and uncertainties at a 30 m resolution for 51 sites across the ABoVE domain, including 39 sites in Alaska and 12 sites in Northwest Canada. The ALT and soil moisture profile retrievals simultaneously use L- and P-band synthetic aperture radar (SAR) data acquired by the NASA/JPL Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instruments during the 2017 Arctic Boreal Vulnerability Experiment (ABoVE) airborne campaign. The data are provided in NetCDF Version 4 format along with a python script for estimating soil volumetric water content from data.

Sat_ActiveLayer_Thickness_Maps_1760

This dataset provides annual estimates of active layer thickness (ALT) at 1 km resolution across Alaska from 2001-2015. The ALT was estimated using a remote sensing-based soil process model incorporating global satellite data from Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) and snow cover extent (SCE), and Soil Moisture Active and Passive (SMAP) satellite soil moisture records. The study area covers the majority land area of Alaska except for areas of perennial ice/snow cover or open water. The ALT was defined as the maximum soil thawing depth throughout the year. The mean ALT and mean uncertainty from 2001 to 2015 are also provided.

Photos_ThermokarstLakes_AK_1845

This dataset includes high resolution orthophotographs of 21 lakes in the region of Fairbanks, Alaska, USA. Aerial photographs were taken on October 8, 2014, three days after lake-ice formation. These photographs were used to identify open holes in lake ice that indicate the location of hotspot seeps associated with the releases of methane from thawing permafrost. Aerial photography can be used to measure changes in lake areas and to observe patterns in the formation of lake ice and other early winter lake conditions.

ABoVE_Open_Water_Map_1643

This dataset contains georeferenced three-band orthomosaics of green, red, and near-infrared (NIR) digital imagery at 1m resolution collected over selected surface waters across Alaska and Canada between July 9 and August 17, 2017. The orthomosaics were generated from individual images collected by a Cirrus Designs Digital Camera System (DCS) mounted on a Beechcraft Super King Air B200 aircraft from approximately 8-11 km altitude. Flights were over the following areas: Saskatchewan River, Saskatoon, Inuvik, Yukon River including Yukon Flats, Sagavanirktok River, Arctic Coastal Plain, Old Crow Flats, Peace-Athabasca Delta, Slave River, Athabasca River, Yellowknife, Great Slave Lake, Mackenzie River and Delta, Daring Lake, and other selected locations. Most locations were imaged twice during two flight campaigns in Canada and Alaska extending roughly SE-NW then NW-SE up to a month apart. The data were georeferenced using 303 ground control points (GCPs) across the study region.

ABoVE_AirSWOT_Radar_Data_1646

This dataset provides AirSWOT (Surface Water and Ocean Topography) Ka-band (35.75 GHz) radar data products collected from an airborne platform over parts of Alaska and Canada during the period 2017-07-09 to 2017-08-17. Flights targeted specific surface water features, including rivers, lakes, ponds, and wetlands in the ABoVE domain. The radar data include six products: elevation (above the WGS84 ellipsoid), incidence angle, magnitude (backscatter), interferometric correlation (coherence), DHDPHI (incidence angle dependent height sensitivity), and error (estimated height random error, 1-sigma standard deviation). The flight lines were selected to span a full spectrum of permafrost conditions (permafrost-free to continuous permafrost, low to high ground ice content), ecosystems, climatic regions, topographic relief, and geological substrates across the ABoVE domain to investigate surface water responses to thawing permafrost and spatial and temporal variability in terrestrial water storage by measuring elevation and extent of surface waters. The data are provided in two forms: 1) the original output (outer-swath products only) at 3.6 m2 resolution in UTM coordinates from the AirSWOT processing group at the Jet Propulsion Laboratory (JPL), and 2) the ABoVE Projection at 3.6 m2 resolution, clipped to the ABoVE reference grid tiles using the C grid.

AirSWOT_Orthomosaic_WaterMask_1655

This dataset provides NASA AirSWOT Ka-band (35.75 GHz) radar interferometry data products for water surface elevation (WSE), a derived color-infrared (CIR) digital image orthomosaic, and derived lake/wetland and river channel water masks at 3.6 x 3.6 m resolution for a study area of ~3,300 km2 in the Yukon Flats Basin (YFB) in eastern interior Alaska. The data were collected during a flight over the region on June 15, 2015.These data were collected to validate AirSWOT WSE mappings and to improve the understanding of surface water flow through complex Arctic-Boreal wetland systems.

ABoVE_AirSWOT_Water_Mask_1707

This dataset provides 1) a conservative open water mask for future water surface elevation (WSE) extraction from the co-registered AirSWOT Ka-band interferometry data, and 2) high-resolution (1 m) water body distribution maps for water bodies greater than 40 m2 along the NASA Arctic-Boreal Vulnerability Experiment (ABoVE) foundational flight lines. The masks and maps were derived from georeferenced three-band orthomosaics generated from individual images collected during the flights and a semi-automated water classification algorithm based on the Normalized Difference Water Index (NDWI). In total, 3,167 km2 of open water were mapped from 23,380 km2 of flight lines spanning 23 degrees of latitude. Detected water body sizes range from 40 m2 to 15 km2. The image tiles were georeferenced using manually selected ground control points (GCPs). Comparison with manually digitized open water boundaries yields an overall open-water classification accuracy of 98.0%.

Alaska_Lake_Pond_Occurrence_2399

The Alaska Lake and Pond Occurrence Dataset (ALPOD) is a spatially explicit map of lakes and ponds across Alaska and their seasonal fluctuations. The core product is an open water occurrence raster that: (a) separates lakes and ponds from other components of the landscape (e.g., rivers and wetlands); (b) is built from Sentinel-2 imagery and has 10-m resolution; and (c) records the percentages of time that each pixel was open water and attached to a lake or pond during the 2016-2021 ice-free seasons at near-daily temporal resolution. The number of water bodies depends on the chosen occurrence threshold, but a conservative estimate is that ALPOD maps over 800,000 lakes and ponds larger than 0.001 km2. The lake occurrence rasters are tiled by UTM zone and latitude. ALPOD also includes a vector product defined using a 25% occurrence threshold. ALPOD was created using a U-Net lake identification model and manual inspection to produce a maximum possible lake extent mask. This mask serves as the region of interest for an adaptive NDWI threshold water classification algorithm written in Google Earth Engine (GEE), which was used to classify open water within the maximum lake mask in every available cloud- and ice-free Sentinel-2 image during the study period. ALPOD is suitable for investigations of individual water bodies as well as lake and pond patterns across Alaska. The data are provided in GeoTIFF and shapefile formats.

Alder_Shrub_Soil_Alaska_V2_2300

This dataset holds measures of vegetative cover and soil characteristics for sites in interior Alaska, U.S., along the James W. Dalton Highway (Alaska Route 11). The field data were collected during August in 2018 and 2019 to study the expansion of shrub cover, particularly alders (Alnus spp.) in tundra ecosystems and the potential impact of shrubs on soil properties. Samples were measured along transects at 5- to 10-m intervals. Soil samples were collected and analyzed in the laboratory. Vegetation variables include percent cover of mosses, lichens, graminoid species, shrubs, alder, birch (Betula spp.), and willow (Salix spp.) along with the biomass, size, and age structure of alder. An allometric model to estimate alder biomass was developed. Soil metrics include moisture content, conductivity, bulk density, carbon and nitrogen content and isotope ratios. The data include the maximum annual Normalized Difference Vegetation Index (NDVI) for 2019 and the trend in maximum NDVI for 2000-2020. This is version 2 of this dataset.The data are provided in comma-separated values (CSV) format.

ABoVE_MODIS_MAIAC_Reflectance_1858

This dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances across the ABoVE domain in Alaska and western Canada from 2000 to 2017. Using random forests (RF), a machine-learning approach, the original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) to reduce artifacts and variability due to angular effects. The original MAIAC data's sub-daily temporal resolution and 1 km spatial resolution with seven land bands (bands 1-7) and five ocean bands (bands 8-12) were preserved. The resulting surface reflectance data are suitable for long-term studies on patterns, processes, and dynamics of surface phenomena. The data cover 11 different Terra and Aqua satellite MODIS MAIAC tiles.

Annual_30m_AGB_1808

This dataset provides estimated annual aboveground biomass (AGB) density for live woody (tree and shrub) species and corresponding standard errors at a 30 m spatial resolution for the boreal forest biome portion of the Core Study Domain of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) Project (Alaska and Canada) over the time period 1984-2014. The data were derived from a time series of Landsat-5 and Landsat-7 surface reflectance imagery and full-waveform lidar returns from the Geoscience Laser Altimeter System (GLAS) flown onboard IceSAT from 2004 to 2008. The Change Detection and Classification (CCDC) model-fitting algorithm was used to estimate the seasonal variability in surface reflectance, and AGB density data were produced by applying allometric equations to the GLAS lidar data. A Gradient Boosted Machines machine learning algorithm was used to predict annual AGB density across the study domain given the seasonal variability in surface reflectance and other predictors. The data received statistical smoothing to reduce noise and uncertainty was estimated at the pixel level. These data contribute to the characterization of how biomass stocks are responding to climate and disturbance in boreal forests.

Annual_Seasonality_Greenness_1698

This dataset provides annual maps of the timing of spring onset with leaf emergence, of autumn onset with leaf senescence, and of peak greenness for each 30 m pixel derived from Landsat time series of Thematic Mapper (TM) and Enhanced Thematic Mapper (ETM+) observations from 1984 to 2014. The ABoVE core domain includes 169 ABoVE grid tiles across Alaska, USA and Alberta, British Columbia, Northwest Territories, Nunavut, Saskatchewan, and Yukon, Canada. The data provided for deriving seasonality includes the total number of cloud-free observations, r-squared values between observed and spline-predicted Enhanced Vegetation Index (EVI), long-term average minimum EVI, long-term average maximum EVI, long-term average spring onset, long-term average autumn onset, annual spring onset, and annual autumn onset. The data provided for peak greenness includes annual peak Normalized Difference Vegetation Index (NDVI), Normalized Burn Ratio (NBR), annual composite red reflectance, annual composite NIR reflectance, annual composite shortwave infrared reflectance (band 6, SWIR1), annual composite shortwave infrared reflectance (band 7, SWIR2), number of dates used to calculate composites, and day of year of associated maximum composite.

Annual_Thaw_Slump_1724

This dataset provides a time series of spatial data showing the expansion of a thaw slump on the East Fork Chandalar River near the community of Venetie, Alaska, from 2008 through 2017. The erosion of vegetated areas along the river was documented by manually digitizing imagery from ESRI basemaps and Landsat 5 (TM), 7 (ETM+), and 8 (OLI), using the band combination of shortwave infrared 2, shortwave infrared 1, and red.

ABoVE_Atmospheric_Flask_Data_1717

This dataset provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e. "concentrations") from flights over Alaska and the Yukon and Northwest Territories of Canada during the Arctic Carbon Aircraft Profile (Arctic-CAP) monthly sampling campaigns from April-November 2017. The data were derived from laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard the aircraft. During each of the six monthly campaigns, flights over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain included 25 vertical profiles, from the surface up to 6 km altitude, at locations selected to complement regular long-term vertical profiles, remote sensing data, and ground-based flux tower measurements. Measurements were initiated by the aircraft pilot at predetermined locations within each profile in order to evenly distribute flask sampling points throughout each flight. A total of 408 flask samples were collected during 55 individual flights. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by thawing of Arctic permafrost.

ABoVE_Arctic_CAP_1658

This dataset provides in situ airborne measurements of atmospheric carbon monoxide (CO), carbon dioxide (CO2), methane (CH4), and water vapor concentrations, plus air temperature, pressure, relative humidity, and wind speed values over Alaska and the Yukon and Northwest Territories of Canada during the Arctic Carbon Aircraft Profile (Arctic-CAP) monthly sampling campaigns from April-November 2017. Observations have been averaged to a 10-second interval and are reported with the number of samples (N) and standard deviation. During each of the six monthly campaigns, flights over the Arctic-Boreal Vulnerability Experiment (ABoVE) domain included 25 vertical profiles, from the surface up to 6 km altitude, at locations selected to complement regular long-term vertical profiles, remote sensing data, and ground-based flux tower measurements.

AVHRR_Fire_Products_1545

This dataset provides annual forest fire burned area and daily hotspot products developed using data acquired from the Advanced Very-High-Resolution Radiometer (AVHRR) instruments carried aboard two NOAA polar-orbiting satellites (NOAA-11 and NOAA-14). The fire products were generated over 12 fire seasons (1st May - 31st October) from 1989-2000 across North America at 1-km resolution and subset to the ABoVE spatial domain of Alaska and Canada.

ABoVE_Airborne_AV3_V1_2388

This dataset includes L1B radiance and L2A surface reflectance imagery acquired by the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument over portions of Alaska and northwestern Canada in 2023. These data were collected for the Arctic-Boreal Vulnerability Experiment (ABoVE) project. NASA's AVIRIS-3 is a spectral mapping system that measures reflected radiance at 7.4-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 390-2500 nm. The AVIRIS-3 sensor has a 40 degree instantaneous field of view with 1234 pixels, providing altitude dependent ground sampling distances from 12 m to sub meter range. This dataset represents one part of a multi-sensor airborne sampling campaign conducted by eleven different aircraft teams for ABoVE. The imagery data are provided in ENVI format along with a GeoJSON showing imagery boundaries. These data were specifically processed for the ABoVE project.

ABoVE_Airborne_AVIRIS_NG_V3_2362

This dataset supersedes the previously published ABoVE AVIRIS-NG Level 2 surface reflectance files for 2017-2019 surveys of Alaska and northwestern Canada. It also includes previously unpublished L1 radiance and L2 reflectance for the 2021 surveys in Iceland when COVID-era policies prevented normal ABoVE flights, and the 2022 surveys, which returned to the ABoVE domain. The dataset comprises ~1700 individual flight lines covering ~120,000 km2 with a nominal spatial resolution of 5 m. Sampling includes individual transects to capture key gradients like the tundra-taiga ecotone and raster maps of key study areas like the CHARS Greiner watershed, the Mackenzie Delta, and the Utqiagvik/Point Barrow area. AVIRIS-NG measures reflected radiance in 425 bands at 5-nanometer (nm) intervals in the visible to shortwave infrared spectral range between 380 and 2510 nm. Measurements were radiometrically and geometrically calibrated. This dataset represents one part of a multi-sensor airborne sampling campaign conducted by eleven different aircraft teams for ABoVE. The imagery data are provided in ENVI format along with a RGB composite image for each flight line and shapefiles showing imagery boundaries.

IMERG_Precip_Canada_Alaska_2097

This dataset is a modification to the Integrated Multi-satellitE Retrievals for GPM (IMERG) Final Run microwave-only, daily precipitation Version 06 data. It provides bias-corrected IMERG monthly precipitation data for Alaska and Canada from June 2000 through December 2020 in Cloud-Optimized GeoTIFF (.tif) format. Data are provided in the units of mm/day. NASA's IMERG data product is one of the most advanced satellite precipitation products with a 0.1-degree spatial resolution and near global coverage. This dataset bias-corrected IMERG's HQprecipitation precipitation estimates, which are based on passive microwave (PMW)-only retrievals, using a linear regression method. This method utilizes empirical measurements from rain gauge stations from the Global Historical Climatology Network (GHCN) and a digital elevation model. This bias correction approach improves estimates at elevations above 500 m a.s.l., which are typically underestimated.

Boreal_Forest_Survey_Fairbanks_2390

This dataset includes five metrics of forest resilience (recruitment, invasives, permafrost change, tree damage, and radial growth) at five recently burned forest sites (2010-2019) near Fairbanks, Alaska. The sites were imaged by the Airborne Visible InfraRed Imaging Spectrometer (AVIRIS-NG) in 2017 and 2022 during the Arctic-Boreal Vulnerability Experiment (ABoVE). Field measurements were conducted in 2021. Random forest (RF) vegetation classification models constructed from key hyperspectral bands were validated with ground-truthing (GT) of 44 measured plots and 45 geotagged plots. GT included stem densities, understory cover, soil characteristics, radial growth of 51 spruce trees from cores, and visual damage assays of 668 conifers and deciduous trees. There are 10 data files in comma-separated values format (CSV) in this dataset.

NWT_Burn_Severity_Maps_1694

This dataset provides maps at 30-m resolution of landscape surface burn severity (surface litter and soil organic layers) from the 2014-2015 fires in the Northwest Territories and Northern Alberta, Canada. The maps were derived from Landsat 8 Operational Land Imager/Thermal Infrared Sensor (OLI/TIRS) imagery and two separate multiple linear regression models trained with field data; one for the Plains and a second for the Shield ecoregion. Field observations were used to estimate area burned in each of five severity classes (unburned, singed, light, moderate, severely burned) in six stratified randomly selected plots of 10 x 10-m in size across a 1-ha site. Using this five class scale a burn severity index (BSI) for each 1-ha site was calculated using multiple weighted and averaged field parameters. Pre- and post-fire phenologically paired Landsat 8 images were used to model the five discrete severity classes using midpoints as breaks.

Wildfires_NWT_Canada_1548

This data set provides a fire progression map for year 2015 and measures of burn severity and vegetation community biophysical data collected from areas that were burned by wildfires in 2014 and 2015 in the Northwest Territories, Canada. Field data collected in 2016 include an estimate of burn severity, woody seedling/sprouting data, soil moisture, peat depth, thaw depth, and vegetation cover for selected sites.

Wildfires_2014_NWT_Canada_1307

This data set provides peatland landcover classification maps, fire progression maps, and vegetation community biophysical data collected from areas that were burned by wildfire in 2014 in the Northwest Territories, Canada. The peatland maps include peatland type (bog, fen, marsh, swamp) and level of biomass (open, forested). The fire progression maps enabled an assessment of wildfire progression rates at a daily time scale. Field data, collected in 2015, include an estimate of burn severity, woody seedling/sprouting data, soil moisture, and tree diameter and height of burned sites and similar vegetation characterization at landcover validation sites.

Burned_Area_Depth_AK_CA_2063

This dataset provides annual gridded estimates of fire locations and associated burn fraction per pixel for Alaska and Canada at approximately 500 m spatial resolution for the period 2001-2019. Gridded predictions of carbon combustion and burn depth for the same period within the ABoVE extended domain using the burn area maps and field data are also available. Fire locations and date of burn (DOB) were detected by MODIS-derived active fire products. Burned area was primarily estimated from finer-scale Landsat imagery using a differenced Normalized Burn Ratio (dNBR) algorithm and upscaled to an approximate 500 m MODIS resolution. Aboveground combustion, belowground combustion, and burn depth were statistically modeled at the pixel level for every mapped burned pixel in the ABoVE extended domain based on field observations across Alaska and western Canada. Predictor variables included remotely sensed indicators of fire severity, topography, soils, climate, and fire weather. Quality flags for burned area and combustion are available. Fire is the dominant disturbance agent in Alaskan and Canadian boreal ecosystems and releases large amounts of carbon into the atmosphere. These data are useful for studies of disturbance, fire ecology, and carbon cycling in boreal ecosystems.

Southern_Boreal_Plot_Attribute_1740

This dataset provides the results of field measurements and estimates of carbon stocks and combustion rates that characterize burned and unburned southern boreal forest stands near the La Ronge and Weyakwin communities in central Saskatchewan (SK), Canada. Measurements were completed in 2016 at 47 stands that burned in the 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in comparable adjacent areas. Stands were characterized through field observations and sampling of the vegetative community (i.e., tree species, abundance, and biophysical measurements, stand age, coarse woody debris, history of fires or logging), soils (i.e., soil moisture class, unburned and burned soil organic layer depth, samples for bulk density and carbon analyses), and basic landscape geophysical traits. From these results, the pre-fire carbon stocks and carbon combustion values from both the above- and below-ground pools were estimated using a combination of linear and mixed-effects modeling and were calibrated against carbons stocks from the unburned stands. Estimates of uncertainty were generated for above- and below-ground carbon stocks and combustion values using a Monte Carlo framework paired with classic uncertainty propagation techniques.

Wildfire_Effects_Spruce_Field_1595

This dataset provides the results of field observations of soil characteristics and depth to permafrost, survey results for Composite Burn Index (CBI) determination, and Landsat-derived estimates of Relative Difference Normalized Burn Ratio (RdNBR) for 38 burned and unburned forest sites near Tanana, Alaska in 2017. Forests in the study area, at the confluence of the Yukon and Tanana Rivers about 200 km west of Fairbanks, are predominately black spruce on wetter soils and white spruce on drier soils. The burned areas were from wildfires that occurred in the summer of 2015.

ABoVE_Soil_Radiocarbon_NWT_1664

This dataset provides field data from boreal forests in the Northwest Territories (NWT), Canada, that were burned by wildfires in 2014. During fieldwork in 2015, 211 burned plots were established. From these plots, thirty-two forest plots were selected that were dominated by black spruce and were representative of the full moisture gradient across the landscape, ranging from xeric to sub-hygric. Plot observations included slope, aspect, and moisture. At each plot, one intact organic soil profile associated with a specific burn depth was selected and analyzed for carbon content and radiocarbon (14C) values at specific profile depth increments to assess legacy carbon presence and combustion. Vegetation observations included tree density. Stand age at the time of the fire was determined from tree-ring counts. Estimates of pre-fire below and aboveground carbon pools were derived. The percent of total NWT wildfire burned area comprising of "young" stands (less than 60 years old at time of fire) was estimated.

Seasonality_Tundra_Vegetation_1606

This dataset provides a summary of potential climate drivers of Arctic tundra vegetation productivity that have been compiled for growing seasons from 1982 to 2015. The scale of interest is the entire pan-arctic non-alpine tundra and the continental subdivisions of the North American and the Eurasian Arctic North of 70 degrees. These climate drivers include (1) maximum normalized difference vegetation index (MaxNDVI) and time-integrated NDVI (TI-NDVI), (2) summer sea ice concentrations, (3) oceanic heat content, (4) land surface temperature, and (5) summer warmth index (SWI). Data are provided variously as timeseries and weekly and bi-weekly averages over selected time ranges and study regions with calculated trends and trend significance. Data collected over 33 years were compiled to observe seasonal trends of vegetation productivity and to detect dynamics between arctic vegetation and climate drivers.

AK_North_Slope_NEE_CH4_Flux_1562

This dataset provides CO2 and CH4 fluxes and meteorological parameters from five eddy covariance (EC) tower sites located at Barrow (three sites), Atqasuk (ATQ) and Ivotuk (IVO), Alaska. These locations form a 300-km north-south transect across Alaska's North Slope. Flux measurements include CO2, CH4, and H2O fluxes plus sensible and latent heat fluxes. Meteorological data include air temperature, wind speed, rain, soil temperature, PAR, radiation, soil water content, RH, ground heat fluxes, and air pressure. All data are reported at half-hourly intervals and cover the period 2015-01-01 to 2017-03-09.

MODIS_MAIAC_Reflectance_1700

This dataset provides angular corrections of MODIS Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) surface reflectances by two methods at each of 62 flux tower sites (1 km x 1 km area) across the ABoVE domain in Alaska and western Canada from 2000 to 2015/2016. The original MAIAC reflectance data were corrected to consistent view and illumination angles (0 degree view zenith angle and 45 degree of sun zenith angle) using two independent algorithms: the first based on the original BRDF (Bidirectional Reflectance Distribution Function) parameters provided by the MAIAC team, and the second based on a machine learning approach (random forests). The corrected data preserve the original MAIAC data's sub-daily temporal resolution and 1 km spatial resolution with seven land bands (bands 1-7) and five ocean bands (bands 8-12). The resulting tower site sub-daily timeseries of angular corrected surface reflectances are suitable for long-term studies on patterns, processes, and dynamics of surface phenomena.

Dall_Sheep_Population_Dynamics_1640

This dataset contains estimated annual average Dall sheep (Ovis dalli dalli) lamb-to-ewe ratios for each year from 2000-2015 across the full species range in Alaska and Northwestern Canada. Sheep population data are from surveys conducted over the 14 major mountain ranges encompassing the range of Dall sheep. For this study, the mountain ranges were divided into 24 mountain units due to differing climate gradients. Estimated covariate environmental and climate data used to examine the relationship between environmental conditions and Dall sheep population performance (per mountain unit) are also provided and include precipitation, temperature, snow cover, elevation, and distance to the center of the range.

Dall_Sheep_Snowpack_1602

This dataset provides daily estimates of snow depth and snow density for the study area in Lake Clark National Park and Preserve (LCNPP), Alaska. The data were generated using SnowModel and used as snow covariates along with landscape covariates in modeling efforts to study Dall sheep movements in response to dynamic snow conditions. Thirty adult Dall sheep (12 male, 18 female) were captured and outfitted with global positioning system (GPS) collars programmed to acquire locations every seven hours. Given the individual sheep locations, their distances to land cover (e.g., shrub, forest, glacier), landscape characteristics (e.g., elevation, terrain ruggedness index (TRI), vector ruggedness measure (VRM), slope, and aspect), snow depth and density, MODIS normalized difference snow index (NDSI), and other covariates were determined and are provided in the environmental data file. The snow density and depth data are provided at 25-m, 100-m, 500-m, 2000-m, and 10000-m grid resolutions, at 1-day increments, and cover the period September 1, 2005 through August 31, 2008. The sheep, snow, and landscape data cover the years 2006, 2007, and 2008.

Snowpack_Dall_Sheep_Track_1583

This dataset contains Dall sheep (Ovis dalli dalli) hoof sinking depths in snow tracks, and snow depth, hardness, and density measurements in snow pits adjacent to the tracks. Snow measurements were collected between March 19-22, 2017 at sites on Jaeger Mesa in the Wrangell Mountains (WRST), Alaska. Estimated sheep age classes and track site coordinates are also provided.

ABoVE_Planning_Field_Sites_1582

This dataset provides a listing of the ~6,700 field sites used in planning the ABoVE Airborne Campaign (AAC) for 2017. The sites included point, polygon, and line locations that were used in determining the 2017 AAC flight paths. We intend this compilation to assist investigators in understanding the flight line choices and as a method for investigators to identify ground locations used in the airborne campaign. Data users may also search for the underlying data available at each of these locations. Site descriptors include name, coordinates, principal investigators with emails, data types, long-term archive locations, and links to project descriptions.

Frac_FuelComponent_Maps_Tundra_1761

This dataset provides maps of the distribution of three major wildland fire fuel types at 30 m spatial resolution covering the Alaskan arctic tundra, circa 2015. The three fuel components include woody (evergreen and deciduous shrubs), herbaceous (sedges and grasses), and nonvascular species (mosses and lichens). Multi-seasonal and multispectral mosaics were first developed at 30 m resolution using Landsat 8 surface reflectance data collected from 2013 to 2017. The spectral information from Landsat mosaics was combined with field observations from representative tundra vegetation plots collected during multiple field trips to model the fractional cover of fuel type components. An improved vegetation mask for shrub and graminoid-dominated tundra was developed using random forest classification and is also included. The final fractional cover maps were developed using the trained model with the multi-seasonal and multi-spectral Landsat mosaics across the entire Alaskan tundra.

Great_Slave_Lake_Ecosystem_Map_1695

This dataset provides an ecosystem type map at 12.5 meter pixel spacing and 0.2 ha minimum mapping unit for the area surrounding Great Slave Lake, Northwest Territories, Canada for the time period 1997 to 2011. The map includes nine classes for peatland, wetland, and upland areas derived from a Random Forest classification trained on multi-date, multi-sensor remote sensing images across the study extent, and using field data and high-resolution Worldview-2 image interpretation for training and validation. The nine classes are: Water, Marsh, Swamp, Open Fen, Treed Fen, Bog, Upland Deciduous, Upland Conifer, and Sparsely Vegetated. A tenth map class identifies areas of historical fires (prior to 2011) that are currently undergoing post-fire successional revegetation. This dataset provides an ecosystem type map of the area before the large fire season of 2014 to better understand the effects of fires in the area.

End_of_Season_Snow_Depth_1702

This dataset provides 20,582 snow depth measurements collected at six sites near Fairbanks, Alaska, USA. Measurements were made during March or April from 2014-2019. The sites were located at or near Goldstream, Creamer's Field, APEX, the Permafrost Tunnel and Farmer's Loop. The US Army Corps of Engineers, Cold Regions Research and Engineering Laboratory (CRREL) owns and operates facilities at the Permafrost Tunnel and Farmer's Loop. The sites are suitable for manipulation experiments, installing permanent equipment, and establishing long-term measurements.

Environmental_Disturbances_AK_1705

This dataset provides descriptions and photos of environmental conditions that impacted availability to subsistence resources by residents in nine rural communities within the Yukon River basin of Interior Alaska. The data (photos) were collected by citizens (harvesters) residing in the communities while engaged in subsistence harvesting activities. The data include descriptions of the environmental condition captured in the photo, photo date, an explanation of how the condition influenced travel and access to resources, the subsistence activity when the photo was taken, effects of the environmental condition on the participant's safety, and the participant's observations regarding frequency and extent of the condition. A sensitivity metric was derived that incorporated the adaptive capacity of the participants to environmental conditions. The observations are for the period February 2016 - June 2017.

Effect_Environment_Moose_1739

This dataset provides daily and annual air temperature, river water level, and leaf drop dates coincident with the moose (Alces alces) hunting season (September) for the area surrounding the rural communities of Nulato, Koyukuk, Kaltag, Galena, Ruby, Huslia, and Hughes in interior Alaska, USA, over the period 2000-2016. The main objective of the study was to assess how the environmental conditions impacted the success of hunters who rely on moose as a subsistence resource.

ABoVE_Frac_Open_Water_1362

This data set provides land surface fractional open water cover maps for two overlapping regions: the entire pan-Arctic region (latitude > 45 degrees) and the Arctic-Boreal Vulnerability Experiment (ABoVE) domain across Alaska and Canada. The data are a 10-day averaged time step at 5-km spatial resolution for the period 2002-2015. Data represent the aerial portion of a grid cell covered by open water. The data were produced using high frequency (89 GHz) brightness temperatures from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), with other ancillary inputs from AMSR-E/AMSR2 25-km products and the Moderate Resolution Imaging Spectroradiometer (MODIS). The resulting data record for fractional water is suitable for documenting open water patterns and inundation dynamics in boreal-Arctic ecosystems experiencing rapid climate change.

Maps_AGB_North_Slope_AK_1565

This dataset includes 30-m gridded estimates of total plant aboveground biomass (AGB), the shrub AGB, and the shrub dominance (shrub/plant AGB) for non-water portions of the Beaufort Coastal Plain and Brooks Foothills ecoregions of the North Slope of Alaska. The estimates were derived by linking biomass harvests from 28 published field site datasets with NDVI from a regional Landsat mosaic derived from Landsat 5 and 7 satellite imagery. The data cover the period 2007-06-01 to 2016-08-31.

Snow_Cover_Extent_and_Depth_1757

This dataset provides estimates of maximum snow cover extent (SCE) and snow depth for each 8-day composite period from 2001 to 2017 at 1 km resolution across Alaska. The study area covers the majority land area of Alaska except for areas covered by perennial ice/snow or open water. A downscaling scheme was used in which Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2) global reanalysis 0.5 degree snow depth data were interpolated to a finer 1 km spatial grid. In the methods used, the downscaling scheme incorporated MODIS SCE (MOD10A2) to better account for the influence of local topography on the 1km snow distribution patterns. For MODIS cloud-contaminated pixels, persistent and patchy cloud cover conditions were improved by applying an elevation-based spatial filtering algorithm to predict snow occurrence. Cloud-free MODIS SCE data were then used to downscale MERRA-2 snow depth data. For each snow-covered 1 km pixel indicated by the MODIS data, the snow depth was estimated based on the snow depth of the neighboring MERRA-2 0.5 grid cell, with weights predicted using a spatial filter.

Historical_Lake_Shorelines_AK_1859

This dataset includes maps of historical lake shorelines with derived lake areas in the southern portion of the Goldstream Valley and the surrounding landscape north of Fairbanks, Alaska, USA. Historical lake margins were mapped for 1949, 1967, and 1985 using 1 m aerial photographs available through U.S. Geological Survey Earth Explorer, and for 2009 using 2.5 m SPOT satellite image mosaics. The study area was a 214 km² area of Pleistocene-aged yedoma permafrost in the southern portion of the Goldstream Valley. An increasing number of thermokarst lakes and ponds, from 130–275 per year, were identified over the entire study period. Anthropogenic lakes, formed by mining peat, gravel, and gold concentrated in the northwestern extent of Goldstream Valley, were excluded.

IceFraction_WaterBodies_ACP_AK_2451

This dataset contains ice fraction data at 1-km spatial resolution and approximately 6-day temporal resolution for small water bodies (900 m2 to 25 km2) across the Arctic Coastal Plain of Alaska (ACP) from 2017 to 2023. The data were developed using Sentinel-1 Synthetic Aperture Radar (SAR) imagery, texture features, and temperature data. Ice fraction is summarized for 1-km grid cells. Ice cover of water bodies in the northern high latitudes (NHL) is highly sensitive to the changing climate, and its dynamics exert substantial impacts on the NHL ecosystems, hydrological processes, and carbon cycle. Compared with the Google Dynamic World (DW) product derived from Sentinel-2 observations, this dataset shows high consistency with DW (R =0.91, RMSE = 0.19) while having enhanced temporal coverage due to fewer constraints from solar illumination, cloud cover, and atmospheric conditions.

Fire_Ignitions_Locations_AK_CA_2316

This dataset provides daily fire ignition locations and timing for boreal fires in Alaska, U.S., and Canada between 2001 and 2019. The fire ignition locations and timing are extracted from the ABoVE Fire Emission Database; however, the temperate prairies of Canada, the Atlantic Highlands, and Mixed Wood Plains were not included. Fires were detected from Landsat differenced normalized burn ratio (dNBR) and the daily MODIS burned area and active fire products. Detections by dNBR were limited to fire perimeters from national fire databases. Fire ignition locations were retrieved using a local minimum within the fire perimeters. However, when fire locations were confounded due to simultaneous active fire detections, the fire ignition location was set as the centroid of these pixels. A spatial uncertainty equaling the standard deviation of the pixels' coordinates and the nominal nadir of 1000 m was applied to the fire ignition location. The temporal resolution of the ignition timing is within one day. Data is provided in comma separated values (CSV) and shapefile formats.

BurnedArea_Emissions_AK_YT_NWT_1812

This dataset provides estimates of daily burned area, carbon emissions, and uncertainty, and daily fire ignition locations for boreal fires in Alaska, U.S., and in the Yukon and Northwest Territories, Canada. The data are at 500 m resolution for the 18-year period from 2001-2018. Burned area was retrieved from combining fire perimeter data from the Alaskan and Canadian Large Fire Databases with surface reflectance and active fire data from the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6. Per-pixel carbon consumption was estimated based on a statistical relationship between field estimates of pyrogenic consumption and several environmental variables. To derive the carbon consumption estimates, the approach from Alaskan Fire Emissions Database (AKFED) was updated and extended for the period 2001-2018. Fire weather variables, temperature, and the drought code complemented remotely sensed tree cover and burn severity as model predictors. Fire ignition location and timing were extracted from the daily burned area maps.

ABoVE_L1_P_SAR_1800

This dataset provides Level 1 (L1) polarimetric radar backscattering coefficient (Sigma-0 or S-0), multi-look complex, polarimetrically calibrated, and georeferenced data products from the UAVSAR P-band SAR radar instrument collected over 74 study sites across Alaska, USA, and western Canada. The radar instrument is a fully polarimetric P-band (ultra-high frequency) SAR operating in the 420-440 MHz band. The flight campaigns took place periodically in May-August 2017 onboard a NASA Gulfstream-III aircraft. Each set of products was produced from a data take (i.e., acquisition) of the UAVSAR P-band SAR radar instrument, where one data take is equivalent to one flight line over a site. Two to four data takes were sought for each site, although for some sites as few as one or as many as six are provided. There were a total of 139 data takes over the 74 sites.

Alaska_Lake_Pond_Maps_2134

This dataset provides polygon spatial files of lake and pond extents for three sub-regions of Interior Alaska's boreal forest, and one tundra region located in Alaska's Yukon-Kuskokwim Delta. Files provide lake and pond extents of standing water without wetland vegetation or other obstructions with a minimum area of 0.01 ha. Water extents were derived from Planet Labs PlanetScope imagery with resolution of 3.125 m. A deep learning model (U-Net) was applied to PlanetScope orthotile imagery from Planet Labs' Dove-R and Super Dove satellites. The U-Net model used the red, green, blue, and near-infrared bands along with a slope raster derived from a 30-m digital elevation model (DEM) as inputs. The U-Net detected water bodies in all available cloud-free images from the snow-free period (May-September) of 2019-2021. Water body data are provided as 3-year composites (2019-2021) for all four regions and monthly climatological composites (May-September) over 2019-2021 for the three boreal forest regions. The composite water files indicate the presence of open, standing water in >40% of valid PlanetScope observations for a given composite time-slice. Files are provided in shapefile format.

Lake_Wetland_Classes_UAVSAR_1883

This dataset contains a high-resolution land cover classification focused on water and wetland vegetation classes over three NASA ABoVE Campaign regions: Yukon Flats, Alaska, USA; the Peace-Athabasca Delta, Alberta; and the Canadian Shield, Northwest Territories (NWT), Canada. The product was derived from L-band synthetic aperture radar (SAR) acquisitions from the airborne NASA UAVSAR instrument in 2017-2019. The classification was trained and validated from field visits, UAV images, satellite imagery as well as other ABoVE datasets. Classifications in all regions are provided as both preliminary 13-class versions and final, simplified 5-class versions. Training and test data used for the classifier are also included as well as characteristics of lakes in the study area. This land cover classification was developed to support a project focusing on potential methane emissions from the shallow near-shore, or littoral, regions of lakes. The emergent aquatic vegetation classes can be used as a proxy for these littoral zones. Wetland vegetation classifications are provided as gridded raster files with an approximately 5-meter spatial resolution and aligned with the original UAVSAR footprints. Composite mosaics that aggregate these UAVSAR scenes by region and day of acquisition, if applicable, are also provided. Classifications in all regions are provided as both preliminary 13-class versions and final 5-class versions.

ABoVE_GrowingSeason_Lake_Color_1866

This dataset provides an annual time series of Landsat green surface reflectance and the derived annual trend during the growing season (June and July) for 472,890 lakes across the ABoVE Extended Study Domain from 1984 to 2019. The reflectance data are from Landsat-5, Landsat-7, and Landsat-8 sensors for the green band (center wavelength 560 nm). Over 270,000 Landsat scenes were evaluated and quality assured to be cloud-free and over water. Lakes were selected from HydroLAKES, a global database of lakes of at least 10 ha. Lake surface reflectance was extracted from a 3-by-3-pixel area centered on each lake centroid from the selected Landsat scenes determined from lake polygons. This dataset demonstrates changes in lake color over time in the arctic and boreal regions of North America. Color is relevant for understanding physical, ecological, and biogeochemical processes in some of the world’s highest concentrations of lakes where climate change may have significant impacts.

Lakes_Ponds_Weekly_Occurrence_2430

This dataset provides weekly surface water occurrence across lakes and ponds in six study regions: the Alaskan Coastal Plain, Yukon Flats, Yukon Kuskokwim Delta, Mackenzie River Delta, Tuktoyaktuk Peninsula, and Anderson Plain. The study regions are in Alaska, US, and Yukon and Northwest Territories, Canada. This binary (water or not water) raster product is built from Sentinel-2 imagery and a maximum lake extent vector product also produced from Sentinel-2 imagery. When cloud-free imagery was available, raster files were produced at 10 m spatial resolution and weekly temporal resolution during the ice-free season (May-September) for the period 2016-2023. Open water was differentiated from land using an adaptive NDWI threshold water classification algorithm and then clipped to the lake and ponds vector product so that only lake and pond water is reported. The data are provided in netCDF format along with two Jupyter notebooks holding code used for this analysis.

Methane_Flux_BigTrail_Lake_2393

This dataset provides field data and gridded land cover and terrain data for an area north of Big Trail Lake, an active thermokarst lake in Goldstream Valley (near Fairbanks, Alaska, USA). Field samples were collected at 56 locations across the study area in June 2021 and August 2022. Field data include chamber-based methane (CH4) and carbon dioxide (CO2) flux, soil moisture, soil temperature, soil pH, vegetation communities, meteorological data, and soil samples. The soil samples were used to conduct SIMPER microbial community analysis and soil chemical analyses and to quantify methanogen and methanotroph relative abundance. Land cover classifications (10 cm spatial resolution) were derived from supervised random forest classifications using RedEdge-MX and multiSPEC-4C multispectral drone imagery, normalized difference vegetation index (NDVI), and USGS 3D Elevation Program (3DEP) digital surface model (DSM) data. RedEdge-MX drone imagery was collected in July 2021 and multiSPEC-4C drone imagery was collected in August 2019. Gridded microtopography and slope estimates were derived at 1 m spatial resolution using USGS 3DEP digital terrain model (DTM) data. Gridded products are provided in cloud-optimized GeoTIFF format, field data are provided in comma-separated values format, and sample location photos in JPEG format are contained within a compressed file.

Tundra_Greeness_Temp_Trends_1893

This dataset provides annual tundra greenness and summer air temperatures at a resolution of 50 km over the pan-Arctic tundra biome above 31.5 degrees over the time period 1985 to 2016. Annual tundra greenness was assessed using the maximum Normalized Difference Vegetation Index (NDVImax) derived from surface reflectance measured by sensors on the Landsat satellites. Summer air temperatures were quantified using the Summer Warmth Index (SWI) derived from an ensemble of five global temperature datasets. Tabular data include NDVImax, SWI, and estimates of uncertainty using Monte Carlo simulations at 45,334 vegetated sampling sites. Raster data provide (1) annual SWI from 1985 to 2016; (2) temporal trends in annual NDVImax and SWI from 1985 to 2016 and from 2000 to 2016; and (3) temporal correlations between annual NDVImax - SWI during these two periods. Each raster also includes estimates of uncertainty that were generated using Monte Carlo simulations. This dataset provides a new pan-Arctic product for assessing inter-annual variability in tundra using moderate resolution observations from the Landsat satellites.

BorealForest_Greenness_Trends_2023

This dataset provides information on interannual trends in annual maximum vegetation greenness from 1985 to 2019 for recently undisturbed areas in the boreal forest biome. Multi-decadal changes in remotely sensed vegetation greenness provide evidence of an emerging boreal biome shift driven by climate warming. Annual maximum vegetation greenness was assessed at about 100,000 random sample locations using an ensemble of spectral vegetation indices (NDVI, EVI2, kNDVI, and NIRv) derived from Landsat products. The dataset provides raster data summarizing vegetation greenness trends for sample locations stratified by Ecological Land Unit in GeoTIFF format. These raster data span the circum-hemispheric boreal forest biome between 45 to 70 degrees north at 300 m resolution. Estimates of uncertainty were generated using Monte Carlo simulations. Interannual trends in annual maximum vegetation greenness from 1985 to 2019 and 2000 to 2019 are provided for sample locations with adequate data for time series analysis; these data are in comma-separated values (CSV) format.

ABoVE_AGBD_Uncertainty_Maps_2465

This dataset provides annual aboveground biomass (AGB) maps and associated uncertainty maps for Alaska and Canada from 1984 to 2022 at ~30 m resolution (0.00027 degrees). The dataset was derived using predictors from synthetic spectral features from Landsat Collection 2 and Continuous Change Detection and Classification algorithm. Extensive collections of ground plots (n = 45,002) and airborne lidar data (n = 421,942) were compiled for reference AGB in order to calibrate AGB models using Extreme Gradient Boosting (XGBoost) per ecoregion. Fifty AGB predictions were derived, of which the mean and standard deviation was used as per-pixel AGB prediction and uncertainty, respectively. The dataset can promote better understanding of carbon dynamics across arctic and boreal regions of North America. The data are provided in cloud optimized GeoTIFF format.

ABoVE_ForestDisturbance_Agents_1924

This dataset provides spatial data on disturbance agents of fire, insects, and logging in the Arctic Boreal Vulnerability Experiment (ABoVE) core domain at an annual time step from 1987-2012 and 30 m resolution. Using a time-series of Landsat data, the three disturbance types were identified by abrupt changes in Tasseled Cap (dTC) indices of brightness, greenness, and wetness. Disturbances were detected by a Continuous Change Detection and Classification (CCDC) harmonic regression model applied to the time series. The dTC indices and disturbance results are provided.

Annual_Landcover_ABoVE_1691

This dataset provides two 30-m resolution time series products of annual land cover classifications over the Arctic Boreal Vulnerability Experiment (ABoVE) core domain for each year of the period 1984-2014. The data are the annual dominant plant functional type in a given 30-m pixel derived from Landsat surface reflectance, landcover training data mapped across the ABoVE domain (using Random Forests modeling, with clustering and interpretation of field photography) and very high resolution imagery to assign land cover classifications. One product has a 15-class land cover classification that breaks out forest and shrub types into several additional classes; the other product provides a simplified, 10-class approach. Classification accuracy assessment results are provided per year. Assessments were based on a probability-based random sample of reference data that supported statistically robust estimation of areas and uncertainties in mapped areas.

Boreal_LandCoverClasses_AK_CA_2423

This dataset contains a 30-m resolution time series of annual land cover classifications as the dominant plant functional type class for all of boreal Alaska and Canada from 1986 to 2020. The data were derived from a time series of Landsat Collection 2 Surface Reflectance and processed using the Continuous Change Detection and Classification (CCDC) algorithm. This dataset includes a nine-class land cover scheme. Classification accuracy was assessed using a probability-based random sample, ensuring statistically robust area estimates and uncertainty measures. The classifications were produced using a supervised Random Forest classification model and Canadian National Forest Inventory photo plot data. The data are provided in multiband GeoTIFF file format and distributed by tile in the ABoVE Level B grid.

ABoVE_Fire_Severity_dNBR_1564

This dataset contains differenced Normalized Burned Ratio (dNBR) at 30-m resolution calculated for burn scars from fires that occurred within the Arctic Boreal and Vulnerability Experiment (ABoVE) Project domain in Alaska and Canada during 1985-2015. The fire perimeters were obtained from the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) fire occurrence datasets. Only burns with an area larger than 200-ha were included. The dNBR for each burn scar at 30-m pixel resolution was derived from pre- and post-burn Landsat 5, 7, and 8 scenes within a 5-km buffered area surrounding each burn scar using Landsat LEDAPS surface reflection image pairs.

Last_Day_Spring_Snow_1528

This dataset provides the last day of spring snow cover for most of Alaska and the Yukon Territory for 2000 through 2016. The data are based on the MODIS daily snow cover fraction product (MODSCAG) and are provided at 500-m resolution. Pixels in the daily snow cover fraction grids from April 1 through July 31 were flagged as "Snow" if the snow fraction exceeded 0.15, resulting in a time series of binary daily snow cover grids for each year. The annual last day of spring snow for each pixel was identified by day of the year ranging from 91 (April 1) to 183 (July 2).

ABoVE_Footprints_WRF_AK_NWCa_1896

This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) Footprint data products for receptors (observations) located at positions along flight paths and at various fixed observing sites at circumpolar locations at northern latitudes during 2016-2019. Each aircraft and station position is treated as an independent receptor in the WRF-STILT model in order to simulate the land surface influence on observed atmospheric constituents. The footprints are independent of chemical species and can be applied to different flux models and incorporated into formal inversion frameworks. The particle trajectories that determine the footprint field are constrained only by the outer edges of the WRF modeling domain. The measurements included in this data set are crucial for understanding changes in Arctic carbon cycling and the potential threats posed by the thawing of Arctic permafrost.

ABoVE_Particles_WRF_AK_NWCa_1895

This dataset provides Weather Research and Forecasting (WRF) Stochastic Time-Inverted Lagrangian Transport (STILT) particle trajectory files for receptors located at positions along flight paths and at various fixed observing sites at circumpolar locations above 45 degrees North during 2016-2019. The particle files describe the motion of particles released backward in time over a 10-day period. The particle files are separated into archives by platform type (some platforms are combined) and can be characterized as either low resolution or high resolution depending on whether the subsequent footprint fields were generated on a circumpolar 0.5-degree grid (low-resolution) or both 0.5-degree and 0.1-degree grids (high-resolution). The platforms include flux towers at fixed sites, laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard aircraft, and observations by NASA's Orbiting Carbon Observatory-2 satellite. These particle files were thinned to retain particle location information only when the particles have non-zero contributions to the corresponding footprint field. These particle files are used to compute the footprint fields available in a companion dataset. The particle trajectories that determine the footprint field are constrained only by the outer edges of the WRF modeling domain. Likewise, the companion footprint files are provided on a regular latitude-longitude grid. This dataset extends previous research on the atmospheric transport of land-surface emissions of greenhouse gases by the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) project. In particular, the content of the low-resolution particle files is similar to those for the CARVE dataset.

ABoVE_Forage_Lichen_Maps_1867

This dataset provides modeled estimates of lichen ground cover at 30 m resolution across the Fortymile study area in interior eastern Alaska, U.S., and the Yukon Territory, Canada, for the nominal year 2015. The mapped lichens are important winter forage for the nine resident caribou (Rangifer tarandus) herds in the region. A random forest modeling approach with vegetation inputs and environmental and spectral predictors was used to estimate lichen cover for 2015. Input data for the model were aggregated from historical in-situ vegetation plots, visual aerial surveys, and recent unmanned aerial system (UAS) imagery to align with 30 m resolution Landsat pixels over the 583,200 km2 study area. The model was also used to estimate lichen cover for the year 2000 by applying the trained model to historical Landsat imagery. An estimate of lichen volume in 2015, based on a published algorithm, is also provided. In addition, site-level presence-absence maps at <1 m resolution and site-level lichen cover maps at both 2 m and 30 resolution are provided. Site-level data were derived from high-resolution RGB imagery collected in summer 2017 from UASs at 22 forested and alpine sites across interior Alaska and western Yukon. Due to the use of two unique UAS imagers at 7 sites, there are 29 data collections across the 22 sites. Each UAS data collection is associated with three data files. These landscape-scale maps could be useful for understanding trends in lichen abundance and distribution, as well as for caribou research, management, and conservation.

GPP_MODIS_Alaska_Canada_2024

This dataset contains gridded estimations of daily ecosystem Gross Primary Production (GPP) in grams of carbon per day at a 1 km2 spatial resolution over Alaska and Canada from 2000-01-01 to 2018-01-01. Daily estimates of GPP were derived from a light-curve model that was fitted and validated over a network of ABoVE domain Ameriflux flux towers then upscaled using MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) data to span the extended ABoVE domain. In general, the methods involved three steps; the first step involved collecting and processing mainly carbon-flux site-level data, the second step involved the analysis and correction of site-level MAIAC data, and the final step developed a framework to produce large-scale estimates of GPP. The light-curve parameter model was generated by upscaling from flux tower sub-daily temporal resolution by deconvolving the GPP variable into 3 components: the absorbed photosynthetically active radiation (aPAR), the maximum GPP or maximum photosynthetic capacity (GPPmax), and the photosynthetic limitation or amount of light needed to reach maximum capacity (PPFDmax). GPPmax and PPFDmax were related to satellite reflectance measurements sampled at the daily scale. GPP over the extended ABoVE domain was estimated at a daily resolution from the light-curve parameter model using MODIS MAIAC daily reflectance as input. This framework allows large-scale estimates of phenology and evaluation of ecosystem sensitivity to climate change.

ABoVE_LVIS_VegetationStructure_1923

This dataset provides Level 3 (L3) footprint-level gridded metrics and attributes collected from NASA's Land, Vegetation, and Ice Sensor (LVIS)-Facility instrument for each flightline from 2017 and 2019. In 2017, the LVIS-Facility instrument was flown at a nominal flight altitude of 28,000 ft onboard a Dynamic Aviation Super King Air B200T. In 2019, the LVIS-Facility and LVIS-Classic instruments were flown at a nominal flight altitude of 41,000 feet onboard the NASA Gulfstream V. LVIS data are collected as waveforms over footprints of ~10-m diameter. The L3 data include grids of canopy relative height (RH), complexity, canopy cover (CC), ground elevation, and the number of LVIS footprints available to produce a pixel's estimate.. These 30-m resolution grids describe the vertical column of the vegetation canopy in detail with relative canopy height metrics and are enriched with an additional set of canopy cover estimates at a variety of height thresholds. The LVIS-Facility instrument 2017 and 2019 acquisitions span Arctic, boreal, temperate, and sub-tropical landscapes in support of a variety of Arctic-Boreal Vulnerability Experiment (ABoVE)- and Global Ecosystem Dynamics Investigation (GEDI)-related science. In the ABoVE study domain of arctic and boreal Alaska and Western Canada, some of these acquisitions coincide spatially with legacy small-footprint airborne lidar. Data are included for the ABoVE domain and also for the continental U.S. and central America in support of GEDI calibration and validation. Data files are provided in GeoTIFF format and one geopackage file shows flightlines.

Methane_Ebullition_Lakes_AK_1861

This dataset includes maps of the locations and number of methane ebullition hotspots in 15 frozen lakes in the southern portion of the Goldstream Valley and the surrounding landscape just north of Fairbanks, Alaska, USA. Hotspots were identified from early winter high resolution aerial photographs acquired three days after lake-ice formation in October 2014. Hotspot ebullition seeps are defined as point-sources of high ebullition that release methane from lake sediments year-round. High rates of bubbling impede ice formation. In early winter, bubbling leads to dark, round open holes in lake ice which were visible in the aerial photos. This project investigated the role of theromkarst lakes in thawing of permafrost and mobilization of organic carbon in frozen soils.

CH4_Flux_BigTrail_Goldstream_1778

This dataset provides diffusive methane (CH4) fluxes collected from two thermokarst lakes in the Goldstream Valley, north of Fairbanks in interior Alaska. Fluxes were collected from the littoral zones, adjacent shoreline, and upland vegetation. The data were collected during July 2018. Measurements were made using a mobile, closed chamber technique where chamber air was recirculated through a Los Gatos Research (LGR) Ultraportable Cavity Ring-down Spectrometer. The chamber was large enough to enclose emergent and upland vegetation up to 1.5 m in height, allowing plant-facilitated fluxes to be measured. These in situ measurements were used to verify spatial patterns in methane flux (i.e., exponential decay with distance from water) detected by NASA's Next Generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG).

AK_Yukon_PFT_TopCover_2032

This dataset contains data files of modeled top cover estimates by plant functional type (PFT) for the Arctic and Boreal Alaska and Yukon regions. Estimates are presented for single years at 5-year intervals from 1985 to 2020. Also included are root mean square error (RMSE) and source year, which indicate the specific year from which pixels in the top cover maps were derived. Plant functional types include conifer trees, broadleaf trees, deciduous shrubs, evergreen shrubs, graminoids, forbs, and light macrolichens. Estimates were derived through the combination of two stochastic gradient-boosting models that used environmental and spectral covariates. Environmental covariates represented topographic, climatic, permafrost, hydrographic, and phenological gradients, and spectral covariates were based on Landsat Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and Operational Land Imager (OLI) data collected between 1984-2020. These maps catalog widespread changes in the distribution of PFTs occurring in the Arctic and boreal forest ecosystems, such as tundra shrub expansion, due to the intensification of disturbances such as fire and climate-driven vegetation dynamics.

MODIS_CCaN_NDVI_Trends_Alaska_1666

This dataset provides the average Normalized Difference Vegetation Index (NDVI) at 1-km resolution over the north slope of Alaska, USA, for the growing season (June-August) of each year from 2000-2015, and NDVI trends for the same period. The dataset presents growing-season averages and trends from two sources: 1) derived from 1-km, 8-day data from the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI (MOD13A2) product, and 2) predicted by the Coupled Carbon and Nitrogen model (CCaN). CCaN is a mass balance carbon and nitrogen model that was driven by 1-km MODIS surface temperature and climate data for the North Slope of Alaska and parameterized using model-data fusion, where model predictions were ecologically constrained with historical ecological ground and satellite-based data.

Albedo_Boreal_North_America_1605

This dataset contains MODIS-derived daily mean shortwave blue sky albedo for northern North America (i.e., Canada and Alaska) and a set of quality control flags for each albedo value to aid in user interpretation. The data cover the period of February 24, 2000 through April 22, 2017. The blue sky albedo data were derived from the MODIS 500-m version 6 Bidirectional Reflectance Distribution Function and Albedo (BRDF/Albedo) Model Parameters MCD43A1 dataset (MCD43A1.006, https://doi.org/10.5067/MODIS/MCD43A1.006) (Schaaf & Wang, 2015a, please refer to the MCD43 documentation and user guides for more information). Blue sky refers to albedo calculated under real-world conditions with a combination of both diffuse and direct lighting based on atmospheric and view-geometry conditions. Daily mean albedo was calculated by averaging hourly instantaneous blue sky albedo values weighted by the solar insolation for each time interval. Potter et al. (2019, https://doi.org/10.1111/gcb.14888) is the associated paper for this dataset. Note the actual extent of the dataset in Figure 1 of the User Guide. Users are encouraged to refer to the User Guide for further important information about the use of this dataset.

Alaska_Yukon_NDVI_1614

This dataset provides the maximum Normalized Difference Vegetation Index (NDVI) at 1-km resolution over northern Alaska, USA and the Yukon Territory, Canada for each year from 2002-2017, as well as a 16 year maximum NDVI product. MODIS products MOD13Q1 and MYD13Q1 from Collection 6 were acquired at 250-m pixel size from June 1-August 30 of each year. Within each growing season from 2002-2017, the maximum NDVI was determined for each pixel. These maximum NDVI values were then aggregated to 1-km by selecting the maximum NDVI from the sixteen 250-m pixels values nested within each 1-km pixel. A long-term 16-year maximum NDVI was then derived from the time series of annual maximum NDVI values.

Monthly_Hydrological_Fluxes_1647

This dataset provides modeled estimates of monthly hydrological fluxes at 0.25-degree resolution over Alaska and Canada for the years 1979-2018. The estimates were derived from the Variable Infiltration Capacity (VIC) macroscale hydrological model version 4.1.2 with water and energy balance schemes at 0.25-degree spatial and daily temporal resolution for this 38-year period. The gridded output data products are monthly average water balance variables including precipitation (P), evapotranspiration (E), 'P minus E', evaporation, soil moisture in three soil layers, base flow and runoff, snow depth, snow water equivalent (SWE), and snow sublimation, and energy balance variables including surface temperature, albedo, latent and sensible heat flux, ground heat flux, short- and long-wave and other radiative fluxes. The daily modeled values for precipitation and evapotranspiration were also aggregated to water years and precipitation was also aggregated to a 30-year climate normal average.

ABoVE_Uncertainty_Maps_1652

This dataset provides estimates of the uncertainty in components of the carbon cycle including: soil carbon stock, autotrophic respiration (Ra), heterotrophic respiration (Rh), net ecosystem exchange (NEE), net primary production (NPP), and gross primary productivity (GPP) across the entire ABoVE Study Domain at 0.5-degree resolution for the reference year 2003. The uncertainties were calculated from the multi-model (n = 20) disagreement, i.e. standard deviation, from the Trends in Net Land Atmosphere Carbon Exchanges program (TRENDY) and the North American Carbon Program (NACP) regional synthesis model outputs averaged to annual means. This total uncertainty integrates both structural uncertainty of land-surface physics among models as well as inherent parametric uncertainty introduced within models, and uncertainty from forcing data.

Vegetation_greenness_trend_1576

This dataset provides the summer NDVI trend and trend significance for the period 1984-2012 over Alaska and Canada. The NDVI were calculated per-pixel from all available peak-summer 30-m Landsat 5 and 7 surface reflectance data for the period. NDVI time series were assembled for each 30-m land location (i.e., non-water, non-snow), from observations that were unaffected by clouds as indicated by data-quality masks and following additional processing to remove anomalous NDVI values. A simple linear regression via ordinary least squares was applied to the per-pixel NDVI time series. The slope of the regression was taken as the annual NDVI trend (unit NDVI change per year) and is reported in the "trend" data files. A Student's t-test was used to assess the significance of the trend and the per-pixel significance is reported in the "trend_sig" data files. A significant positive slope indicates a greening trend, and a significant negative slope indicates a browning trend.

Chlorophyll_Fluorescence_Data_1785

This dataset provides the results of in situ measurements of needle-level chlorophyll fluorescence (ChlF) obtained from a pulse amplitude modulated (PAM) fluorometer from evergreen needleleaf forested sites one in Alaska and one in Idaho. Measured light-adapted minimal fluorescence (Ft) is reported as the quantum yield of fluorescence and light-adapted variable fluorescence over maximal fluorescence (Fv/Fm) and is reported as the quantum yield of photosystem II. Also reported for both sites are two modeled irradiance products: (1) the top-of-canopy instantaneous irradiance (W/m2) and (2) needle-level irradiance (W/m2) that was modeled to account for shadow casting and canopy orientation in modulating direct radiation. Both products were modeled to be contemporaneous with ChlF observations. At the Idaho site only, needle-level irradiance (W/m2) was measured in situ with a handheld pyranometer. The Alaska field site is located in the northern latitudinal forest-tundra ecotone (FTE) near the Dalton Highway in Northern Alaska. Thirty-six Picea glauca (white spruce) trees were sampled on 2017-07-07 to 2017-07-08. The Idaho field site is located in a montane forest near McCall, Idaho. Ten selected Abies grandis (grand fir) trees were sampled on 2019-07-05 to 2019-07-06. Measurement of needle-level ChlF occurred during clear-sky conditions such that the canopies experienced a broad range of variability in sunlit-shading patterns across the day during these near-solstice periods.

SnowMeltDuration_PMicrowave_1843

This dataset provides the annual period of snowpack melting (i.e., snow melt duration, SMD) across northwest Canada; Alaska, U.S.; and parts of far eastern Russia at 6.25 km resolution for the period 1988-2018. SMD is the number of days between the main melt onset date (MMOD) and the last day of seasonal snow cover when the melting of snow is complete. These dates were derived from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). This dataset documents variability in SMD across space and the 31-year temporal period. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). There is not a coastal mask for the 2017-2018 data. The full data are included, and data users should be aware that coastal values can be adversely affected by adjacent water bodies.

Main_Melt_Onset_Dates_1841

This dataset provides the annual date of snowpack seasonal beginning melt (i.e., main melt onset date, MMOD) across northwest Canada, Alaska, US, and parts of far eastern Russia at 6.25-km resolution for the period 1988-2023. MMOD was derived from the daily 19V (K-band) and 37V (Ka-band) GHz bands from the Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR). The PMW MMOD dataset was validated using the transition date from Freeze Degree Days (FDD) to Thaw Degree Days (TDD) from in situ air temperature observations from 31 SNOw TELemetry network (SNOTEL) stations, and compared to an established Freeze-Thaw ESDR (FT-ESDR) spring onset date record. The resulting MMOD data record is suitable for documenting the spatial-temporal impacts of MMOD variability in ecosystem services, wildlife movements, and hydrologic processes within the ABoVE (Arctic Boreal Vulnerability Experiment) domain. The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012). No coastal mask is used for the 2017-2023 data. The full data are included, and data users should be aware that values adjacent to large water bodies can be adversely affected.

Canada_Boreal_Forest_Greenness_1587

This dataset provides a 28-year time series of peak greenness (NDVI) data derived from Landsat 5 TM imagery over the boreal forest region of Canada. Landsat 5 TM scenes were collected for 46 selected sidelap sites along gradients in climate, tree cover, and disturbance history from 1984 to 2011. Peak-greenness reflectance was computed for 30-m Landsat pixels using the maximum normalized difference vegetation index (NDVI) along with the normalized burn ratio (NBR) during the period between days of the year (DOY) 180 and 204. To facilitate trend analysis at each site, the NDVI and NBR data of the 30-m Landsat pixels were regridded to the coarser MODIS 500-m (463.3-m) spatial scale to reduce the effects of missing data and to enhance the significance of the trend. The regridded NDVI and NBR 28-year time series data at 500-m resolution are provided for each of the 46 sites. Two trend analyses were run on the 500-m resolution data and are reported for each site. Supplemental site metadata are also provided, including the number of valid Landsat pixels, land cover composition, and disturbance history, for each 500-m pixel.

Permafrost_Thaw_Depth_YK_1598

This dataset provides field observations of thaw depth and dominant vegetation types, a LiDAR-derived elevation map, and permafrost distribution and probability maps for an area on the coastal plain of the Yukon-Kuskokwim Delta (YKD), in western Alaska, USA. Field data were collected during July 8-17, 2016 to parameterize and to validate the derived permafrost maps. The YKD is in the sporadic to isolated permafrost zone where permafrost forms extensive elevated plateaus on abandoned floodplains. The region is extremely flat and vulnerable to eustatic sea-level rise and inland storm surges. These high-resolution permafrost maps support landscape change analyses and assessments of the impacts of climate change on permafrost in this region of high biological productivity, critical wildlife habitats, and subsistence-based human economy.

Radial_Growth_PRI_1781

This dataset provides simultaneous in-situ measurements of the photochemical reflectance index (PRI) and radial tree growth of selected white spruce trees (Picea glauca (Moench) Voss) at the northern treeline in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. PRI and dendrometer measurements were simultaneously collected on 29 trees from six plots spaced along a 5.5 km transect from south to north where tree density becomes increasingly sparse. Measurements were made throughout the 2018 and 2019 growing seasons (May 1 to September 15) with a sampling interval of 5 minutes. The data were collected to better understand the suitability of the PRI to remotely track radial tree growth dynamics.

Veg_Soil_Tundra_Burned_Area_2119

This dataset includes field measurements from 26 burned and unburned transects established in 2008 in the region of the Anaktuvuk River tundra fire on the Arctic Slope of Alaska, US. Measurements include plant cover by species, shrub and tussock density, thaw depth, and soil depth. This wildfire occurred in 2007, and sampling took place in 2008-2011 and in 2017.

ABoVE_NWT_2017_Field_Data_1771

This dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2017 from 11 study sites in the ABoVE Study area. The 11 study areas contained 28 sites that were burned by wildfires in 2014 and 2015, and 10 unburned sites in the Northwest Territories (NWT), Canada. Burned sites included peatland and upland. These field data include assessment of burn severity, vegetation inventories, ground cover, diameter and height for trees and shrubs, seedling and sprouting cover, soil moisture, and depth of unfrozen soil. Plot sizes were 10 m x 10 m with smaller subplots for selected measurements. Similar data were collected for these sites in the years 2015-2019 and are available in related separate datasets. Field data are provided in CSV format. The dataset includes digital photographs (in JPEG format) of vegetation conditions at sampling sites.

Wildfires_NWT_Canada_2018_1703

This dataset provides vegetation community characteristics and biophysical data collected in 2018 from areas that were burned by wildfire in 2014 and 2015, and from nine unburned validation sites in the Northwest Territories, Canada. The data include vegetation inventories, ground cover, regrowth, tree diameter and height, and woody seedling/sprouting data at burned sites, and similar vegetation community characterization at unburned validation sites. Additional measurements included soil moisture, collected for validation of the UAVSAR airborne collection, and depth to frozen ground at the nine unburned sites. This 2018 fieldwork completes four years of field sampling at the wildfire areas.

Wildfires_NWT_Canada_2019_1900

This dataset provides vegetation community characteristics, soil moisture, and biophysical data collected in 2019 from 11 study areas, which contained 28 sites that were burned by wildfires in 2014 and 2015, and 14 unburned sites in the Northwest Territories (NWT), Canada. Burn sites included peatland and upland. These field data include vegetation inventories, ground cover, as well as diameter and height for trees and shrubs in the unburned sites. Similar data were collected for the unburned sites in the years 2015-18 and are available in related separate datasets. In 2019, the focus was on woody and non-woody seedling/sprouting regrowth data in the burned sites. Additional measurements collected at all sites included total peat depth, soil moisture, and active layer thickness (ALT). Soil moisture and ALT were collected for validation of the UAVSAR airborne collection and Radarsat-2 overpasses. This 2019 fieldwork completes five years of field sampling at the wildfire areas.

Rain-on-Snow_Data_V2_2415

This dataset provides maps of rain-on-snow (ROS) events across Alaska for the individual cold season months from November to March using observations from two space-borne passive microwave radiometers: (a) the Advanced Microwave Scanning Radiometer for EOS and Advanced Microwave Scanning Radiometer 2 (AMSR-E/2) from 2002 to 2023; and (b) the Special Sensor Microwave Imager and the Special Sensor Microwave Imager Sounder (SSMI/S) from 1988 to 2020. Considering the differences in sensor overpass time, observation geometry, and ancillary snow cover data, the AMSR-E/2 and SSMI/S-based ROS records were generated separately. ROS events were defined as changes in surface snow wetness and isothermal states induced by atmospheric processes associated with winter rainfall or latent heat exchange. The data are summations of the number of days with ROS events per pixel at 6-km spatial resolution per month or per 5-month water year. Winter months are when snowmelt from solar illumination is minimal and snow cover is widespread and relatively consistent throughout the region. The data are provided in GeoTIFF format.

Reflectance_Spectra_Alaska_1685

This dataset reports full-spectrum (350-2500 nm) reflectance measurements of diverse plant communities at the plot-level and individual plant species at the leaf-level, at multiple sites across northern Alaska during the 2017 and 2018 summer field seasons. Plot-level reflectance data (1 m2) include an assemblage of vascular and non-vascular species comprising tundra plant communities, while leaf-level scans are specific to one particular tundra species. Reflectance measurements were collected using a HR-1024i spectrometer and data were calibrated using a Spectralon white reference panel during sampling to correct for changing light conditions. Sampling methods and data and metadata structure follow that of the Ecological Spectral Information System (EcoSIS) Spectral Library.

River_Ice_Breakup_Freezeup_1697

This dataset provides estimates of river ice breakup and freeze-up stages along selected reaches of the Yukon and Tanana Rivers in the Yukon River Basin in interior Alaska from 1972-2016. Time series of Landsat satellite images were visually interpreted to identify the day of year and characteristics of the different stages of river ice seasonality. The stages of breakup or freeze-up were distinguished from one another based on the spatial extent and patterns of open water and ice cover. Images were displayed as false color composites, with the shortwave infrared (SWIR), near infrared (NIR), and green bands represented by red, green, and blue.

Erosion_Vegetation_Yukon_1616

This dataset provides a time series of riverbank erosion and vegetation colonization along reaches of the Yukon River (3 study areas), Tanana and Nenana Rivers (1 area), and Chandalar River (1 area) in interior Alaska over the period 1984-2017. The change data were derived from selected 30-m images from Landsat TM, Landsat ETM+, and Landsat Operational Land Imager (OLI) surface reflectance products. Image classification used the Normalized Differenced Vegetation Index (NDVI) with an NDVI threshold of 0.2 to differentiate vegetated from non-vegetated pixels. Images were assigned to one of seven or eight multiyear intervals, within the 1984-2017 overall range, for each study area. Time intervals vary by study site. Change detection identified shifts from one time interval to the next: changes from vegetated to non-vegetated classes were considered riverbank erosion and changes from non-vegetated to vegetated classes were considered vegetation colonization.

SAR_Methane_Ebullition_AK_1790

This dataset provides Synthetic Aperture Radar (SAR) estimates of lake-source methane ebullition flux in mg CH4/m2/d for thousands of lakes in five regions across Alaska. The study regions include the Atqasuk, Barrow Peninsula, Fairbanks, northern Seward Peninsula, and Toolik. L-band SAR backscatter values for early winter lake ice scenes were collected from 2007 to 2010 over 5,143 lakes using the Phased Array type L-band Synthetic Aperture Radar (PALSAR) instrument on the Advanced Land Observing Satellite (ALOS-1) satellite. The backscatter data were combined with field measurements of methane ebullition from 48 study lakes across the five regions to obtain a volumetric flux estimate for each lake. Mean methane gas-fractions from each region were applied to the SAR-based volumetric fluxes to obtain an estimate of methane ebullition mass flux per lake. The data files contain lake perimeters and the lake-specific attributes of lake area, SAR backscatter values and standard errors, volumetric flux with standard errors, mean percent of methane from gas samples, and methane ebullition mass flux.

Dissolved_Gases_Alaska_Rivers_2360

This dataset provides dissolved carbon dioxide (CO2) and methane (CH4) concentrations alongside their stable and radiocarbon isotopic compositions within the Arctic Sagavanirktok and Kuparuk River watersheds located on the North Slope of Alaska. The data were collected during the spring, fall, and summer seasons in 2022. In field separation of the bulk gaseous components (N2, CO2, and CH4) from the liquid phase was achieved using a degassing membrane contactor. Laboratory isotopic analyses were conducted at the W. M. Keck Carbon Cycle Accelerator Mass Spectrometer facility at UC Irvine. This collection aims to provide insights into the seasonal dynamics of greenhouse gas emissions in these critical Arctic environments, thereby contributing valuable information for climate change research and monitoring programs. The data are provided in comma separated values (CSV) format.

Active_Layer_Thaw_Depths_1701

This dataset provides soil active layer thaw depth measurements collected along transects at three sites near Fairbanks, Alaska, USA. Measurements were made during the late summers of 2014-2018. The sites were located at Creamer's Field, the Permafrost Tunnel, and Farmer's Loop (two transects). Vegetation ecotypes along the transects are also reported. The US Army Corps of Engineers, Cold Regions Research and Engineering Laboratory (CRREL) owns and operates facilities at the Permafrost Tunnel and Farmer's Loop. The sites are suitable for manipulation experiments, installing permanent equipment, and establishing long-term measurements.

ABoVE_Soil_ThawDepth_Moisture_1903

This dataset provides soil thaw depth and moisture (STDM) measurements and dielectric properties measured by different research teams at sites in Alaska, U.S., and the Northwest Territories, Canada. There are multiple observations per site and 352,719 total observations. The dataset includes 206,000 observations of active layer thickness measured by mechanical probing (6.0%) or ground penetrating radar (GPR) (94.0%). Approximately 16,000 volumetric water content measurements were collected using GPR (22.1%), Hydrosense I and II probes (75.3%), and DualEM (2.6%). Metadata includes the location, time, geospatial coordinates, technique, measurement teams. Measurements were typically collected in August and September near the end of the thaw season and cover the period 2008-06-22 to 2020-08-15.

ABoVE_Soil_ThawMoisture_2369

This dataset provides soil thaw depth and moisture measurements and dielectric properties measured by different research teams at sites in Alaska, U.S., and the Northwest Territories, Canada. There are multiple observations per site and 528,703 total observations. The dataset includes 223,230 observations of active layer thickness measured by mechanical probing (9.8%) or ground penetrating radar (GPR) (90.2%). Approximately 179,154 volumetric water content (VWC) measurements were collected using GPR (2.0%), HydroSense I and II probes (8.8%), in situ loggers (89.2%), and DualEM (<1.0%). Metadata includes the location, time, geospatial coordinates, sampling technique, measurement teams, and field team contact information. Measurements were typically collected in August and September near the end of the thaw season and cover the period from 2005 to 2022. This dataset, referred to as field measurements of Soil Moisture and Active layer Thickness (SMALT) (Schaefer et al., 2021), was developed for work in Clayton et al. (2021). It has been expanded in Version 2 to comprise a comprehensive dataset of NASA Arctic-Boreal Vulnerability Experiment (ABoVE) field campaign measurements of soil moisture and active layer thickness, including logger data of relevance to Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) acquisitions. The data are provided in comma separated values (CSV) format.

ALT_Soil_Collection_Protocols_2373

This dataset contains soil moisture sampling protocols and calibration algorithms for Campbell Scientific Hydrosense-I and II units used at burned and unburned sites in the ABoVE project domain. The soil moisture sampling protocols document provides guidance for sampling in situ soil moisture to relate to synthetic aperture radar (SAR) data collections. Two additional documents describe algorithms for calibrating handheld Hydrosense units used to measure volumetric water content in soils. One calibration document applies to organic soils in fire-affected sites of the Northwest Territories and Alaska. A second document provides calibrations for samples collected in non-burned tundra, boreal bog, fen, upland deciduous and conifer sites located in Alberta and tundra sites in Alaska. The information is provided in portable document format (PDF).

Soil_Temp_Moisture_Alaska_1869

This dataset provides soil temperature and volumetric water content (VWC) measurements at 15 cm depth collected at 12 selected boreal and tundra sites located across Alaska. Each site is equipped with a HOBO MicroStation Data Logger that hosts two soil temperature sensors (HOBO S-TMB-M006 Temperature Smart Sensor), and two soil moisture sensors (HOBO S-SMD-M005 10HS Soil Moisture Smart Sensor). Each sensor was installed horizontally at a depth of 15 cm within the soil profile. Samples of soil from seven sites were taken to a laboratory for determination of site-specific soil moisture sensor calibration curves to correct raw measurements. Data were nominally recorded at an hourly frequency and downloaded from the sites at least annually for the period 2016-08-11 to 2023-09-02, but data coverage varies by site. These measurements were collected at the same sites as previously archived CO2 efflux and thaw depth data. The data are provided in comma-separated values (CSV) format.

USArray_Ground_Temperature_1680

This dataset includes soil temperature profile measurements taken at 63 monitoring sites associated with the USArray program, located across the NASA ABoVE domain in interior Alaska. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m from 2016-2021 using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska. This station data complement an existing temperature monitoring network, allowing for better characterization of ground temperatures and permafrost conditions in northern and western Alaska. The temperature measurements are provided for each site in 64 data files in comma-separated values (.csv) format. Site descriptive data are also provided for soil, vegetation, and location.

Soil_Temperature_Profiles_AK_1767

This dataset includes soil temperature profile measurements taken at 16 monitoring sites in Alaska, USA, and at one site in Yukon, Canada. The six sites are collocated with seismic stations of the USArray program. The measurement dates and depths vary per site as does measurement frequency (hourly or every 6 hours). Measurements were made from the soil surface to a maximum depth of 1.5 m. Measurements were made from 2016-2018 at two sites, 2017-2019 at four sites, and 2018-2019 at 11 sites using temperature sensors attached to HOBO data loggers. These measurement stations complement existing temperature monitoring networks allowing for better characterization of ground temperatures and permafrost conditions across Alaska.

Post_Fire_C_Emissions_1787

This dataset provides spatial estimates of carbon combustion from all 2015 wildfire burned areas across Saskatchewan, Canada, on a 30-m grid. Carbon combustion (kg C/m2) was derived from post-fire field measurements of carbon stocks completed in 2016 at 47 stands that burned during three 2015 Saskatchewan wildfires (Egg, Philion, and Brady) and at 32 unburned stands in adjacent areas. The study areas covered two ecozones (Boreal Plains and Boreal Shield), two stand-replacing history types (fire and timber harvest), three soil moisture classes (xeric, mesic, and subhygric), and three stand dominance classifications (coniferous, deciduous, and mixed). To spatially extrapolate estimates of combustion to all 2015 fires in Saskatchewan, a predictive radial support vector machine model was trained on the 47 burned stands with associated environmental variables and geospatial predictors and applied to historical fire areas. The dataset also includes uncertainty estimates represented as per pixel standard deviations of model estimates derived using a Monte Carlo analysis.

ABoVE_reference_grid_1367

The Arctic - Boreal Vulnerability Experiment (ABoVE) has developed two standardized spatial data products to expedite coordination of research activities and to facilitate data interoperability. First, the ABoVE Study Domain encompasses the Arctic and boreal regions of Alaska, USA, and the western provinces of Canada, North America. Core and Extended study regions have been designated within this Domain and are provided in both a vector representation (Shapefile) and a raster representation (GeoTIFF at 1,000-meter pixel resolution). Second, a Standard Reference Grid System has been developed to cover the entire Study Domain and also extends to the eastern portion of North America. This Reference Grid is provided as a nested pair of polygon grids at scales of 240- and 30-meter spatial resolution. A shapefile is provided for each grid. Note that the designated standard projection for the all ABoVE products is the Canadian Albers Equal Area projection.

ABoVE_reference_grid_v2_1527

The Arctic - Boreal Vulnerability Experiment (ABoVE) has developed two standardized spatial data products to expedite coordination of research activities and to facilitate data interoperability. The ABoVE Study Domain encompasses the Arctic and boreal regions of Alaska, USA, and the western provinces of Canada, North America. Core and Extended study regions have been designated within this Domain and are provided in a vector representation (Shapefile), a raster representation (GeoTIFF at 1,000-meter spatial resolution), and a NetCDF file. A standard Reference Grid System has been developed to cover the entire Study Domain and extends to the eastern portion of North America. This Reference Grid is provided as nested polygon grids at scales of 240, 30, and 5-meter spatial resolution. The 5-meter grid is new in Version 2. Note that the designated standard projection for all ABoVE products is the Canadian Albers Equal Area projection.

Interior_Alaska_Subsistence_1725

This dataset provide maps to show the search and harvest areas used by community residents for all subsistence resources combined across Interior Alaska for the years 2011 through 2017. The maps show the extent of areas used by residents for those communities where data collection and research has occurred; it is not a comprehensive use map for the entire area. The maps are a composite of data collected by the Division of Subsistence, Alaska Department of Fish and Game using standardized methods where respondents indicated the search areas for species harvested, the amounts harvested, and the location and months of harvest. These data are important for research, analysis, and regulatory assessment.

Decadal_Water_Maps_1324

This data set provides the location and extent of surface water (open water not including vegetated wetlands) for the entire Boreal and Tundra regions of North America for three epochs, centered on 1991, 2001, and 2011. Each of the products were generated with at least three years of ice-free Landsat imagery. The data are at 30-m resolution and were derived from time series of Landsat 4 and 5 Thematic Mapper (TM) data and Landsat 7 Enhanced Thematic Mapper (ETM+) covering all of Alaska and all provinces of Canada. The overall goal was to generate a map of the nominal extent of water for a given epoch, where nominal is neither the maximum nor the minimum but rather a representative extent for that time period.

ABoVE_Plot_Data_Burned_Sites_1744

This dataset is a synthesis of field plot characterization data, derived above-ground and below-ground combusted carbon, and acquired Fire Weather Index (FWI) System components for burned boreal forest sites across Alaska, USA, the Northwest Territories, and Saskatchewan, Canada from 1983-2016. Unburned plot data are also included. Compiled plot-level characterization data include stand age, disturbance history, tree density, and tree biophysical measurements for calculation of the above-ground (ag) and below-ground (bg) biomass/carbon pools, pre-fire and residual post-fire soil organic layer (SOL) depths and estimates of combustion of tree structural classes. The measured slope and aspect for each site and an assigned moisture class based on topography are also provided. Data from 1019 burned and 152 unburned sites are included. From the estimates of combusted ag and bg carbon pools and SOL losses, the total carbon combusted, the proportion of pre-fire carbon combusted, and the proportion of total carbon combusted were calculated for each plot. FWI System components including moisture and drought codes and indices of fire danger were obtained for each plot from existing data sources based on the plot location, year of burn, and a dynamic start-up date (day of burn, DOB) from the global fire weather database. Data for soil characteristics are included in a separate file.

PostFire_Tree_Regeneration_1955

This dataset is a synthesis of species-specific pre- and post-fire tree stem density estimates, field plot characterization data, and acquired climate moisture deficit data for sites from Alaska, USA eastward to Quebec, Canada in fires that burned between 1989 and 2014. Data are from 1,538 sites across 58 fire perimeters encompassing 4.52 Mha of forest and all major boreal ecozones in North America. To be included in this synthesis, a site had to contain information on species-specific post-fire seedling densities. This included sites where seedlings had been counted 2-13 years post-fire, a timeframe over which there was little change in relative dominance of species based on densities. Plot characterization data includes stand age, site drainage, disturbance history, crown combustion severity, seedbed conditions, and stand structural attributes. Gridded values of Hargreaves Climate Moisture Deficit (CMD) were obtained for each plot where plot coordinates were available. These values included 30-year normals (1981-2010) and CMD in the two years immediately following the fire year. CMD anomalies were calculated as the difference between the 30-year normal and the single year values for each of the first two years after a fire. These synthesis data are provided in comma-separated values (CSV) format.

LiDAR_Tundra_Forest_AK_1782

This dataset provides terrestrial lidar scanning (TLS) point cloud data collected at 10 research plots along the forest-tundra ecotone (FTE) in the Brooks Range of Alaska, south of Chandalar Shelf and Atigun Pass on the east side of the Dalton Highway. Data were collected in mid-June 2016. Data were acquired for each plot from multiple scan positions with a Leica ScanStation C10 green wavelength laser instrument. After processing the point spacing is < 1 cm. TLS enables resolution of 3-dimensional landscape features that can be used to derive ecologically important metrics of canopy structure and surface topography at high spatial resolution.

ABoVE_Thaw_Depth_1579

This dataset provides thaw depth measurements made at seven locations across Alaska, during August 2016, June and September 2017, and July-August 2018. Three of the locations are paired unburned-burned sites. At each site, three 30-meter transects were established and thaw depth was measured at 1-meter increments along each transect using a 1.15 m T-shaped thaw depth probe. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma-separated values (CSV) format.

Boreal_CanopyCover_StandAge_2012

This dataset contains Landsat-derived locally-calibrated estimates of tree canopy cover (TCC) and forest stand age across global boreal forests from 1984-2020 in Cloud-Optimized GeoTIFF (.tif) format. These raster data span the circum-hemispheric boreal forest biome between 47 to 73 degrees north at 30 m resolution. Machine learning models calibrated with data from the World Reference System 2 were used to predict TCC from Landsat data at 30-m spatial resolution at annual temporal resolution. Through analysis of TCC time series, forest change estimates of stand age from 1984-2020 were developed. The broad spatial and temporal coverage of these data provide insight into forest and carbon dynamics of the global boreal forest system. Boreal forests store a large proportion of global soil and biomass carbon and have experienced disproportionately high levels of warming over the past century.

AK_Tundra_PFT_FractionalCover_1830

This dataset provides predicted continuous-field cover for tundra plant functional types (PFTs), across ~125,000 km2 of Alaska's North Slope at 30-m resolution. The data cover the period 2010-07-01 to 2015-08-31. The data were derived using a random forest data-mining algorithm, predictors derived from Landsat satellite observations (surface reflectance composites for ~15-day periods from May-August), and field vegetation cover and site characterization data spanning bioclimatic and geomorphic gradients. The field vegetation cover was stratified by nine PFTs, plus open water, bare ground and litter, and using the cover metrics total cover (areal cover including the understory) and top cover (uppermost canopy or ground cover), resulting in a total of 19 field cover types. The field data and predictor values at the field sites are also included.

NorthSlope_NEE_TVPRM_1920

This dataset includes hourly net ecosystem exchange (NEE) simulated by the Tundra Vegetation Photosynthesis and Respiration Model (TVPRM) at 30 km horizontal resolution for the Alaskan North Slope for 2008-2017. TVPRM calculates tundra NEE from air temperature, soil temperature, photosynthetically active radiation (PAR), and solar-induced chlorophyll fluorescence (SIF) using functional relationships derived from eddy covariance tower measurements. These relationships were then scaled over the region using gridded meteorology and a vegetation map. The site-level CO2 fluxes fell into two distinct ecosystem groups: inland tundra (ICS, ICT, ICH, IVO) and coastal tundra (ATQ, BES, BEO, CMDL). The expanded modeling framework allowed for the easy substitution of ecological behaviors and environmental drivers, including the choice of representative inland tundra site, coastal tundra site, vegetation map (CAVM, RasterCAVM, or ABoVE-LC), meteorological reanalysis product (NARR or ERA5), and SIF product (GOME2, GOSIF, or CSIF). Using all of these variations generated an ensemble of 288 different TVPRM simulations of regional CO2 flux and one additional simulation option with added aquatic and zero curtain fluxes (AqZC).

ALT_Maps_AK_CA_2332

The dataset consists of maps of estimated Active Layer Thickness (ALT) at 30-m resolution throughout the northern half of Alaska for the years 2014, 2015, and 2017. The maps were generated by using a machine learning-based regression and a set of spatial data layers to upscale ALT from narrow swaths of ALT that were retrieved from airborne high-resolution P-band Polarimetric Synthetic Aperture Radar (PolSAR) imagery. The data are provided in cloud-optimized GeoTIFF format.

ABoVE_Izaviknek_Field_Data_1772

This dataset provides ecological field data that were collected during July 2017 and July 2018 from 43 plots spanning gradients of fire history in the upland tundra of the Yukon-Kuskokwim (Y-K) Delta, Alaska. Plot-level data include vegetation species composition and structure, leaf area index (LAI), topography, thaw-depth, and soil characteristics collected at plots burned in 1971-1972, 1985, 2006-2007, 2015, or unburned controls. Vegetation species were sampled along transects using the vegetation point-intercept (VPI) sampling approach and summarized by three metrics of vegetation cover: (1) top-hit cover, (2) any-hit cover, and (3) multi-hit cover. Each metric is the total number of hits for a species divided by the total number of sample points. The VPI any-hit cover metric data were combined with Landsat imagery to develop fractional maps of any-hit cover for four aggregated plant functional types (PFTs); bryophytes, herbs, lichen, and shrubs for the upland tundra area. Photographs of vegetation transects and soil pits are included as companion files.

InundationMap_YkFlats_PeaceAth_1901

This dataset provides time series of wetland inundation coverage maps and corresponding inundation frequency maps at ~10-meter resolution estimated every 12 days during the free-water period (May to October) for the years 2017-2019 over the Yukon Flats (YK) portion of the Yukon River, Alaska, USA, and the Peace-Athabasca Delta (PAD), Alberta, Canada. Wetland inundation coverage was determined by a two-step modified decision-tree classification approach that first used Sentinel-1 C-band SAR to identify likely inundated areas across a study site and was followed by a decision-tree classification step with C-band SAR backscatter statistics thresholds to distinguish among different inundation components. The result of this process was five classes for each inundation map, namely Open Water (OW), Floating Plants (FP), Emergent Plants (EP), Flooded Vegetation (FV), and Dry Land (DRY). After all the individual (every 12 days) inundation coverage maps were derived for a study site, they were generalized to two-class maps which maintained only inundation status. These generalized maps were then stacked and summarized to produce the inundation frequency map for the site. In these maps, higher values signify more frequently inundated areas, with the maximum value representing permanently inundated pixels. The Sentinel-1 inundation mapping capability demonstrated here provided frequent, broad-scale mapping of different wetland inundation components. Integration of such products with process-based methane (CH4) models would improve simulation of CH4 emissions from wetlands.

Ecosystem_Map_SRD_PAD_1947

This dataset provides ecosystem-types for the Slave River Delta (SRD) and Peace-Athabasca Delta (PAD), Canada, for the time periods circa 2007 and circa 2017. The image resolution is 12.5 m with 0.2-hectare minimum mapping unit. Included are an 18-class modified Enhanced Wetland Classification (EWC) scheme for wetland, peatland, and upland areas. Classes were derived from a Random Forest classification trained on multi-seasonal moderate-resolution images and synthetic aperture radar (SAR) imagery sourced from aerial and satellite sensors, field data, and calculated indices. Indices included Height Above Nearest Drainage (HAND) and Topographic Position Index (TPI), both derived from a digital elevation model, to differentiate between land cover types. The c. 2007 remote sensing data were comprised of early and late growing season Landsat-5, ERS2, L-Band PALSAR from 2006 to 2010 and growing season Landsat thermal composites. The c. 2017 remote sensing data were comprised of early and late growing season Landsat-8 and L-Band PALSAR-2 from 2017 to 2019, Sentinel-1 June VV and VH mean and standard deviations, and growing season Landsat thermal composites. Elevation indices from multi-resolution TPI and HAND were created from the Japan Aerospace Exploration Agency Advanced Land Observing Satellite 30 m Global Spatial Data Model. Also included are the images used for classification and the classification error matrices for each map and time period. Data are provided in GeoTIFF and GeoPackage file formats.

Wetland_VegClassification_PAD_2069

This dataset contains land cover classification focused on water and wetland vegetation communities over the Peace-Athabasca Delta, Canada. Four classification maps with 5-m resolution were derived various combinations of Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) and Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) acquired in July and September 2019, and a historical LiDAR archive data. The maps include 10 land cover classes, including open water, emergent aquatic vegetation types, terrestrial vegetation, and forest. Based on field data, the best performing model, which combined all three data sources, achieved an overall accuracy of 93.5%. The land cover maps are provided in GeoTIFF format along with polygons of AVIRIS-NG, UAVSAR, and LiDAR footprints in shapefile and KML formats.

WhiteSpruce_Leaf_Traits_Alaska_2124

This dataset provides measurements of gas exchange (light response curves, Kok curves and ACi curves), leaf traits (carbon, nitrogen, and specific leaf area), leaf pigments (Chlorophyll a, Chlorophyll b and Carotenoids), the photochemical reflectance index (PRI), and average photosynthetic photon flux density as collected from hemispherical photographs. Data were collected on white spruce trees (Picea glauca (Moench) Voss) growing at the northern edge of the species' distribution in Alaska and at the southern edge of the species' distribution in Black Rock Forest (BRF), New York. Measurements were taken at high and low canopy positions on each tree at both sites during the 2017 growing season (2017-06-19 to 2017-07-20). Gas exchange, leaf trait, pigment and spectral measurements were obtained using a portable photosynthesis system (LI-6800, LI-COR, Lincoln, NE). Photochemical reflectance index was determined using a spectroradiometer, and hemispherical photographs were taken with a digital camera. These data were collected to better understand how vertical canopy gradients in photosynthetic physiology change from the southernmost to the northernmost range extremes of white spruce. The data are provided in comma-separated value (CSV) format.

Fire_Emissions_NWT_1561

This dataset provides estimates of wildfire carbon emissions and uncertainties at 30-m resolution, and measurements collected at burned and unburned field plots from the 2014 wildfire sites near Yellowknife, Northwest Territories (NWT), Canada. Field data were collected at 211 burned plots in 2015 and include site characteristics, tree cover and species, basal area, delta normalized burn ratio (dNBR), plot characteristics, soil carbon, and carbon combusted. Data were collected at 36 unburned plots with characteristics similar to the burned plots in 2016. The emission estimates were derived from a statistical modeling approach based on measurements of carbon consumption at the 211 burned field plots located in seven independent burn scars. Estimates include uncertainty of field observations of aboveground and belowground combustion, as well as prediction uncertainty from a multiplicative regression model. To apply the model across all 2014 NWT fire perimeters, the final model covariates were re-gridded to a common 30-m grid defined by the Arctic Boreal and Vulnerability Experiment (ABoVE) Project. The regression model was then applied to burned pixels defined by a threshold of Landsat-derived differenced Normalized Burn Ratio (dNBR) within fire perimeters. Derived carbon emissions and uncertainty in g/m2 are provided for each 30-m grid cell. The modeled NWT domain encompasses 29 tiles within the ABoVE 30-m reference grid system.

Wildfires_Date_of_Burning_1559

This dataset provides estimates of wildfire progression represented by date of burning (DoB) within fire scars across Alaska and Canada for the period 2001-2019. Burn scar locations were obtained from two datasets: the Alaskan Interagency Coordination Center (AICC) and the Natural Resources Canada (NRC) databases. All scars within these databases were used in this study. The estimated DoB was derived using an algorithm for identifying the first fire occurrence from the Moderate Resolution Imaging Spectroradiometer (MODIS) active fire detection product (MCD14ML, Collection 6) and to subsequently determine all dates of burning within fire scars. The DoB data are provided as polygons and map the daily progression of a fire within each burn scar. As a result, there is one polygon for each DoB detected within an identified burn scar boundary. The MODIS active fire points associated with the burn scar data are also provided. Data for the period 2001-2015 were first published in 2017 and data for the period 2016-2019 were added in January 2021.

Wolves_Denning_Pups_Climate_1846

This dataset provides annual gray wolf (Canis lupus) denning spatial information and timing, associated climatic and phenologic metrics, and reproductive success (i.e., pup survival) in wolf populations across areas of western Canada and Alaska within the NASA ABoVE Core Domain. The study encompasses 18 years between the period 2000-2017. Wolves were captured from eight populations following standard animal care protocols and released with Global Positioning System (GPS) collars. Data from 388 wolves were used to estimate den initiation dates (n=227 dens of 106 packs) and reproductive success in the eight populations. Each population was monitored from 1 to 12 years between 2000 and 2017. Denning parturition phenology was measured each year as the number of calendar days from January 1st to the initiation date of each documented denning event. Reproductive success was determined as to whether pups survived through the end of August following a reproductive event. To evaluate the effect of climate factors on reproductive phenology, aggregated seasonal climate metrics for temperature, precipitation, and snow water equivalent based on three biological seasons for seasonal wolf home ranges were produced. Normalized Difference Vegetation Index (NDVI) time-series data were used to estimate phenological metrics such as the start of the growing season (SOS), length of the growing season (LOS), and time-integrated NDVI (tiNDVI), and were summarized for the populations' home range.

Arctic_Winter_Respiration_v2_1762

This dataset provides soil-surface carbon dioxide (CO2) efflux derived from measurements of soil respiration with forced diffusion (FD) chambers. Soil Respiration Stations (SRS) were installed at 11 boreal and tundra sites along a broad south-to-north transect starting from near Fairbanks in interior Alaska and extending to Atqasuk in northern Alaska. Each SRS measures soil respiration and ambient atmospheric CO2 concentrations with a forced diffusion (FD) chamber to derive soil CO2 flux. The SRS also measures soil CO2 concentrations and temperatures using instrumented chambers buried at 5, 10, and 15 cm depths in the soil profile. At the highest measurement frequency, data are collected hourly, and during the lowest winter frequency, every 48 hours. The data include flux values and running median filtered values from the two or three FD chambers at each site. Soil CO2 and temperature profile data (beginning June 2017) were collected beginning 2016-08-18 through 2023-09-02. This dataset updates four sites with extended temporal coverage. As of this publication, sampling is continuing, and new data will be added as available.

Boreal_AGB_Density_ICESat2_2186

This dataset provides estimates of Aboveground dry woody Biomass Density (AGBD) for high northern latitude forests at a 30-m spatial resolution. It is designed both for boreal-wide mapping and filling the northern spatial data gap from NASA's Global Ecosystem Dynamics Investigation (GEDI) project. Mapping forest aboveground biomass is essential for understanding, monitoring, and managing forest carbon stocks toward climate change mitigation. The AGBD estimates cover the extent of high latitude boreal forests and extend southward to 50 degrees latitude outside the boreal zone. AGBD was predicted using two modeling steps: 1) Ordinary Least Squares (OLS) regression related field plot measurements of AGBD to NASA's ICESat-2 30-m lidar samples, and 2) random forest models were used to extend estimates beyond the field plots by relating ICESat-2 AGBD predictions to wall-to-wall covariate stacks from Harmonized Landsat Sentinel-2 (HLS) and the Copernicus DEM. Per-pixel uncertainties are estimated from bootstrapping both models. Non-vegetated areas (e.g. built up, water, rock, ice) were masked out. HLS composites and ICESat-2 data were from 2019-2021; three years of conditions were aggregated into the circa 2020 map. ICESat-2 data were filtered to include only strong beams, growing seasons (June through September), solar elevations less than 5 degrees, snow free land (snow flag set to 1), and "msw_flag" equal to 0 (clear skies and no observed atmospheric scattering). ICESat-2's ATL08 product was resampled to a 30-m spatial resolution to better match both the field plots and mapped pixels. HLS data (L30HLS) were used to create a greenest pixel composite of growing season multispectral data, which was then used to compute a suite of vegetation indices: NDVI, NDWI, NBR, NBR2, TCW, TCG. These were then used, in combination with the slope and elevation data from the Copernicus DEM product, to predict 30-m AGBD per 90-km tile. Estimates of mean AGBD with standard deviation are provided in cloud-optimized GeoTIFF (CoG) format. Training data are in comma-separated values (CSV) format. A polygon map of data tiles is included as a GeoPackage file and a Shapefile.

AGB_Great_Slave_Lake_NWT_2365

This dataset holds aboveground biomass (ABG) estimates for areas in the Great Slave Lake Region in the Northwest Territories of Canada for 2019. ABG was estimated from L-band synthetic aperture radar (SAR) data obtained from JAXA's Advanced Land Observing Satellite-2 (ALOS-2/PALSAR-2) and supplemented with data from NASA's airborne Uninhabited Aerial Vehicle SAR (UAVSAR) instrument. SAR data were collected from 2017 to 2021. In situ AGB measurements at 14 plots sampled in 2019 were used to calibrate a logarithmic regression to relate the radar datasets to in situ AGB data. Then, AGB was mapped over available ALOS-2 tiles. The estimates are provided in 20-Mg ha-1 bins at 100-m resolution in cloud optimized GeoTIFF format.

YKDelta_EnvChange_InfoExchange_1894

This dataset provides a booklet documenting the discussions and outcomes from a knowledge-exchange meeting with Yup'ik elders from the Yukon-Kuskokwim Delta (YKD), western Alaska, community members, and natural scientist to discuss landscape and weather changes that have been observed in their homelands. The meeting was held during November 14-16, 2018. Yup'ik participants represented several YKD villages that occupy very different biophysical environments, and they have lifelong perspectives of environmental conditions and change that predate the era of Earth-observing satellites by many decades. Nearly 16 hours of discussion and testimonials from YKD elders were recorded during the meeting. The booklet is structured according to the environmental change processes that were discussed (e.g., coastal flooding, permafrost thaw, shrub expansion, climate change) and includes narrative summaries, quotations from participants, graphical illustrations, and examples of the field- and remote-sensing-based scientific findings, and map products developed as part of the larger ABoVE project.

Alaska_Arctic_Tundra_Veg_Map_1353

This data set provides the spatial distributions of vegetation types, geobotanical characteristics, and physiographic features for the Arctic tundra region of Alaska for the period 1993-2005. Specific attributes include dominant vegetation, bioclimate subzones, floristic subprovinces, landscape types, lake coverage, and substrate chemistry. This data set generally includes areas North and West of the forest boundary and excludes areas that have a boreal flora such as the Aleutian Islands and alpine tundra regions south of treeline.

Arctic_Boreal_Burned_Area_V2_2328

This dataset provides annual cumulative end-of-season burned area in circumpolar boreal forests and tundra for the years 2002-2022. The data were generated using the Arctic Boreal Burned Area (ABBA) version 2 algorithm with MODIS collection 6 products. The algorithm is based on Normalized Burned Ratio differencing (dNBR) and is designed specifically to capture late season fires. The annual MODIS Vegetation Continuous Fields (VCF) 250-m Collection 5.1 (MOD44B) product allowed for additional vegetation-dependent dNBR thresholds within the algorithm's processing steps. The spatial domain is circumpolar regions above 50 degrees north latitude. The data are provided in cloud-optimized GeoTIFF format with 463-m resolution.

North_Slope_Veg_Plots_1536

This dataset provides vegetation cover and environmental plot and soil data collected at flux tower sites of the North Slope Arctic System Science/Land-Atmosphere-Ice Interactions (ARCSS/LAII) project in August of 1995 and 1996. The 19 ARCSS/LAII flux tower sites are located along a North-South transect from near Prudhoe Bay to the foothills of the Brooks Range on the North Slope of Alaska. At 17 of the flux tower sites, one or more vegetation plots (29 total) were established and measurements including (1) plant species cover for the major vegetation types using the Braun-Blanquet approach, (2) plot environmental data, and (3) soil profile descriptions were recorded. In addition, at all 19 sites, plant growth form composition and cover were surveyed using a point sampling technique along multiple transects within selected plots.

Arrigetch_Peaks_Veg_Plots_1358

This data set provides environmental and vegetation data collected between 1978 and 1981 from 439 study plots at Arrigetch Peaks research site, located in the Gates of the Arctic National Park and Preserve in the Endicott Mountains of the central Brooks Range, Alaska. Plots varied between 1 and 50 square meters in size and were located in 13 broad habitat types across the glaciated landscape. Environmental data include aspect, elevation, and cover of bare soil, rock, soil crust, and litter. Species data are described according to the Braun-Blanquet system. This product brings together for easy reference all the available information collected from the vegetation plots in the Arrigetch Peaks region of Alaska.

Atqasuk_Veg_Plots_1371

This data set provides vegetation species abundance data collected in 1975 from 60 sites on the Arctic Coastal Plain near Atqasuk, Alaska, as well as environmental and species data for 31 of the sites that were revisited in 2000 and 2010. The study sites are located on Arctic tundra near the Meade River, about 60 miles southwest of Barrow. Data includes baseline plot information for vegetation and site factors for the study plots subjectively located in 9 plant communities. Specific attributes include: site characteristics such as altitude, slope, aspect, and topographic position; soil pH and organic layer depth; and dominant plant communities. This product brings together for easy reference all of the available information collected from the plots that has been used for the classification, mapping, and analysis of geo-botanical factors at the Atqasuk research sites and across Alaska.

Frost_Boils_Veg_Plots_1361

This data set describes the environment, soil, and vegetation on nonsorted circles and earth hummocks at seven study sites along a N-S-transect from the Arctic Ocean to the Arctic Foothills based on data collected from 2000 to 2006. The study sites are located along the Dalton Highway, beginning in Prudhoe Bay, on the North Slope of Alaska. These frost-boil features are important landscape components of the arctic tundra. Data include the baseline plot information for vegetation, soils, and site factors for 117 study plots subjectively located in areas of homogeneous, representative vegetation on frost-heave features surrounding stable tundra. Nine community types were identified in three bioclimate subzones. Vegetation was classified according to the Braun-Blanquet system.

Happy_Valley_Veg_Plots_1354

This dataset provides environmental, soil, and vegetation data collected in July 1994 from 56 study plots at the Happy Valley research site, located along the Sagavanirktok River in a glaciated valley of the northern Arctic Foothills of the Brooks Range. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in 17 plant communities that occur in 5 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species, cover, indices, and biomass pools, soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping, and analysis of geo-botanical factors in the Happy Valley region and across Alaska.

Imnavait_Creek_Veg_Plots_1356

This dataset provides environmental, soil, and vegetation data collected during the periods of August 1984 and August-September 1985 from 84 study plots at the Imnavait Creek research site. Imnavait Creek is located in a shallow basin at the foothills of the central Brooks Range. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in 14 plant communities that occur in 19 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species, cover, indices, and biomass pools; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping, and analysis of geo-botanical factors in the Imnavait Creek region and across Alaska.

Nome_Veg_Plots_1372

This data set provides environmental, soil, and vegetation data collected in July and August 1951 from 80 study plots in the Nome River Valley about 10 miles northeast of Nome, Alaska on the Seward Peninsula. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in plant communities that were found to occur in 5 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species and cover, and soil characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping and analysis of geo-botanical factors in the Nome River Valley and across Alaska.

Oumalik_Veg_plots_1506

This data set provides environmental, soil, and vegetation data collected between 1983 and 1985 from 87 study plots near an abandoned test oil well in Oumalik, Alaska. Specific attributes include dominant vegetation, species, and cover, soil chemistry, physical characteristics, moisture, and organic matter, as well as site disturbance from various sources. The vegetation sampling sites were chosen to represent the full range of vegetation in the area with replication, and for uniformity in floristic composition and environmental conditions.

Prudhoe_Bay_Veg_Plots_1360

This data set provides environmental, soil, and vegetation data collected between 1973 and 1980 from 89 study plots in the Prudhoe Bay region of Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for study plots subjectively located in 43 plant communities and 4 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation, species, and cover; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for classification, mapping, and analysis of geobotanical factors in the Prudhoe Bay region and across Alaska.

Toolik_Lake_Veg_Plots_1333

This dataset provides environmental, soil, and vegetation data collected in August 1989 from 81 study plots at the Toolik Lake research site, located in the southern Arctic Foothills of the Brooks Range, Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in 26 communities and 4 broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species, cover, indices, and biomass pools; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping, and analysis of geobotanical factors in the Toolik Lake region and across Alaska.

Umiat_Veg_Plots_1370

This data set provides vegetation cover and plot data collected during the periods of July and August, 1951, from 51 stands (areas of homogeneous vegetation communities) in the the Umiat region of Alaska, on the Colville River. The Umiat area is within the Northern Foothills section of the Arctic Foothills province on the slope north of the Brooks Range. Data include vegetation species, percent cover classes, soil moisture, topographic position, slope, aspect, and plot shape and size.

ATLAS_Veg_Plots_1541

This data set provides environmental, soil, and vegetation data collected from study sites on the North Slope and Seward Peninsula of Alaska during the Arctic Transition in Land-Atmosphere System (ATLAS) project. ATLAS-1 sites on the North Slope, located in Barrow, Atqasuk, Oumalik, and Ivotuk, were sampled in 1998-1999. ATLAS-2 sites located at Council and Quartz Creek on the Seward Peninsula were sampled in 2000. Specific attributes include dominant vegetation species and cover, biomass, soil chemistry and moisture, leaf area index (LAI), normalized difference vegetation index (NDVI), topography and elevation, and plant cover abundance.

Barrow_Tundra_Veg_Plots_1535

This data set provides vegetation cover and environmental plot data collected as part of the International Biological Program (IBP), U. S. Tundra Biome Program, in Barrow, Alaska in 1972. Forty-three (43) plots were assessed for estimated percent land cover by species and plot data including moisture, topographic position, slope, aspect, shape, and soil data. In 1999, 2008, and 2010, 33 of the same plots were resampled for these same measures as part of the IPY "Back to the Future: Resampling old research sites to assess changes in high latitude terrestrial ecosystem structure and function" project. The tundra at Barrow is considered coastal tundra located in the most northern region of North Slope and is characterized by various microtopographic features such as polygons, as well as many ponds and lakes.

Barrow_NGEE_Arctic_Veg_Plots_1505

This data set provides vegetation cover and environmental plot data collected on the Barrow Environmental Observatory (BEO), Barrow, Alaska in 2012. Forty-eight 1 x 1 m plots were established in homogenous plant communities along two perpendicular transects across ice wedge polygon geomorphic features on the BEO. Plots were distinguished as to their location within the polygons as center, edge, or trough. Vegetation data were originally collected by the U.S. Department of Energy (DOE) Next-Generation Ecosystem Experiment (NGEE) Arctic Project as part of a larger study to understand the response of Arctic terrestrial ecosystems to climate change.

Pingo_Veg_Plots_1507

This data set provides vegetation species and vegetation plot data collected between 1983 and 1985 from 293 study plots on 41 pingos on the North Slope of Alaska. The pingos were located within the Arctic Coastal Plain in the Kuparuk, Prudhoe Bay, Kadleroshilik, and Toolik River areas. Specific attributes include dominant vegetation species, cover, soil pH, moisture, and physical characteristics of the plots.

Tundra_Fire_Veg_Plots_1547

This dataset provides environmental and vegetation data collected in late June and July of 2011 and of 2012 from study plots located in tundra fire scars and adjacent unburned tundra areas on the Seward Peninsula and the northern foothills of the Brooks Range in Arctic Alaska. The surveys focused on upland tundra settings and provide information on vegetative differences between the burned and unburned sites. The sampling design established a chronosequence of sites that varied in time since last fire to better understand post-fire vegetation successional trajectories. Complete species lists and their cover abundance data are provided for both study areas. Environmental data include the baseline plot descriptive information for vegetation, soils, and site factors. No soil samples were collected.

Flux_Tower_Zona_Veg_Plots_1546

This data set provides vegetation, environmental, and soil data collected from plots located in the footprints of eddy covariance flux towers along a 300 km north-south latitudinal gradient from Barrow, to Atqasuk, and to Ivotuk across the North Slope of Alaska in 2014. Within each of the five flux tower footprints, 1x1-m quadrats were placed subjectively within widespread habitat or micro-habitat types to map the dominant vegetation communities and site environmental factors. Specific attributes included species cover data and environmental, soil and spectral data (active layer thaw depth, moss layer depth, organic horizon layer depth, standing water depth, soil moisture status, vegetation height, LAI).

Canadian_West_Arctic_Veg_Plots_1543

This dataset provides vegetation, soil, and plot characteristics for 154 study plots located at three sites across the Richardson Mountains, Northwest Territories (NWT), and the British Mountains, Yukon Territory (YT). Study sites in the NWT included areas near Canoe Lake and Divided Lake; the study site in the YT was near Trout Lake. Specific attributes include dominant vegetation, species cover, and the physical characteristics of the plot areas. A soil pit was dug at each plot and the physical and chemical characteristics were determined for soil horizons. The data were collected in June, July, and August of 1965 and July and August of 1966.

Arctic_Network_Veg_plots_1542

This dataset provides environmental, soil, and vegetation data collected at selected locations in the parks and preserves of the National Park Service (NPS) Arctic Network (ARCN) between 2002 and 2008. The ARCN includes five national parks and preserves in northern Alaska encompassing 19.5 million acres and represents some of the wildest, most undisturbed areas left on earth: The Bering Land Bridge National Preserve, Cape Krusenstern National Monument, Gates of the Arctic National Park and Preserve, Kobuk Valley National Park, and the Noatak National Preserve. The sampling sites were chosen to represent the full range of vegetation in the area with replication, and for uniformity in floristic composition and environmental conditions and were positioned on transects along toposequences within major physiographic units (riverine, lacustrine, lowland, upland, subalpine and alpine). Specific attributes include dominant vegetation, species, and cover, soil chemistry, physical characteristics, moisture, and organic matter, as well as site disturbance from various sources.

Willow_Veg_Plots_1368

This data set provides environmental, soil, and vegetation data collected in July and August 1997 from 85 study plots in willow shrub communities located along a north-south transect from the Brooks Range to Prudhoe Bay on the North Slope of Alaska. Data includes the baseline plot information for vegetation, soils, and site factors for the study plots subjectively located in three broad habitat types across the glaciated landscape. Specific attributes include: dominant vegetation species, cover, indices, and biomass pools; soil chemistry, physical characteristics, moisture, and organic matter. This product brings together for easy reference all the available information collected from the plots that has been used for the classification, mapping, and analysis of geobotanical factors in the region and across Alaska.

Barter_Barrow_Veg_Plots_1534

This dataset provides vegetation cover and environmental plot and soil data collected at two U.S. Air Force sites at Barter Island (BI) and Point Barrow (B), on the coastal North Slope of Alaska, in 1994. At Barter Island, 31 plots, and 30 plots at Barrow, were subjectively located in 14 plant communities. The investigation was part of a larger study initiated by the United States Congress to provide an opportunity to enhance the stewardship of the natural and cultural resources land under Department of Defense jurisdiction. These two sites were characterized to build an inventory of the biotic communities to compare them to historic communities.

Unalaska_Veg_Plots_1375

This data set provides environmental, soil, and vegetation data collected during August 2007 from 69 study plots at the Unalaska Island research site, and one plot on Amaknak Island. The study sites are within the eastern Aleutian Islands, Alaska, USA. Data includes the plot information for vegetation, soils, and site characteristics for the study plots subjectively located in 11 plant communities that occur in six broad habitat types. Specific attributes include: dominant vegetation species and cover, soil chemistry, moisture, organic matter, topography, and elevation. Cover-abundance was estimated for all vascular plants, bryophytes, and macrolichens according to the nine-point ordinal scale of Westhoff and van der Maarel (1973).

Poplar_Veg_Plots_1376

This data set provides vegetation cover and environmental plot data collected from 32 balsam poplar (Populus balsamifera L., Salicaceae) vegetation plots located on the Arctic Slope of Alaska and in the interior boreal forests of Alaska and the Yukon from 2003 to 2005. The estimated percent land cover by species per plot are according to the older Braun-Blanquet cover-abundance scale. Plot data includes moisture, topographic position, slope, aspect, shape, and soil data.

Prudhoe_Bay_ArcSEES_Veg_Plots_1555

This dataset provides environmental, soil, and vegetation data collected from study plots in the vicinity of Lake Colleen off the Spine Road at Prudhoe Bay, Alaska, during August of 2014. Data include vegetation species, leaf area index (LAI), percent cover classes, soil moisture and color, and plot characteristics including geology, topographic position, slope, aspect, and plot disturbance.

LandCover_Great_Lakes_Basin_2440

This dataset holds maps of land cover and wetland ecotype for the Great Lakes Basin (GLB) circa 2010. The data were derived from multi-season L-band SAR, Landsat 5 optical imagery, and thousands of field training data samples. The dataset includes detailed landcover maps of shorelines of each of five Great Lakes along with a land cover map of the entire GLB. This is the first binational map of the GLB that specifically depicts native wetland ecotypes and monocultures of invasive wetland plants (Phragmites australis and Typha spp.). The goal of the effort was to improve understanding of nutrient flow and transport of water across the landscape to the coasts and the processes at the land-water interface in wetland ecosystems that lead to the success of invasive species. The data are provided in Cloud-optimized GeoTIFF (COG), shapefile, comma separated values (CSV), and text file formats. An associated report is included in PDF format.

Plot_Data_Noatak_Seward_AK_1919

This dataset includes field measurements from unburned and burned 10 m x 10 m and 1 m x 1 m plots in the Noatak, Seward, and North Slope regions of the Alaskan tundra during July through August in the years 2016-2018. The data include vegetation coverage, soil moisture, soil temperature, soil thickness, thaw depth, and weather measurements. Measurements were recorded using ocular assessments and standard equipment. Plot photographs are included.

Arctic_Vegetation_Maps_1323

This data set provides the spatial distributions of vegetation types, geobotanical characteristics, and physiographic features for the circumpolar Arctic tundra biome for the period 1982-2003. Specific attributes include dominant vegetation, bioclimate subzones, floristic subprovinces, landscape types, lake coverage, Arctic treeline, elevation, and substrate chemistry data. Vegetation indices, trends, and biomass estimate products for the circumpolar Arctic through 2010 are also provided.

Boreal_AGB_Density_ICESat2_V3_2437

This dataset provides estimates of aboveground dry woody biomass density (AGBD) and vegetation height for high northern latitude forests at a 30-m spatial resolution for the year 2020, accounting for >30% of global forest area. The estimates were derived with state-of-the-art earth observation datasets collected from space, including lidar observations from NASA's ICESat-2 and imagery from NASA's Harmonized Landsat/Sentinel-2 project. They are designed for circumpolar boreal-wide mapping from local to global scales and provide the northern component of global forest structure estimates, to which complementary estimates from NASA's Global Ecosystem Dynamics Investigation (GEDI) mission contribute temperate and tropical portions. The AGBD and height predictions cover the extent of high latitude boreal forests and shrublands, and while they extend southward outside the boreal domain nominally to ~50 degrees N they are intended to contribute to global estimates northward from 51.6 degrees N.

CH4_CO2_WaterBodies_YK_Delta_2178

This dataset provides estimates of carbon dioxide (CO2) and methane (CH4) diffusive fluxes from waterbodies, and watershed landcover data for the central-interior of the Yukon-Kuskokwim Delta (YK delta), Alaska. Dissolved concentrations of methane and carbon dioxide were predicted using an integrated terrestrial-aquatic approach to scale observations based on landscape and waterbody remote sensing drivers. The observations include 300 samples of surface water dissolved gases collected in July 2016-2019 from the central region of the YK Delta, Alaska. A machine learning model was used to generate estimated fluxes. Model inputs include Sentinel-2 MSI with derived normalized difference vegetation index (NDVI) and normalized difference water index (NDWI), an Arctic digital elevation model (DEM) with derived slope and flow accumulation, Sentinel-1 C-band July and December VV and VH composites, and a landcover map. Waterbody size, shape, and reflectance were determined using object-based image analysis in Google Earth Engine. Landscape-level input data were averaged in non-nested sub-basins calculated using the System for Automated Geoscientific Analyses (SAGA) "channel network" algorithm at three threshold sizes. Cross validation was used to tune and select variables for gradient boosting models. The trained gradient boosting models were then used to predict dissolved methane and carbon dioxide in all waterbodies (17,000) in the region. These aquatic concentrations were converted to fluxes using an average gas transfer velocity from observations (0.33 m/d). The data are provided in GeoTIFF and shapefile formats.

ABoVE_SnowModel_Data_2105

This dataset provides daily SnowModel simulation outputs on a 3-km grid for the period 1 September 1980 through 31 August 2020, covering the Core ABoVE Domain. The daily outputs include: air temperature (deg C), relative humidity (%), wind speed (m/s), wind direction (deg from True North), total precipitation (rain+snow) (m), rainfall (m), snowfall (m), snow melt (m), snow sublimation (m), runoff (m), surface temperature (deg C), bulk snowpack thermal resistance (K/W), snow depth (m), snow density (kg/m3), and snow-water-equivalent (SWE) depth (m). Model data inputs included land cover and topography, ground-based observations of snow, remote sensing observations of snow from satellites and aircraft, and meteorological forcing data from weather stations and reanalysis data. The SnowModel includes the processing modules MicroMet, Enbal, SnowDunes, SnowAssin, SnowPack, and SnowTran-3D. The data are provided in NetCDF format.

DeciduousFractionl_CanopyCover_2296

This dataset holds deciduous fraction and tree canopy cover at 30-m resolution over the North American boreal domain for 1992 to 2015. Deciduous fraction is the areal percentage of deciduous trees relative to all tree canopy cover within a pixel, and tree canopy cover is the areal percentage of a pixel that is covered by tree canopy. Deciduous fraction values are valid only for pixels with tree canopy cover >25 percent. Normalized difference vegetation index (NDVI)-based median-value image composites were derived from Landsat 5, 7, and 8 Collection 1 surface reflectance datasets for years 1987-1997, 1998-2002, 2003-2007, 2008-2012, and 2013-2018 to create composites for nominal years 1992, 2000, 2005, 2010, and 2015, respectively. These image composites were prepared for early spring, mid-summer, and mid-to-late fall seasons to identify key differences in deciduous and evergreen green-up amplitudes. Random Forest (RF) regression models were used to derive deciduous fraction and tree canopy cover from the image composites. These models were trained with data from in-situ samples across Alaska and Canada from a variety of studies. Seventy percent of the in-situ samples were used for training and 30% for validation. Per-pixel uncertainty for both deciduous fraction and tree canopy cover are included and were based on one standard deviation of output values across all decision trees in the RF regression. These datasets were developed as part of NASA's ABoVE project to capture forest composition changes over the North American boreal domain across the last several decades. The data are provided in GeoTIFF format.

ArcticTreeLine_Dendrometry_Env_2185

This dataset provides in situ measurements of radial tree growth of selected white spruce (Picea glauca) and black spruce (Picea mariana) trees, as well as simultaneous in situ measurements of environmental variables (air temperature, air pressure, relative humidity, soil temperature, volumetric water content, and solar irradiance) at two Arctic treeline sites: one in the Brooks Range of Alaska (AK), USA, and the other near Inuvik, Northwest Territories (NWT), Canada. In AK, 36 trees were monitored from June 7, 2016 to September 13, 2019, and in NWT, 24 trees were monitored from July 5, 2017 to July 25, 2019 with a sampling interval of 5- or 20-minutes for radial tree growth and 5-minutes for all environmental variables. The dendrometer data included in this dataset are only those gathered from 2016-2017. Dendrometer data from 2018-2019 are available from a related dataset. The data were collected to better understand the influence of environmental variables on radial tree growth dynamics. The data are provided in comma-separated values (CSV) format.

FieldData_Alaska_Tundra_2177

This dataset, titled the Synthesized Alaskan Tundra Field Database (SATFiD), provides a comprehensive collection of in-situ field data compiled from 37 existing datasets resulting from field surveys conducted at Alaska tundra sites between 1972 to 2020. The data were harmonized prior to being included in this dataset. The variables include active layer thickness, vegetation cover (by plant functional types), soil moisture and temperatures, as well as the wildfire history. SATFiD provides a unique lens into various long-term ecological processes within the tundra (such as the fire-permafrost-vegetation interactions) under a rapidly changing climate.

Prudhoe_Bay_Veg_Maps_1387

This data set provides a collection of maps of geoecological characteristics of areas within the Beechey Point quadrangle near Prudhoe Bay on the North slope of Alaska: a geobotanical atlas of the Prudhoe Bay region, a land cover map of the Beechey Point quadrangle, and cumulative impact maps in the Prudhoe Bay Oilfield for ten dates from 1968 to 2010. The geobotanical atlas is based on aerial photographs and covers 145 square kilometers of the Prudhoe Bay Oilfield. The land cover map of the Beechey Point quadrangle was derived from the Landsat multispectral scanner, aerial photography, and other field and cartographic methods. The cumulative impact maps of the Prudhoe Bay Oilfield show historical infrastructure and natural changes digitized from aerial photos taken in each successive analysis year (1968, 1970, 1972, 1973, 1977, 1979, 1983, 1990, 2001, and 2010). Nine geoecological attributes are included: dominant vegetation, secondary vegetation, tertiary vegetation, percentage open water, landform, dominant surface form, secondary surface form, dominant soil, and secondary soil. These data document environmental changes in an Arctic region that is affected by both climate change and rapid industrial development.

GeoCryoAI_PermafrostThaw_CFlux_2371

This dataset provides model code, input data, sample results, and documentation for an artificial intelligence-driven model, GeoCryoAI. GeoCryoAI is a hybridized process-constrained ensemble learning framework consisting of stacked convolutionally layered long short-term memory-encoded recurrent neural networks. The purpose of GeoCryoAI is to quantify permafrost thaw dynamics and greenhouse gas emissions in Alaska. The dataset includes pre-processed input data (i.e., thaw depth, active layer thickness, thaw subsidence; CO2 flux, CH4 flux) acquired from in situ measurements (e.g., CALM, GTNP, ITEX, SMALT STDM, ReSALT, AmeriFlux, NEON), remote sensing platforms (e.g., UAVSAR, AVIRIS-NG), and process-based modeling products. Field data were included to quantify CO2 and CH4 flux (e.g., chamber, eddy-covariance, and tall-tower measurements via flux tower networks) and active layer thickness (e.g., mechanical probing, borehole temperatures, ground-penetrating radar). These measurements were resampled to a 1-km grid, standardized, transformed, and assimilated into GeoCryoAI, a framework that simultaneously ingests, scales, and analyzes input data after resolving disparate spatiotemporal sampling and data densities. Model outputs were generated from two process-based models: SIBBORK-TTE derived thaw subsidence and TCFM-Arctic generated carbon flux outputs. The objective was to quantify how the Arctic is changing in response to climate change and how evidence of the permafrost carbon feedback may contribute toward a better understanding of the uncertainty of nonlinear feedbacks and their impact on the earth system.

ABoVE_CO2_CH4_Flux_Estimates_2121

This dataset provides gridded estimates of gross primary productivity (GPP), ecosystem respiration (Reco), net ecosystem CO2 exchange (NEE = Reco - GPP), and methane (CH4) emissions from tundra and boreal wetland soils, across the pan-Arctic and Boreal zone (>49 degrees north) at 1-km spatial resolution. The data were produced through simulations of the Arctic Terrestrial Carbon Flux Model (TCFM-Arctic) and are provided at the daily time step for the years 2003-2015. TCFM-Arctic uses a light-use efficiency approach driven by satellite estimates of FPAR (fraction of absorbed photosynthetically active radiation) to estimate GPP, and autotrophic respiration (Rauto) is estimated as a fraction of GPP. Heterotrophic respiration (Rhetero) is estimated using decomposition rates with environmental constraints applied to three near-surface soil organic carbon (SOC) pools, and Reco is determined as the sum of Ra and Rh. Methane production is estimated using optimal CH4 production rates with environmental constraints applied to the labile carbon pool, and transfer of CH4 from the soil to the atmosphere is modeled through vegetation, soil diffusion, and water ebullition pathways. The model estimates were calibrated and evaluated using >60 tower eddy covariance (EC) sites. Baseline carbon pools were initialized by continuously cycling (spinning-up) the model for 1,000 model years using recent climatology from 1985 to 2002 to reach a dynamic steady-state between estimated net primary productivity (NPP = GPP - Rauto) and near-surface SOC pools. The TCFM-Arctic simulations were extended to the full Arctic-boreal domain at a 1-km spatial resolution using land cover maps representing high latitude vegetation communities. The data are provided in NetCDF and comma-separated values (CSV) formats.

Soil_Carbon_Flux_Maps_1683

This dataset provides gridded estimates of soil CO2 flux (g C m-2 d-1) for the winter non-growing season (NGS) across pan-Arctic and Boreal permafrost regions (>49 Deg N), at 25 km spatial resolution. The data are the daily average flux over a monthly period for two climate periods: the baseline climate period represents 2003-2018 and the future climate scenarios period represents 2018-2100 under Representative Concentration Pathways (RCP) 4.5 and 8.5. The data were produced by applying a Boosted Regression Tree machine learning approach to create gridded estimates of emissions based on in situ observations of NGS fluxes provided in a related dataset. The resulting monthly average flux data records can be used to calculate annual NGS soil CO2 flux budgets from 2003-2100.

Vegetation_Photos_Toolik_Lake_1718

This dataset contains 731 ground-based nadir vegetation community and ground surface photographs of selected field plots taken as ground reference data for vegetation classification studies at three areas near Toolik Lake, Alaska during the summers of 2014 and 2015. The largest area, 'Toolik', (approximately 6 km2) covers research areas near Toolik Field Station at Toolik Lake, including Arctic LTER installations. The other two areas are each roughly 3 km2: the 'Pipeline' area: a stretch of the Trans-Alaska Pipeline, and the 'Imnavait' area: along Imnavait Creek roughly 10 km east of Toolik Lake.

Shrub_Biomass_Toolik_Lake_AK_1573

This dataset contains estimates for aboveground shrub biomass and uncertainty at high spatial resolution (0.80-m) across three research areas near Toolik Lake, Alaska. The estimates for August of 2013 were generated and mapped using Random Forest modeling with input variables of optimized LiDAR-derived canopy volume and height, mean NDVI from 4-band RGB color and near-IR orthophotographs, and harvested biomass data. Uncertainty in the final shrub biomass maps was quantified by producing separate maps showing the coefficient of variation (CV) of the Random Forest map estimates. Shrub biomass was harvested at Toolik Lake in 2014 and used to optimize inputs and validate the final model and these biomass data are also provided.

Vegetation_Maps_Toolik_Lake_1690

This dataset contains vegetation community maps at 20 cm resolution for three landscapes near the Toolik Lake research area in the northern foothills of the Brooks Range, Alaska, USA. The maps were built using a Random Forest modeling approach using predictor layers derived from airborne lidar data and high-resolution digital airborne imagery collected in 2013, and vegetation community training data collected from 800 reference field plots across the lidar footprints in 2014 and 2015. Vegetation community descriptions were based on the commonly used classifications of existing Toolik area vegetation maps.

LakeBathymetry_Model_NSlope_AK_2243

This dataset provides lake bathymetry maps derived from Landsat surface reflectance products for a portion of the North Slope area of Alaska. A random forest regression algorithm was used to generate depths for each point identified as being part of a lake, creating depth prediction files for each Landsat scene available for the study period: 2016-07-01 to 2018-08-31. These products are fitted to the ABoVE standard projection and reference grid to make them easily scalable and geometrically compatible with other products in the ABoVE study domain. The data are provided in cloud-optimized GeoTIFF (COG) format.

Lakes_Ponds_Inundation_YKDelta_2450

This dataset provides high resolution (3 m) maps delineating open water and partial inundation in the Yukon-Kuskokwim Delta (YKD), Alaska, and a database that tracks rapid changes in surface area for individual lakes and ponds over 2019-2023. The dataset was built using daily PlanetScope imagery (3 m), allowing for tracking changes in surface water extent at high spatial and temporal resolutions. Two types of maps were produced; (1) open water lakes and ponds and (2) partial inundation, defined as areas that partially contain water (e.g. emergent vegetation, flooded vegetation, or saturated soil). Open water lakes and ponds were mapped using a convolutional neural network, and partial inundation was classified using a pixel-based thresholding approach. For both map categories, monthly climatological composites for the snow and ice-free period are provided. For open water lake and pond maps, monthly composites are provided as well. An accompanying database includes the geometry and surface area time series for each mapped water body.

Northern_Alaska_Veg_Maps_1359

This data set provides four land cover and ecosystem classification maps for northern Alaska. The maps were produced for several projects and from different data sources including Landsat imagery and existing maps and models, and cover a range of ecosystem and vegetation classes. The data used to derive the maps covered the period 1976-08-04 to 2014-09-01.

Seward_Peninsula_Veg_Maps_1363

This data set provides two landcover and vegetation maps for the Seward Peninsula, Alaska. These maps were produced from existing maps, Landsat imagery, and color infrared aerial photography covering the period 1976-06-01 to 1999-09-01.

Arctic_Wildlife_Refuge_Veg_Map_1384

This data set provides a landcover map with 16 landcover classes for the northern coastal plain of the the Arctic National Wildlife Refuge (ANWR) on the North Slope of Alaska. The map was derived from Landsat Thematic Mapper (Landsat TM) data, Digital Elevation Models (DEMs), aerial photographs, existing maps, and extensive ground-truthing. The data used to derive the map cover the period 1982 to 1993.

ABoVE_Domain_Projected_LULC_2353

This dataset provides projections of land use and land cover (LULC) change within the Arctic Boreal Vulnerability Experiment (ABoVE) domain, spanning from 2015 to 2100 with a spatial resolution of 0.25 degrees. It includes LULC change under two Shared Socioeconomic Pathways (SSP126 and SSP585) derived from Global Change Analysis Model (GCAM) at an annual scale. The specific land types include: needleleaf evergreen tree-temperate, needleleaf evergreen tree-boreal, needleleaf deciduous tree-boreal, broadleaf evergreen tree-tropical, broadleaf evergreen tree-temperate, broadleaf deciduous tree-tropical, broadleaf deciduous tree-temperate, broadleaf deciduous tree-boreal, broadleaf evergreen shrub-temperate, broadleaf deciduous shrub-temperate, broadleaf deciduous shrub-boreal, C3 arctic grass, C3 grass, C4 grass, and C3 unmanaged rainfed crop. The data were generated by integrating regional LULC projections from GCAM with high-resolution MODIS land cover data and applying two alternative spatial downscaling models: FLUS and Demeter. Data are provided in NetCDF format.

LVISC1B

This data set contains Level-1B geolocated return energy waveforms collected by the NASA Land, Vegetation, and Ice Sensor (LVIS) Facility, an imaging lidar and camera sensor suite.

LVISC2

This data set contains Level-2 geolocated surface elevation and canopy height measurements collected by the NASA Land, Vegetation, and Ice Sensor (LVIS) Facility, an imaging lidar and camera sensor suite.

CO2Fluxes_Arctic_Boreal_Domain_2377

This dataset provides gridded estimates of gross primary productivity (GPP), ecosystem respiration (Reco), and net ecosystem CO2 exchange (NEE) across the circumpolar terrestrial Arctic-boreal region at a 1-km spatial resolution. Monthly CO2 flux data from 2001 to 2020 were generated using terrestrial eddy covariance and chamber CO2 flux observations, combined with geospatial meteorological, remote sensing, topographical and soil data, all within a random forest modeling framework. Aggregated average annual NEE, average annual NEE with direct fire emissions added based on the Global Fire Emissions Database (GFED) product, and temporal trends in annual NEE rasters over 2002-2020 are also included. The data are provided in NetCDF and GeoTIFF formats.

Imnavait_Creek_Veg_Maps_1385

This dataset provides the spatial distribution of vegetation types, soil carbon, and physiographic features in the Imnavait Creek area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology. Data are also provided on the research grids for georeferencing. The map data are from a variety of sources and encompass the period 1970-06-01 to 2015-08-31.

Kuparuk_Veg_Maps_1378

This data set provides a collection of vegetation, landscape, geobotanical, elevation, hydrology, and geologic maps for the Kuparuk River Basin, North Slope, Alaska. The maps cover either (1) the entire Kuparuk River Basin, from the headwaters on the north side of the Brooks Range to the Beaufort Sea coast, or (2) the selected Upper Kuparuk River Region including the Toolik Lake and Imnavait Creek research areas. The maps were produced from imagery and existing geobotanical maps covering the period 1976-08-04 to 2008-12-31.

Toolik_Lake_Area_Veg_Maps_1380

This data set provides the spatial distributions of vegetation types, soil carbon, and physiographic features in the Toolik Lake area, Alaska. Specific attributes include vegetation, percent water, glacial geology, soil carbon, a digital elevation model (DEM), surficial geology and surficial geomorphology.

North_Slope_Transect_Veg_Maps_1386

This dataset includes vegetation cover maps, Normalized Difference Vegetation Index (NDVI) maps, snow depth and thaw depth data that were obtained as part of a biocomplexity project on the North Slope of Alaska, USA, and the Northwest Territories (NWT), Canada. In Alaska, seven sites are located along the Dalton Highway and in the Prudhoe Bay Oilfield area, forming a transect across the climate gradient of the North Slope. From South to North, the sites are Happy Valley, Sagwon (an acidic and nonacidic site), Franklin Bluffs, Deadhorse, West Dock and Howe Island. Four sites are in the NWT, forming a latitudinal gradient from South to North; the sites include Inuvik, Green Cabin, Mould Bay, and Isachsen.

CH4_Fluxes_ThermokarstLakes_AK_1870

This dataset provides methane fluxes from hot-spot and non-hot spot differing surfaces at Big Trail Lake (BTL) in the Goldstream Valley near Fairbanks, AK, USA. Measurements were taken at a remotely-sensed methane hotspot on the shoreline of a pond, adjacent to BTL with a Los Gatos Ultra-Portable Greenhouse Gas Analyzer (UGGA), and from various non-hotspot surfaces representative of the broader thermokarst lake ecosystem with bucket chambers. All data were collected between 2019-07-04 and 2019-12-04 during the daytime hours of 09:35-17:32 local time. A ground-based CH4 enhancement survey was performed on 2019-07-06 between 13:25-17:15 Alaska Daylight Time (AKDT), approximately two hours following an AVIRIS-NG overflight and hotspot detection at the Eastside Pond. Methane flux is reported in units of both mmol CH4 m-2 hr-1 and mg CH4 m-2 d-1. Flux errors are quantified for each

ABoVE_Annual_Veg_Resilience_2374

This dataset provides estimates of vegetation resilience in the Arctic Boreal Vulnerability Experiment (ABoVE) core domain at annual time steps for 2000-2019 and at 300-m spatial resolution. Vegetation resilience is defined as the recovery rate from deviations, due to climate perturbations or disturbances, to the equilibrium state. It is quantified as the negative temporal lag-1 autocorrelation of Enhanced Vegetation Index (EVI). Using a time series of MODIS EVI, the vegetation resilience was estimated using a Bayesian dynamic linear model. The mapped vegetation resilience was derived from Terra EVI products (MOD13Q1v061) across 175 ABoVE B grid tiles over the ABoVE core domain. The estimated mean resilience, upper boundary, and lower boundary results are provided for each tile in cloud optimized GeoTIFF (COG) format.

NDVI_Forest_Structure_1797

This dataset provides leaf area index (LAI), tree species and canopy cover, normalized difference vegetation index (NDVI), and NDVI trends for boreal forests in interior Alaska, U.S. These data were collected to investigate NDVI trends with forest structure and composition as influenced by disturbance and succession. The data are from 102 sites surveyed in 2017 and 2018 and include locations with and without a fire since 1940. A time series of NDVI was developed from Landsat (1999-2018) to measure NDVI trends. The field data cover the period 2017-08-29 to 2018-08-20. The surveyed forest stands spanned a distance of over 425 km across interior Alaska. The sites were selected before visiting the field to include locations with and without a fire since 1940. Recently burned sites were selected to span a range of years since fire, while sites without a recent fire were selected to include a range of Landsat NDVI trends. For each year, the median NDVI during the growing season was calculated. Then, a simple linear regression trend was calculated for years 1999-2018.

Passive_Microwave_Snowoff_Data_1711

This dataset provides annual maps of the snowoff (SO) date from 1988-2023 across Alaska and parts of Far East Russia and northwest Canada at a resolution of 6.25 km. SO date is defined as the last day of persistent snow and was derived from the MEaSUREs Calibrated Enhanced-Resolution Passive Microwave (PMW) EASE-Grid Brightness Temperature (Tb) Earth System Data Record (ESDR) product. The spatial domain was intended to match MODIS Alaska Snow Metrics and extend its temporal fidelity beyond the MODIS era. SO date estimates were compared to snow depth measurements collected at SNOTEL stations across Alaska and to three SO datasets derived from MODIS, Landsat, and the Interactive Multisensor Snow and Ice Mapping System (IMS). The data from 1988-2016 included a coastal mask removing coastal pixels due to potential water contamination from coarse brightness temperature observations (Dersken et al., 2012).

Post_Fire_SOC_NWT_2235

This dataset provides site moisture, soil organic layer thickness, soil organic carbon, nonvascular plant functional group, stand dominance, ecozone, time-after-fire, jack pine proportion, and deciduous proportion for 511 forested plots spanning ~140,000 km2 across two ecozones of the Northwest Territories, Canada (NWT). The plots were established during 2015-2018 across 41 wildfire scars and unburned areas (no burn history prior to 1965), with 317 plots in the Plains and 194 plots in the Shield regions. At each plot, two adjacent 30-m transects were established 2 m apart, running north from the plot origin. Soil organic layer (SOL) depth (cm) was measured every 3 m and the mean was taken from the 10 measurements to calculate a plot-level SOL thickness. Three soil organic layer profiles were destructively sampled at 0, 12, and 24 m using a corer that was custom designed for NWT soils. Within the transects, all stems taller than 1.37 m were identified to species to calculate tree density (stems / m2). Nonvascular plant percent cover was identified to functional group at five, 1-m2 quadrats spaced 6 m apart along the belt transect. A subset of 2,067 of 5,137 total increments from 1,803 profiles from 421 plots were analyzed for total percent C using a CHN analyzer. Time-after-fire was established using fire history records. For older plots where no known fire history is recorded, tree age was used. Data are for the period 2015-06-11 to 2018-08-24 and are provided in comma-separated values (CSV) format.

Gas_Seeps_Lakes_Fairbanks_SAR_2394

This dataset includes maps and locations of potential gas seepage in lakes within the immediate and surrounding area around Fairbanks, Alaska, a region underlain by discontinuous permafrost and characterized by thermokarst lake formation. Gas bubbling from lakes in the Fairbanks area is usually rich in methane, a potent greenhouse gas that is difficult to quantify. A new remote sensing method was used for detecting potential gas seepage, defined as areas of suspected perennial ebullition, by using a previously ground-truthed seep that appeared as a high-backscatter perennial feature in winter L-band Synthetic Aperture Radar (SAR) data from 2006-2011. Based on threshold values determined by the sigma-naught backscatter of this training seep, 1,690 areas of potential seepage were detected in 459 lakes out of 658 lakes analyzed. Results were validated through a different ground-truthed seep. The data are provided in shapefile format.

ALT_GPR_Barrow_1355

This data set provides estimates of Active Layer Thickness (ALT) determined with ground-based measurements, and calculated soil volumetric water content (VWC) at four selected sites around Barrow, Alaska in August 2013. ALT was determined using a ground-penetrating radar (GPR) system and traditional mechanical probing. Calculated uncertainties are also included. GPR measurements were taken along four transects of varying length (approx. 1 to 7 km). Mechanical probing included several high-density surveys (every 1 m within 100-m survey line) along each GPR transect. VWC of the active layer soil was calculated at 3-8 calibration points per site where the probe measurement was exactly co-located with a GPR trace.

Alaska_L4_WRF_STILT_Footprints_1544

This dataset provides Stochastic Time-Inverted Lagrangian Transport model outputs for receptors located at the NOAA Barrow Alaska Observatory for 12 selected years (15 August to 15 October) across the 30-year, 1982 to 2011, study timeframe. Meteorological fields from version 3.5.1 of the Weather Research and Forecasting model are used to drive STILT. STILT applies a Lagrangian particle dispersion model backwards in time from a measurement location (the "receptor" location), to create the adjoint of the transport model in the form of a "footprint" field. The footprint, with units of mixing ratio (ppm --- CO2; ppb --- CH4) per (umol m-2 s-1 --- CO2; nmol m-2 s-1 --- CH4), quantifies the influence of upwind surface fluxes on concentrations measured at the receptor and is computed by counting the number of particles in a surface-influenced volume and the time spent in that volume. The simulation results included in this dataset are crucial for understanding changes in Arctic carbon cycling and are part of a retrospective analysis to link changes in atmospheric composition at Arctic receptor sites with shifts in ecosystem structure and function. Each file provides the surface influence-function footprints on a lat/lon/time grid from WRF-STILT simulations for the receptor location.

ReSALT_ALT_GPR_1265

This data set includes estimates of permafrost Active Layer Thickness (ALT; cm), and calculated uncertainties, derived using a ground-penetrating radar (GPR) system in the field in August 2014 near Toolik Lake and Happy Valley on the North Slope of Alaska. GPR measurements were taken along 10 transects of varying length (approx. 1 to 7 km). Traditional ALT estimates from mechanical probing every 100 to 500 m along each transect are also included. These data are suitable for future studies of how ALT varies over relatively large geological features, such as hills and valleys, wetland areas, and drained lake basins.

PreABoVE_AirMOSS_L1_Alaska_1678

This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multi-look complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over 10 study sites across Northern Alaska, USA. Flight campaigns took place in August 2014, October 2014, April 2015, August 2015, September 2015, and October 2015. The acquired L1 P-band radar backscatter data will be used to derive estimates of soil water content and permafrost state at the study sites.

ReSALT_InSAR_Barrow_1266

Active layer thickness (ALT) is a critical parameter for monitoring the status of permafrost that is typically measured at specific locations using probing, in situ temperature sensors, or other ground-based observations. The thickness of the active layer is the average annual thaw depth, in permafrost areas, due to solar heating of the surface. This data set includes the mean Remotely Sensed Active Layer Thickness (ReSALT) over years 2006 to 2011 for the region near Barrow, Alaska. The data were produced by an Interferometric Synthetic Aperture Radar (InSAR) technique that measures seasonal surface subsidence and infers ALT. ReSALT estimates were validated by comparison with ground-based ALT obtained using probing and Ground Penetrating Radar at multiple sites. These results indicate remote sensing techniques based on InSAR could be an effective way to measure and monitor ALT over large areas on the Arctic coastal plain. These data provide gridded (30-m) estimates of active layer thickness (cm; ALT) and seasonal subsidence (cm), as well as calculated uncertainty in each of these parameters. This data set was developed in support of NASA's Arctic-Boreal Vulnerability Experiment (ABoVE) field campaign. The data are presented in one netCDF (.nc) file.

Freeze_Thaw_NorthernHemisphere_2323

This dataset provides a probabilistic freeze/thaw (FT) data record from 2016 to 2020 for the Northern Hemisphere derived using a deep learning model (U-Net). The model was informed by satellite multi-frequency microwave brightness temperature retrievals from the NASA SMAP (Soil Moisture Active Passive) and JAXA AMSR2 (Advanced Microwave Scanning Radiometer 2) radiometers, and trained using daily soil temperature observations from Northern Hemisphere weather stations and global reanalysis data (ERA-5). Unlike other available FT data records that provide only a binary classification of frozen or non-frozen conditions, this product includes both binary FT and continuous variable estimates of the probability of thawed conditions. This product is designed to complement other established binary FT data records, including the NASA FT Earth System Data Record and SMAP Level 3 FT operational products, by providing a probabilistic FT variable with enhanced accuracy and sensitivity to near-surface (<=5 cm depth) soil FT condition. The data are provided in cloud optimized GeoTIFF (COG) format.

PermafrostThaw_CarbonEmissions_1872

This dataset consists of an ensemble of model projections from 1901 to 2299 for the northern hemisphere permafrost domain. The model projections include monthly average values for a common set of diagnostic outputs at a spatial resolution of 0.5 x 0.5 degrees latitude and longitude. The model simulations resulted from a synthesis effort organized by the Permafrost Carbon Network to evaluate the impacts of climate change on the carbon cycle in permafrost regions in the high northern latitudes. The model teams used different historical input weather data, but most used driver data developed by the Climate Research Unit - National Centers for Environmental Prediction (CRUNCEP) as modified for the Multiscale Terrestrial Model Intercomparison Project (MsTMIP). The teams scaled the driver data for the projections using output from global climate models from the fifth Coupled Model Intercomparison Project (CMIP5). The synthesis evaluated the terrestrial carbon cycle in the modern era and projected future emissions of carbon under two climate warming scenarios: Representative Concentration Pathways 4.5 and 8.5 (RCP45 and RCP85) from CMIP5. RCP45 represents emissions resulting in a global climate close to the target climate in the Paris Accord. RCP85 represents unconstrained greenhouse gas emissions.

GPP_COS_Conductance_SoilFluxes_2324

This dataset provides outputs from the Simple Biosphere Model (v 4.2). Products include hourly 0.5-degree gridded fluxes of gross primary productivity (GPP), respiration, carbonyl sulfide (COS) uptake by vegetation and soil, along with conductance of COS (apparent mesophyll and total), stomatal conductance of water and partial pressure of CO2 in the canopy air space, leaf surface, interior and chloroplast. The data are separated by plant functional type (PFT). Fluxes have dimensions of latitude, longitude, time, and plant functional type. Model output spans 53N to 90N latitude and 180W to 180E longitude over years 2000 to 2020. The data are provided in NetCDF version 4 format.

Daily_FineParticulateMatter_AK_2157

The dataset provides simulated PM2.5 concentration estimates over Alaska, U.S. PM2.5 (particulate matter with diameter <= 2.5 microns) concentrations in air (micrograms m-3) are gridded at 0.1-degree resolution for May to September for the years 2001 through 2015. The data were created in a modeling process utilizing the Wildland Fire Emissions Inventory System (WFEIS), the Arctic-Boreal Vulnerability Experiment (ABoVE) Wildfire Date of Burning (WDoB) dataset, and multiple models including the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model. The data are provided in GeoTIFF format.

Snow_Depth_Data_Images_1656

This dataset includes data from late-March snow surveys and hourly digital camera images from two study areas within the Wrangell St Elias National Park, Alaska. These data comprise snow density, stratigraphy, and temperature profiles obtained by snow pits; and snow depth data obtained from transects between snow pits. Daily snow depths, adjacent to each pit, were derived from hourly camera images of snow stakes placed adjacent to each pit. These data were collected to constrain and validate a physically-based, spatially-distributed snow evolution model used to simulate snow conditions in Dall sheep habitat. The two study areas are both located within the Jacksina Park Unit (JPU). The first study area, surveyed in 2017, included the northern end of Jaeger Mesa and an area near Rambler mine in the North East of the JPU. The second study area, surveyed in 2018, was within the upper watershed of Pass Creek in the North of the JPU. The remote cameras operated from September 2016 to August 2017 on Jaeger Mesa/Rambler Mine and from September 2017 to July 2018 at Pass Creek.

Snow_Wildlife_Tracks_AK_WA_2188

This dataset contains three field seasons of snow-wildlife observations conducted at 707 sites from January 2021 to March 2023 in Washington and Alaska, spanning a broad range of snow conditions. Relatively fresh tracks (usually <24 h) of common large mammal predators (bobcats, coyotes, cougars, and wolves) and their ungulate prey (caribou, Dall sheep, moose, mule deer, and white-tailed deer) were investigated to determine how snow affects predator-prey interactions. The track sink depth and dimensions (width and length) of three consecutive footprints were measured from one individual. Age class was recorded for moose based either on visual confirmation of an individual creating snow tracks or based on track dimensions. The ability to differentiate age classes for smaller ungulates was more uncertain, so age classes for deer, caribou, or sheep were not specified. Animal gait was identified using a simple classification scheme. Data also include animal species, snow density, hardness, total ice, surface temperature, and vegetation type. To best capture snow hardness, surface penetrability and hand-hardness were measured throughout the snowpack. The data are provided in comma-separated values (CSV) format.

Arctic_Soil_Properties_2149

This dataset provides lab-measured soil properties, including soil water matric potential, soil dielectric properties, soil electrical conductivity, corresponding soil moisture. The dataset also includes the basic soil physical properties such as soil organic matter, bulk density, porosity, fiber content, root biomass, and mineral texture. Soil samples were collected from August 21 to August 27, 2018, from the surface to permafrost table in soil pits at nine sites along the Dalton Highway in northern and central regions of Alaska. Permittivity and soil electrical conductivity measurements were conducted using METER TEROS 12 probes. Soil moisture measurements were made with a TEROS 21 probe. The measurements were conducted in the lab over the span of three years. The purpose of soil collection and lab measurements was to develop an integrated framework that relates the hydrological properties to dielectric properties of permafrost active layer soil in support of the NASA Arctic and Boreal Vulnerability Experiment (ABoVE) Airborne Campaign.

ABoVE_Soil_Respiration_Maps_1935

This dataset provides gridded estimates of carbon dioxide (CO2) emissions from soil respiration occurring within permafrost-affected tundra and boreal ecosystems of Alaska and Northwest Canada at a 300 m spatial resolution for the period 2016-08-18 to 2018-09-12. The estimates include monthly average CO2 flux (gCO2 C m-2 d-1), daily average CO2 flux and error estimates by season (Autumn, Winter, Spring, Summer), estimates of annual offset of CO2 uptake (i.e., vegetation GPP), annual budgets of vegetation gross primary productivity (GPP; gCO2 C m-2 yr-1), and the fraction of open (non-vegetated) water within each 300 m grid cell. Belowground sources of respiration (i.e., root and microbial) are included. The gridded soil CO2 estimates were obtained using seasonal Random Forest models, information from remote sensing, and a new compilation of in-situ soil CO2 flux from Soil Respiration Stations and eddy covariance towers. The flux tower data are provided along with daily gap-filled flux observations for each Soil Respiration station forced diffusion (FD) chamber record. The data cover the NASA ABoVE Domain.

TundraTransect_VegRefl_Soil_2232

This dataset provides visible-near infrared spectral reflectance, descriptions of vegetation cover, surface temperature, the total fraction of absorbed photosynthetically active radiation (fAPAR, 2001 only), permafrost active layer depth, elevation, and soil temperature at 5 cm depth. Measurements were made at every meter along a 100-m transect aligned mainly in an east-west direction, located approximately 300 m southeast of the National Oceanic and Atmospheric Administration (NOAA) Global Monitoring Laboratory (GML) baseline observatory near Utqiagvik, Alaska. Reflectance measurements were collected at nearly weekly intervals through the growing seasons of 2000 to 2002 to describe characteristics of green-up, peak growth, and senescence. Reflectance measurements were also collected once near peak growth in 2022. Ancillary measurements were collected at intervals through the 2001 and 2002 growing seasons.

ArcticTreeLine_Spruce_CO2_WV_1948

This dataset provides in situ measurements of needle-level gas-exchange and leaf traits from Picea glauca (white spruce) from a field site located in the northern latitudinal forest-tundra ecotone (FTE) near the Dalton Highway in northern Alaska, and from one study site located in Black Rock Forest, New York, USA. Measurements were collected with an open flow portable photosynthesis system (Li6400XT) and custom-built temperature-controlled cuvette. Respiration as a function of leaf temperature was measured continuously as the needle temperature was ramped from approximately 5 to 65 degrees C, at a constant rate of 1 degree C per minute. Additional data include tree diameter at breast height (dbh), leaf area, photosynthetic rate, intercellular C02, conductance to H20, tree height, and data from raw temperature curves. Results are reported on both a leaf area and leaf mass basis. The data are for the period 2018-06-06 to 2018-06-23 and are provided in comma-separated (CSV) format.

ABoVE_SAR_Surveys_2150

This dataset contains tables containing Airborne flight metadata from synthetic aperture radar (SAR) surveys from 2012 to 2022 in Alaska and Canada. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) conducted airborne SAR surveys of over 120,000 km2 in Alaska and northwestern Canada during 2017, 2018, 2019, and 2022. Legacy lines acquired between 2012 and 2015 by other projects are included for completeness and to enable longer times series creation. The data files and companion file contain L-band and P-band airborne SAR metadata acquired during the ABoVE airborne campaigns. Included are detailed descriptions of ~80 SAR flight lines and how each fits into the ABoVE experimental design. Extensive maps, tables, and hyperlinks give direct access to every flight plan as well as individual flight lines. This entry is a guide to enable interested readers to fully explore the ABoVE L- and P-band SAR data.

Nongrowing_Season_CO2_Flux_1692

This dataset provides a synthesis of winter ( September-April) in situ soil CO2 flux measurement data from locations across pan-Arctic and Boreal permafrost regions. The in situ data were compiled from 66 published and 21 unpublished studies conducted from 1989-2017. The data sources (publication references) are provided. Sampling sites spanned pan-Arctic Boreal and tundra regions (>53 Deg N) in continuous, discontinuous, and isolated/sporadic permafrost zones. The CO2 flux measurements were aggregated at the monthly level, or seasonally when monthly data were not available, and are reported as the daily average (g C m-2 day-1) over the interval. Soil moisture and temperature data plus environmental and ecological model driver data (e.g., vegetation type and productivity, soil substrate availability) are also included based on gridded satellite remote sensing and reanalysis sources.

Arctic_Boreal_CO2_Flux_1934

This Arctic-Boreal CO2 fluxes (ABCflux) dataset contains monthly aggregates of terrestrial net ecosystem CO2 exchange and its derived partitioned component fluxes: gross primary productivity (GPP) and ecosystem respiration. Over 70 supporting variables describe key site conditions (e.g., vegetation and disturbance type), micrometeorological and environmental measurements (e.g., air and soil temperatures), and flux measurement techniques. The data contained in this ABCflux dataset form a standardized monthly database of Arctic-Boreal CO2 fluxes (i.e., ABCflux Database) and include 244 sites and 6,309 monthly observations; 136 sites and 2,217 monthly observations represent tundra, and 108 sites and 4,092 observations represent the boreal biome. The data are for the period 1989 to 2020.

CubeSat_Arctic_Boreal_LakeArea_1667

This dataset provides near-daily lake area timeseries for 85,358 lakes across four study areas in Northern Canada and Alaska, USA, between May 1 and October 1, 2017. These lake area estimates were produced using digital images from newly developed Planet Labs CubeSats, small satellites with a 4-band (blue, green, red, near-infrared) camera payload. In constellation, CubeSats collected imagery at very high spatial (3-5m) and temporal (near-daily) resolution. From the imagery, each lake's mean, minimum, and maximum areas and seasonal dynamism were derived. The dataset covers four Arctic-Boreal regions: the Yukon Flats Basin (YFB) in eastern interior Alaska, and the Mackenzie River Valley (MRV), Canadian Shield Transect (CST), and Hudson Bay Lowland (HBL) in Canada.

TowerBased_PhotoSpec_SIF_SK_CA_1887

This dataset includes daily averaged solar-induced chlorophyll fluorescence (SIF) in the red (680-686 nm) and far-red (745-758 nm) wavelength ranges, relative SIF (SIF/Intensity), chlorophyll-carotenoid index (CCI), photochemical reflectance index (PRI), near-infrared vegetation index (NIRv), and normalized difference vegetation index (NDVI) for both black spruce (Picea mariana) and larch (Larix laricina) targets. The study site (Southern Old Black Spruce, SOBS Fluxnet ID CA-Obs) is located near the southern limit of the boreal forest ecotone in Saskatchewan, Canada. Data were collected for the spring transition in both 2019 and 2020 using PhotoSpec. Species-specific averages were calculated over each 30-minute period, then averaged again to report daily averages of SIF relative and reflectance measurements for both black spruce and larch.

CircumArctic_Trends_Hotspots_2322

This dataset provides estimates of trends in temperature, moisture, and vegetation changes over the circumpolar Arctic. Time series trends were measured by the Theil-Sen slope and associated p-values for a variety of variables including 2-meter air temperature, precipitation, soil moisture, non-frozen season days, permafrost active layer thickness, snow cover, vapor pressure deficit, land surface water fraction, normalized difference vegetation index (NDVI), and vegetation optical depth. Trends were measured annually and over specific seasons of spring (March to May), summer (June to August), autumn (September to November) and winter (December to February), and for the 1980-2020 and 1997-2020 time periods, depending on the variable and original data availability. Emerging hotspots of change were identified for the same variables and seasons, but only over the 1997-2020 period. In addition, a multivariate ranking was used to create combined hotspot layers to show areas of substantial changes in the thermal environment, moisture, and vegetation; these themes reflect landscape changes considered to be detrimental (e.g., a threat) to ecosystems and human populations. Ancillary files provide the boundaries of study regions, Brown permafrost regions, and a land cover product. The data are provided in cloud optimized GeoTIFF (COG) and shapefile formats.

Tundra_Leaf_Spectra_2005

This dataset provides leaf-level visible-near infrared spectral reflectance, chlorophyll fluorescence spectra, species, plant functional type (PFT), and chlorophyll content of common high latitude plant samples collected near Fairbanks, Utqiagvik, and Toolik, Alaska, U.S., during the summers of 2019, 2020, and 2021. A FluoWat leaf clip was used to measure leaf-level visible-near infrared spectral reflectance and chlorophyll fluorescence spectra. Fluorescence yield (Fyield) was calculated as the ratio of the emitted fluorescence divided by the absorbed radiation for the wavelengths from 400 nm up to the wavelength of the cut off for the FluoWat low pass filter (either 650 or 700 nm). Chlorophyll content of samples was measured using a CCM-300 Chlorophyll Content. The data are provided in comma-separated values (.csv) format.

TundraVeg_Reflectance_Soil_CO2_1960

This dataset provides measurements at tundra plots collected near Utqiagvik and Atqasuk, AK, including visible-near infrared spectral reflectance, chamber gas exchange measurements of CO2, pulse amplitude modulated (PAM) fluorometry, chlorophyll pigment contents, along with surface temperature, permafrost active layer depth, and soil temperature at 5 cm, through the growing seasons of 2001 and 2002. At all plots, spectral reflectance was measured using a portable spectrometer configured with a straight fiber optic foreoptic, surface temperatures were measured using a handheld Everest Infrared Thermometer, and thaw depth (or active layer depth) was measured using a metal rod graduated in centimeter intervals. At small plots (~15 cm) at Utqiagvik (referred to as Patch plots) chambers were constructed that enclosed an individual patch to determine photosynthetic rate and estimate respiration rate (made by covering the chamber in a dark cloth). Efficiency using PAM fluorometer, ambient yield estimations, and rapid light curve measurements were taken. Chlorophyll concentration was measured with a portable spectrometer configured as a spectrophotometer. At larger plots (approximately 1 m2) which were part of the International Tundra EXperiment (ITEX plots) at Utqiagvik (referred to as Barrow) and Atqasuk, a sub-sample of five control and five warmed plots at each site were fitted with 0.45 m diameter polyvinyl chloride collars for chamber flux measurements. To determine the total fraction of absorbed photosynthetically active radiation (fAPAR), a series of photosynthetically active radiation (PAR) measurements were made using a custom-made light bar consisting of a linear array of GaAsP sensors mounted within an aluminum U-bar under a white plastic diffuser. In addition, a visual estimate was made of the fraction of standing dead vegetation based on percent cover. The data are provided in comma-separated values (.csv) format. In addition, photographs of plots and instruments are provided.

UAVSAR_Wetland_Peace-Athabasca_2442

This dataset holds UAVSAR imagery products for the Peace-Athabasca Delta region of Canada. UAVSAR data were collected for two flight lines on five dates from 2017 to 2022 and processed to NISAR-like products with spatial resolution of approximately 10 m. Products include double bounce, volume, odd, and helical scatter components. In addition, radiometrically terrain corrected HH backscatter, HV backscatter, and the ratio of HH to HV, as well as the local incidence angle in radians and a valid data mask are provided. These data can be used to estimate the extent of inundation of wetlands in this region. The data are provided in cloud optimized GeoTIFF format.

Understory_Veg_Biomass_Alaska_2340

This dataset provides measurements of vegetation biomass from 11 locations across Alaska during 2016 to 2018. Vegetation was harvested from plots that were located at the end of previously established 30-m transects at each site, except at one site where plots were randomly selected. Vascular vegetation was clipped from 50 cm x 50 cm plots, and non-vascular vegetation was clipped from 25 cm x 25 cm plots. All harvested vegetation was sorted by functional group or by species where identification was possible. The sorted vegetation was dried and then weighed to determine biomass. Locations were selected to investigate fire disturbance, to span the range of permafrost regions from continuous to sporadic, and to cover vegetation types from boreal forests, tussock tundra, upland willow/herbaceous scrub, and lowland fen and wet tundra sites across Alaska. The data are provided in comma separated values (CSV) format.

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NASA ACCP Project

An inventory of NASA's airborne and field campaigns for Earth Science

plotchem_420

The purpose of these measurements was to determine plot-level average leaf concentrations of nitrogen, lignin, cellulose, etc. in order to investigate how AVIRIS reflectance measurements vary with chemistry. The plot-level leaf chemistry values were calculated from green leaf chemistry values and litterfall sample weights.

leafchem_421

As part of NASA's Accelerated Canopy Chemistry Program (ACCP) analyses were performed for the determination of carbon constituents and nitrogen content in fresh forest foliage. Samples were analyzed using a series of extraction's that yielded different carbon constituents: non-polar, polar, cellulose and lignin. Nitrogen analyses were conducted using a standard combustion procedure. Approximately 1000 leaf samples were collected from 5 geographically distinct sites and were analyzed at the University of New Hampshire to ensure consistency in analysis. Results were used as a calibration set for Visible/NIR reflectance and the estimation of canopy carbon and nitrogen concentrations.

canopychem_422

This data set describes the nitrogen and chlorophyll content of small, monospecific canopies formed from seedlings of Douglas-fir (Pseudotsuga menziesii) and bigleaf maple (Acer macrophyllum). The trees were provided different levels of fertilization in order to produce canopies with varying nitrogen and chlorophyll concentration. For the Douglas-fir, fertilization was provided during the dormant season, so there were no differences in growth or leaf area among canopies, and canopies were at a constant density with varying foliar chemistry. For the maple, seedlings were aggregated at various densities, producing a matrix of leaf area as well as chemistry variations. Before destructive analysis for foliar chemistry, canopy reflectance was measured under natural sunlight (see ACCP Seedling Canopy Reflectance Spectra Data).

canopyspec_423

This is a data set of spectral reflectance (400-2500 nm) of small, monospecific canopies formed from seedlings of Douglas-fir (Pseudotsuga menziesii) and bigleaf maple (Acer macrophyllum). The trees were provided different levels of fertilization in order to produce canopies with varying nitrogen and chlorophyll concentration. For the Douglas-fir, fertilization was provided during the dormant season, so there were no differences in growth or leaf area among canopies, and canopies were at a constant density with varying foliar chemistry. For the maple, seedlings were aggregated at various densities, producing a matrix of leaf area as well as chemistry variations. Canopy reflectance was measured under natural sunlight, and canopies were then destructively analyzed for chemical content and leaf area (see ACCP Seedling Canopy Chemistry Data).

plotspec_544

AVIRIS image scenes were acquired in 1992 over ACCP sites. Pixels that coincided with field study plots were extracted and reflectance values were correlated with estimated canopy carbon and nitrogen content.

leafspec_424

The leaf spectra datasets contain visible and near infrared reflectance spectra data for both fresh and dry leaf samples collected in the ACCP. These samples are from Blackhawk Island, WI, Harvard Forest, MA, Howland, ME, Jasper Ridge, CA field sites and the Douglas fir and bigleaf maple seedling canopy study sites. Data reported for each sample is absorbance [log(1/Reflectance)] from 400-2498nm at 2nm intervals and a resolution of 10nm. These data were collected for the purpose of determining the relationship of foliar chemical concentrations with visible and near infrared wavelength reflectance spectra.. Both multiple linear regression and partial least square regression techniques have been used to relate lab chemistry data to spectral reflectance. ORNL DAAC maintains information on the entire ACCP.

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NASA ACRIM III Project

Launch and mission info for NASA's AcrimSat Earth satellite, which for 14 years monitored solar radiation and its effects on Earth's atmosphere and climate change.

ACR3L2DM

ACR3L2DM_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Daily Mean Data version 1 product consists of Level 2 total solar irradiance in the form of daily means gathered by the ACRIM III instrument on the ACRIMSAT satellite. The daily means are constructed from the shutter cycle results for each day.

ACR3L2SC

ACR3L2SC_1 is the Active Cavity Radiometer Irradiance Monitor (ACRIM) III Level 2 Shutter Cycle Data version 1 product contains Level 2 total solar irradiance in the form of shutter cycles gathered by the ACRIM instrument on the ACRIMSAT satellite.

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NASA ACT-America Project

The ACT-America Campaign Catalog provides information about the airborne campaigns of the Atmospheric Carbon and Transport (ACT-America) project. ACT-America advanced atmospheric greenhouse gas inversions to a high level of accuracy and precision through new methods and models that improved knowledge of atmospheric transport, prior flux models, and space-based observations. The catalog compiles flight details for the five campaigns conducted during Summer 2016, Winter 2017, Fall 2017, Spring 2018, and Summer 2019 (2016-05-27 to 2019-07-26) across three regions of the eastern and central United States. Data include flight dates, regions, objectives, weather conditions, instrument status, aircraft flight paths, detailed weather reports, and measurement summary figures. A total of 121 research flights were conducted within the five six-week seasonal campaigns by each of the two instrumented aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Wallops Flight Facility's C-130 Hercules. During 1,140 flight hours remote and in situ sensors onboard the two research aircraft measured greenhouse gas mole fractions, trace gases, and thermodynamic variables across a variety of continental surfaces and atmospheric conditions to study the transport and fluxes of atmospheric carbon dioxide and methane. As noted in the Flight_patterns_staus field, there were flights when both aircraft flew directly under Orbiting Carbon Observatory-2 (OCO-2) overpasses to evaluate the ability of OCO-2 to observe high-resolution atmospheric CO2 variations. The C-130 aircraft was also equipped with active remote sensing instruments for planetary boundary layer height detection and column greenhouse gas measurements. The data are provided in comma separated values (CSV), compressed Keyhole Markup Language (KMZ) formats along with figures as JPEG and Portable Network Graphics images.

CPL_ABL_Top_Height_1825

This dataset consists of the atmospheric boundary layer (ABL) top heights and the altitudes of the two additional aerosol layers (in km above mean sea level) derived from Cloud Physics Lidar (CPL) measurements using the Haar wavelet transform method. The CPL instrument was deployed onboard NASA's C-130 aircraft to obtain aerosol backscatter profiles during four ACT-America field campaigns (Summer 2016, Winter 2017, Fall 2017, and Spring 2018). CPL is a backscatter lidar designed to operate simultaneously at three wavelengths. The profiles were collected at 4-second temporal and 30 m vertical resolutions. The time resolution of the provided CPL-derived ABL top heights and other aerosol layers are 8 seconds.

ACT_CASA_Ensemble_Prior_Fluxes_1675

This data set provides gridded, model-derived gross primary productivity (GPP), ecosystem respiration (RECO), and net ecosystem exchange (NEE) of CO2 biogenic fluxes and their uncertainties at monthly and 3-hourly time scales over 2003-2019 on a 463-m spatial resolution grid for the conterminous United States (CONUS) and on both 5-km and half-degree spatial resolution grids for North America (NA). The biogeochemical model Carnegie Ames Stanford Approach (CASA) was used.

HALO_LiDAR_AOP_ML_Heights_1833

This dataset provides measurements from the High Altitude Lidar Observatory (HALO) instrument, an airborne multi-function Differential Absorption Lidar (DIAL) and High Spectral Resolution Lidar (HSRL), operating at 532 nm and 1064 nm wavelengths onboard a C-130 aircraft during the June and July 2019 ACT-America campaign. The flights took place over eastern and central North America based from Shreveport, Louisiana; Lincoln, Nebraska; and NASA Wallops Flight Facility located on the eastern shore of Virginia. HALO data were sampled at 0.5 s temporal and 1.25 m vertical resolutions. The data include profiles of aerosol optical properties (AOP), distributions of mixed layer heights (MLH), columns of tropospheric methane, and navigation parameters. The data are provided in HDF5 format along with PNG images and a companion files in Portable Document (.pdf) format.

ACTAMERICA_MFLL_L1_1817

This dataset provides Level 1 (L1) remotely sensed differential absorption optical depth (DAOD) measurements made through the Multi-Functional Fiber Laser Lidar (MFLL; Harris Corporation) during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the United States for the Atmospheric Carbon and Transport (ACT-America) project. DAOD were measured at 0.1 second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with MFLL. The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1 second column CO2 reporting frequency, are included, but not limited to, latitude, longitude, altitude, and attitude. Data users should note that a Level 2 (L2) MFLL data product is available (related dataset) that contains all data variables (plus the column-average CO2) included in this L1 MFLL data product but has undergone additional processing and calibrations and is recommended for most use cases.

ACTAMERICA_Hskping_1574

This dataset provides aircraft navigational parameters and related meteorological data (often referred to as "housekeeping" data) in support of the research activities for the two aircrafts that flew for the NASA Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spans five years and includes five 6-week intensive field campaigns covering all 4 seasons and 3 regions of the central and eastern United States. Two instrumented aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. During these flights, aircraft positional, meteorological, and environmental data are recorded by a variety of instruments. For this dataset, measurements include, but are not limited to: latitude, longitude, altitude, ground speed, air temperature, and wind speed and direction. These data are incorporated into related ACT-America flight-instrumented datasets to provide geotrajectory file information for position, attitude, and altitude awareness of instrumented sampling.

Insitu_Tower_Greenhouse_Gas_1798

This dataset provides Level 1 (L1) in situ atmospheric carbon dioxide (CO2), carbon monoxide (CO), and methane (CH4) concentrations as measured on a network of instrumented communications towers across the central and eastern USA operated by the Atmospheric Carbon and Transport-America (ACT-America) project. There were 11 towers instrumented with cavity ring-down spectrometers (CRDS; Picarro Inc.) with measurements beginning in January 2015 and continuing to October 2019. The measurement period varied by tower site. The Picarro analyzers continuously measured total CH4, isotopic ratio of CH4, CO2, CO, and other greenhouse gas concentrations. Not all species were measured at all sites. Complete tower location, elevation, instrument height, and date/time information are also provided. Determination of greenhouse gas fluxes and uncertainty bounds is essential for the evaluation of the effectiveness of mitigation strategies. These L1 data are raw instrument outputs from the Picarro instruments. A Level 2 (L2) product derived from this L1 data is available and generally would be the preferred data for most use cases.

ACTAMERICA_PICARRO_1556

This dataset provides atmospheric carbon dioxide (CO2), carbon monoxide (CO), methane (CH4), water vapor (H2O), and ozone (O3) concentrations collected during airborne campaigns conducted by the Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spanned 4 years and included five 6-week airborne campaigns covering all 4 seasons and 3 regions of the central and eastern United States. This dataset provides results from all five campaigns, including Summer 2016, Winter 2017, Fall 2017, Spring 2018, and Summer 2019. Two instrumented aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. CO2, CO, CH4, and H2O were collected with an infrared cavity ring-down spectrometer system (CRDS; Picarro Inc.). Ozone data were collected with a dual beam differential UV absorption ozone monitor (Model 205; 2B Technologies). Both aircraft hosted identical arrays of in situ sensors. Complete aircraft flight information including, but not limited to, latitude, longitude, altitude, and meteorological conditions are also provided.

ACTAMERICA_PFP_1575

This dataset provides atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), molecular hydrogen (H2), nitrous oxide (N2O), sulfur hexafluoride (SF6), and other trace gas mole fractions (i.e., concentrations) from airborne campaigns over North America for the NASA Atmospheric Carbon and Transport - America (ACT-America) project. ACT-America's mission spanned five years and included five six-week field campaigns covering all four seasons and three regions of the central and eastern United States. Two instrumented aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. The data were derived from laboratory measurements of whole air samples collected by Programmable Flask Packages (PFP) onboard the two ACT-America aircraft. Approximately 10 - 12 discrete flask samples were captured during each of the 195 flights. This dataset provides results from all five campaigns, including Summer 2016, Winter 2017, Fall 2017, Spring 2018, and Summer 2019.

ACTAMERICA-PICARRO_Ground_1568

This dataset provides atmospheric carbon dioxide (CO2), carbon monoxide (CO), and methane (CH4) concentrations as measured on a network of instrumented communications towers operated by the Atmospheric Carbon and Transport-America (ACT-America) project. ACT-America's mission spans five years and includes five 6-week intensive field campaigns covering all 4 seasons and 3 regions of the central and eastern United States. Tower-based measurements began in early 2015 and are continuously collecting CO2, CO, and CH4 data to characterize ground-level (>100 m) carbon background conditions to support the periodic airborne measurement campaigns and transport modeling conducted by ACT-America. The towers are instrumented with infrared cavity ring-down spectrometer systems (CRDS; Picarro Inc.). Data are reported for the highest sampling port on each tower. The averaging interval standard deviation and uncertainty derived from periodic flask sample to in-situ measurement comparisons are provided. Complete tower location, elevation, instrument height, and date/time information are also provided.

ACTAMERICA_MFFLL_1649

This dataset provides Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the United States for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at 0.1 second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1 second column CO2 reporting frequency, are included, but not limited to, latitude, longitude, altitude, and attitude. Processing for this Level 2 (L2) product included additional processing and calibration procedures described in this document as applied to retrieval of column CO2 from L1 MFLL data. Data users should use this L2 data unless different CO2 retrieval criteria are preferred.

MFLL_XCO2_Range_10Hz_1892

This dataset provides a direct subset (i.e., the Lite version) of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. Complete aircraft flight information, interpolated to the 0.1-second column CO2 reporting frequency, is included, but not limited to, latitude, longitude, altitude, and attitude.

MFLL_CO2_Weighting_Functions_1891

This dataset provides vertical weighting function coefficients of the Level 2 (L2) remotely sensed column-average carbon dioxide (CO2) concentrations measured during airborne campaigns in Summer 2016, Winter 2017, Fall 2017, and Spring 2018 conducted over central and eastern regions of the U.S. for the Atmospheric Carbon and Transport (ACT-America) project. Column-average CO2 concentrations were measured at a 0.1-second frequency during flights of the C-130 Hercules aircraft at altitudes up to 8 km with a Multi-functional Fiber Laser Lidar (MFLL; Harris Corporation). The MFLL is a set of Continuous-Wave (CW) lidar instruments consisting of an intensity-modulated multi-frequency single-beam synchronous-detection Laser Absorption Spectrometer (LAS) operating at 1571 nm for measuring the column amount of CO2 number density and range between the aircraft and the surface or to cloud tops, and surface reflectance and a Pseudo-random Noise (PN) altimeter at 1596 nm for measuring the path length from the aircraft to the scattering surface and/or cloud tops. The MFLL was onboard all ACT-America seasonal campaigns, except Summer 2019. The MFLL-measured column-averaged CO2 values have certain distinct vertical weights on CO2 profiles depending on the meteorological conditions and the wavelengths used at the measurement time and location. This product includes the instrument location at the time of measurement in geographic coordinates and altitude, along with a vector of weighting function values representing conditions along the nadir direction.

ACTAMERICA_Merge_1593

This dataset provides merged data products acquired during flights over the central and eastern United States as part of the Atmospheric Carbon and Transport - America (ACT-America) project. Two aircraft platforms, the NASA Langley Beechcraft B200 King Air and the NASA Goddard Space Flight Center's C-130H Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions. The merged data products are composed of continuous in situ measurements of atmospheric carbon dioxide (CO2), methane (CH4), carbon monoxide (CO), ozone (O3), and ethane (C2H6, B200 aircraft only) that were averaged to uniform intervals and merged with aircraft navigation and meteorological variables as well as trace gas concentrations from discrete flask samples collected with the Programmable Flask Package (PFP). These merged data products provide integrated measurements at intervals useful to the modeling community for studying the transport and fluxes of atmospheric carbon dioxide and methane across North America.

Profile_based_PBL_heights_1706

This dataset provides profile-based estimates of the height to the top of the planetary boundary layer (PBL), also known as the atmospheric boundary layer (ABL), in meters above mean sea level estimated from meteorological measurements acquired during ascending or descending vertical profile flight segments during NASA's Atmospheric Carbon and Transport - America (ACT-America) airborne campaign. ACT-America flights sampled the atmosphere over the central and eastern United States seasonally from 2016 - 2019. Two aircraft platforms, the NASA Langley Beechcraft B-200 King Air and the NASA Goddard Space Flight Center's C-130 Hercules, were used to collect high-quality in situ measurements across a variety of continental surfaces and atmospheric conditions.

ACTAMERICA_WRF_Chem_Output_1884

This dataset includes hourly output from the WRF-Chem simulation model for North America at a resolution of 27 km for 2016-06-29 through 2019-07-31. WRF-Chem is the Weather Research and Forecasting (WRF) model coupled with Chemistry. The output provides baseline conditions for comparison to data from ACT-America airborne campaigns conducted to study atmospheric CO2 and CH4 from 2016 to 2019. The WRF-Chem (v. 3.6.1) model was driven by meteorological conditions and sea-surface temperatures. The output includes 50 vertical layers up to atmospheric pressure of 50 hPa with 20 levels in the lowest 1 km. It provides information for understanding the fluxes and atmospheric transport of carbon dioxide (CO2), methane (CH4), and ethane (C2H6).

FLEXPART_Influence_Functions_2018

This dataset contains a set of Lagrangian particle dispersion simulations of carbon dioxide concentrations using the FLEXible PARTicle (FLEXPART) model. FLEXPART quantified the source-receptor relationships, so-called "influence functions", in a backward mode. The simulations were constructed for five Atmospheric Carbon and Transport America (ACT-America) deployments over the eastern U.S. that occurred in 2016-2019. Each receptor of the influence function is the 30-second or 10-minute interval along flight tracks, characterized by a box with boundaries between the maximum and minimum latitude/longitude as well as between the maximum and minimum altitudes during the interval. Each receptor box released 5,000 particles and simulated their transport and dispersion backward for 10 or 20 days. The simulations were driven by 27-km meteorology provided by the WRF-Chem simulation or by ERA-Interim data from the European Centre for Medium-Range Weather Forecasts (ECMWF). Background levels of carbon dioxide were obtained from CarbonTracker and OCO-2 v9 MIP. The data are provided in netCDF and FLEXPART binary formats.

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NASA AEHYP Project

The Airborne Hyperspectral Reflectance Indian Cave Nebraska Multi-Day (AEHYPICNE1M) data are from the Indian Cave Forest Global Earth Observatory (ForestGeo) plot in Indian Cave State Park in southeastern Nebraska. The data have a spatial resolution of 1 meter (m) and fall in the spectral range of 400-1000 nanometers (nm). The data can be used by researchers in developing new capabilities for the remote sensing of forest diversity and function, as well as a global biodiversity monitoring system. The Indian Cave ForestGeo plot airborne data were collected on September 6, 2019, and August 4, 2022. The images were collected by the Nebraska Earth Observatory (NEO), using an AisaKESTREL (Specim, Oulu, Finland) hyperspectral pushbroom sensor mounted on a Piper Saratoga fixed-wing aircraft. The AEHYPICNE1M product provides images with 178 hyperspectral bands that have been radiometrically, geometrically, and atmospherically corrected.

AEHYP1TPPOK

The Airborne Hyperspectral Reflectance data are from The Nature Conservancy’s Tallgrass Prairie Preserve in northeastern Oklahoma. The data have a spatial resolution of 1 meter (m) and fall in the spectral range of 400-2450 nanometers (nm). These data can be used to develop approaches for studying grassland biodiversity. The Nature Conservancy’s Tallgrass Prairie Preserve airborne data were collected on three separate occasions: August 3, 2020, July 31, 2021, and July 23, 2022. The images were collected by SpecTIR Remote Sensing (SRS) Division, contracted by Oklahoma State University, using an AisaFENIX 1K (Specim, Oulu, Finland) pushbroom sensor mounted on a Twin Commander 500 fixed wing aircraft. The AEHYP1TPPOK product provides orthorectified individual flight lines of 323 hyperspectral bands that have been radiometrically, geometrically, and atmospherically corrected.

AEHYPCCMN300MM

The Airborne Hyperspectral Reflectance image mosaic is of the Long-Term Ecological Research (LTER) site at Cedar Creek Ecosystem Science Reserve (CCESR), Minnesota (AEHYPCCMN300MM). The image is at approximate 1 meter and resampled to 300 millimeter (0.3 meter) spatial resolution during processing in the 400 to 970 nanometer (nm) spectral range. This image can be used to understand the optical diversity-biodiversity relationship and investigate the spatial sensitivity of the relationship at local scales. Airborne data for the LTER Cedar Creek site were collected on August 2, 2014, using an AisaEAGLE airborne hyperspectral system mounted on a fixed-wing Piper Saratoga aircraft. The images were collected from a height of 1,540 m and spectral binning (approximately 10 nm) increased the signal-to-noise ratio of the data. Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) software was used for atmospheric correction in converting radiance to reflectance. Airborne data covered all the prairie grassland plots in the biodiversity (BioDIV) experiment and the Biodiversity and Climate experiment. The data were mosaicked as one image. Provided in the AEHYPCCMN300MM product are 64 hyperspectral bands in an Environment for Visualizing Images (ENVI) format.

AEHYPWRNE1M

The Airborne Hyperspectral Reflectance image mosaic is of the long-term prairie restoration and biodiversity study site led by the Nature Conservancy near Wood River, Nebraska (AEHYPWRNE1M). The approximately 1-meter (m) spatial resolution image was resampled in the 397 to 1004 nanometer (nm) spectral range. This image can be used to detect grassland biodiversity over time to help develop a regional biodiversity monitoring program. Airborne data for the long-term prairie restoration and biodiversity Wood River site were collected on four separate occasions: August 23, 2017; June 27, 2018; August 24, 2018; October 16, 2018. The images were collected using an AisaKESTREL compact hyperspectral pushbroom sensor. Provided in the AEHYPWRNE1M product are 178 hyperspectral bands that have been radiometrically, geometrically, and atmospherically corrected.

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Data Access

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NASA AQUARIUS SAC-D Project

The version 5.0 Aquarius CAP Level 2 product contains the fourth release of the AQUARIUS/SAC-D orbital/swath data based on the Combined Active Passive (CAP) algorithm. CAP is a P.I. produced dataset developed and provided by JPL. This Level 2 dataset contains sea surface salinity (SSS), wind speed and wind direction data derived from 3 different radiometers and the onboard scatterometer. The CAP algorithm simultaneously retrieves the salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. Each L2 data file covers one 98 minute orbit. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_RAINCORRECTED_CAP_7DAY_V5

Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. CAP Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the 7-Day running mean sea surface salinity (SSS) rain corrected V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_CAP_7DAY_V5

Version 5.0 Aquarius CAP Level 3 products are the fourth release of the AQUARIUS/SAC-D mapped salinity and wind speed data based on the Combined Active Passive (CAP) algorithm. Level 3 standard mapped image products contain gridded 1 degree spatial resolution salinity and wind speed data averaged over 7 day and monthly time scales. This particular dataset is the 7-Day running mean sea surface salinity (SSS) V5.0 Aquarius CAP product. CAP is a P.I. produced dataset developed and provided by JPL. The CAP algorithm utilizes data from both the onboard radiometer and scatterometer to simultaneously retrieve salinity, wind speed and direction by minimizing the sum of squared differences between model and observations. The main improvements in CAP V5.0 relative to the previous version include: updates to the Geophysical Model Functions to 4th order harmonics with the inclusion of sea surface temperature (SST) and stability at air-sea interface effects; use of the Canadian Meteorological Center (CMC) SST product as the new source ancillary sea surface temperature data in place of NOAA OI SST. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_ANCILLARY_CELESTIALSKY_V1

This datasets contains three maps of L-band (wavelength = 21 cm) brightness temperature of the celestial sky ("Galaxy") used in the processing of the NASA Aquarius instrument data. The maps report Sky brightness temperatures in Kelvin gridded on the Earth Centered Inertial (ECI) reference frame epoch J2000. They are sampled over 721 Declinations between -90 degrees and +90 degrees and 1441 Right Ascensions between 0 degrees and 360 degrees, all evenly spaced at 0.25 degrees intervals. The brightness temperatures are assumed temporally invariant and polarization has been neglected. They include microwave continuum and atomic hydrogen line (HI) emissions. The maps differ only in how the strong radio source Cassiopeia A has been included into the whole sky background surveys: 1/ TB_no_Cas_A does not include Cassiopeia A and reports only the whole Sky surveys. 2/ TB_Cas_A_1cell spread Cas A total flux homogeneously over 1 map grid cell (i.e. 9.8572E-6 sr). 3/ TB_Cas_A_beam spreads Cas A over surrounding grid cells using a convolution by a Gaussian beam with HPBW of 35 arcmin (equivalent to the instrument used for the Sky surveys). Cassiopeia A is a supernova remnant (SNR) in the constellation Cassiopeia and the brightest extra-solar radio source in the sky at frequencies above 1.

AQUARIUS_L2_SSS_V5

The version 5.0 Aquarius Level 2 product is the official third release of the orbital/swath data from AQUARIUS/SAC-D mission. The Aquarius Level 2 data set contains sea surface salinity (SSS) and wind speed data derived from 3 different radiometers and the onboard scatterometer. Included also in the Level 2 data are the horizontal and vertical brightness temperatures (TH and TV) for each radiometer, ancillary data, flags, converted telemetry and navigation data. Each data file covers one 98 minute orbit. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath. Enhancements to the version 5.0 Level 2 data relative to v4.0 include: improvement of the salinity retrieval geophysical model for SST bias, estimates of SSS uncertainties (systematic and random components), and inclusion of a new spiciness variable.

AQUARIUS_L3_ANCILLARY_SST_SMI_28DAY-RUNNINGMEAN_V5

Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, 7-Day, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the 28-Day running mean, ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.

AQUARIUS_L3_ANCILLARY_SST_SMI_7DAY-RUNNINGMEAN_V5

Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, seasonal, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the 7-Day running mean ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.

AQUARIUS_L3_ANCILLARY_SST_SMI_ANNUAL_V5

Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, Annual, and annual time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the Annual ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.

AQUARIUS_L3_ANCILLARY_SST_SMIA_DAILY_V5

Aquarius Level 3 ancillary sea surface temperature (SST) standard mapped image data are the ancillary SST data used in the Aquarius calibration for salinity retrieval. They are simply the daily SSTs from the Reynolds National Climatic Data Center (NCDC) 0.25 degree dataset, gridded and averaged using the Aquarius processing L2-L3 processing scheme to the same 1 degree spatial resolution and daily, 7 day, monthly, Daily, and Daily time intervals as Aquarius L3 standard salinity and wind speed products. This particular data set is the Daily, ascending ancillary sea surface temperature product associated with version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission.

AQUARIUS_L3_SSS-RainFlagged_SM_SMIA_MONTHLY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending rain-flagged sea surface salinity smoothed product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SM_SMID_MONTHLY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending rain-flagged sea surface salinity smoothed product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SM_SMI_MONTHLY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly rain-flagged sea surface salinity smoothed product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMI_28DAY-RUNNINGMEAN_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMI_7DAY-RUNNINGMEAN_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day running mean rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMI_ANNUAL_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMIA_28DAY-RUNNINGMEAN_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMIA_7DAY_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMIA_ANNUAL_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMIA_DAILY_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMIA_CUMULATIVE_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission cummulative, Ascending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY-CLIMATOLOGY_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMIA_MONTHLY_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMIA_SEASONAL-CLIMATOLOGY_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMIA_3MONTH_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Ascending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMI_DAILY_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMID_28DAY-RUNNINGMEAN_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Descending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMID_7DAY_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Descending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMID_ANNUAL_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMID_DAILY_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMID_CUMULATIVE_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission cummulative, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY-CLIMATOLOGY_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onbard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMID_MONTHLY_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMID_SEASONAL-CLIMATOLOGY_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Descending rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMID_3MONTH_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Descending rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMI_CUMULATIVE_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission cummulative rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY-CLIMATOLOGY_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMI_MONTHLY_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius dataset. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMI_SEASONAL-CLIMATOLOGY_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS-RainFlagged_SMI_3MONTH_V5

Aquarius Level 3 sea surface salinity (SSS) rain-flagged standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal rain-flagged rain-flagged sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMI_28DAY-RUNNINGMEAN_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMI_7DAY_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMI_7DAY-RUNNINGMEAN_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day running mean, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMI_ANNUAL_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMIA_28DAY-RUNNINGMEAN_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Ascending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMIA_7DAY_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Ascending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMIA_ANNUAL_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Ascending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMIA_DAILY_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Ascending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMIA_CUMULATIVE_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Ascending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMIA_MONTHLY-CLIMATOLOGY_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Ascending sea surface density product for version 5.0. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMIA_MONTHLY_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMIA_SEASONAL-CLIMATOLOGY_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Ascending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMIA_3MONTH_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Ascending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMI_DAILY_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMID_28DAY-RUNNINGMEAN_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Descending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMID_7DAY_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Descending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMID_ANNUAL_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Descending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMID_DAILY_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Descending sea surface density product for version 5.0 of the Aquarius data set. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMID_CUMULATIVE_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Descending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMID_MONTHLY-CLIMATOLOGY_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Descending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onbard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMID_MONTHLY_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMID_SEASONAL-CLIMATOLOGY_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Descending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMID_3MONTH_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution density data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Descending sea surface density product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMI_CUMULATIVE_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, sea surface density product for version 5.0 of the Aquarius data set. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMI_MONTHLY-CLIMATOLOGY_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMI_MONTHLY_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMI_SEASONAL-CLIMATOLOGY_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_DENSITY_SMI_3MONTH_V5

Aquarius Level 3 sea surface density standard mapped image data contains gridded 1 degree spatial resolution derived density averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, sea surface density product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Surface density estimates are based on TEOS-10 and derived using retrieved salinity from Aquarius and collocated ancillary SST (Reynolds OI 0.25 degree product). The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SM_SMIA_MONTHLY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending sea surface salinity smoothed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SM_SMID_MONTHLY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending sea surface salinity smoothed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SM_SMI_MONTHLY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly sea surface salinity smoothed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMI_28DAY-RUNNINGMEAN_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMI_7DAY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMI_7DAY-RUNNINGMEAN_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day running mean sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMI_ANNUAL_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMIA_28DAY-RUNNINGMEAN_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMIA_7DAY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMIA_ANNUAL_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMIA_DAILY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMIA_CUMULATIVE_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Ascending sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMIA_MONTHLY-CLIMATOLOGY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMIA_MONTHLY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMIA_SEASONAL-CLIMATOLOGY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMIA_3MONTH_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Ascending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMI_DAILY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMID_28DAY-RUNNINGMEAN_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMID_7DAY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMID_ANNUAL_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMID_DAILY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMID_CUMULATIVE_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Descending sea surface salinity product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMID_MONTHLY-CLIMATOLOGY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMID_MONTHLY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMID_SEASONAL-CLIMATOLOGY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMID_3MONTH_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Descending sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMI_CUMULATIVE_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative sea surface salinity product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMI_MONTHLY-CLIMATOLOGY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMI_MONTHLY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMI_SEASONAL-CLIMATOLOGY_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SSS_SMI_3MONTH_V5

Aquarius Level 3 sea surface salinity (SSS) standard mapped image data contains gridded 1 degree spatial resolution SSS averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal sea surface salinity product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMI_28DAY-RUNNINGMEAN_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, sea surface spiciness product for version 5.0 of the Aquarius data set. The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMI_7DAY_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMI_7DAY-RUNNINGMEAN_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day running mean sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMI_ANNUAL_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMIA_28DAY-RUNNINGMEAN_V5

Aquarius Level 3 sea surface salinity spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMIA_7DAY_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMIA_ANNUAL_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMIA_DAILY_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMIA_CUMULATIVE_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMIA_MONTHLY-CLIMATOLOGY_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Ascending sea surface spiciness product for version 5.0. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMIA_MONTHLY_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMIA_SEASONAL-CLIMATOLOGY_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMIA_3MONTH_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Ascending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMI_DAILY_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMID_28DAY-RUNNINGMEAN_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMID_7DAY_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMID_ANNUAL_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMID_DAILY_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMID_CUMULATIVE_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMID_MONTHLY-CLIMATOLOGY_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onbard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMID_MONTHLY_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMID_SEASONAL-CLIMATOLOGY_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMID_3MONTH_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Descending sea surface spiciness product for version 5.0 of the Aquarius data set. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMI_CUMULATIVE_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMI_MONTHLY-CLIMATOLOGY_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMI_MONTHLY_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly sea surface spiciness product for version 5.0 of the Aquarius dataset. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMI_SEASONAL-CLIMATOLOGY_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_SPICINESS_SMI_3MONTH_V5

Aquarius Level 3 sea surface spiciness standard mapped image data contains gridded 1 degree spatial resolution spice data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal sea surface spiciness product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMI_28DAY-RUNNINGMEAN_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMI_7DAY_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMI_7DAY-RUNNINGMEAN_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonaltime scales. This particular data set is the 7-Day running mean wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMI_ANNUAL_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMIA_28DAY-RUNNINGMEAN_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMIA_7DAY_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMIA_ANNUAL_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMIA_DAILY_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMIA_CUMULATIVE_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Ascending wind speed product for version 5.0 of the Aquarius data set. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY-CLIMATOLOGY_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMIA_MONTHLY_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMIA_SEASONAL-CLIMATOLOGY_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMIA_3MONTH_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Ascending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Ascending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMI_DAILY_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMID_28DAY-RUNNINGMEAN_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 28-Day running mean, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMID_7DAY_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the 7-Day, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMID_ANNUAL_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Annual, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMID_DAILY_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Daily, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMID_CUMULATIVE_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative, Descending wind speed product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY-CLIMATOLOGY_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMID_MONTHLY_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMID_SEASONAL-CLIMATOLOGY_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMID_3MONTH_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal, Descending wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. Only retrieved values for Descending passes have been used to create this product. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMI_CUMULATIVE_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the mission series mean or cumulative wind speed product for version 5.0 of the Aquarius data set. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY-CLIMATOLOGY_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the monthly climatology wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMI_MONTHLY_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Monthly wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMI_SEASONAL-CLIMATOLOGY_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the seasonal climatology wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_L3_WIND_SPEED_SMI_3MONTH_V5

Aquarius Level 3 ocean surface wind speed standard mapped image data contains gridded 1 degree spatial resolution wind speed data averaged over daily, 7 day, monthly, and seasonal time scales. This particular data set is the Seasonal wind speed product for version 5.0 of the Aquarius data set, which is the official end of mission public data release from the AQUARIUS/SAC-D mission. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath.

AQUARIUS_ANCILLARY_RFI_V1

Aquarius ancillary Radio Frequency Interference (RFI) product used in ADPS mission processing contains monthly-averaged Radio Frequency Interference (RFI) data for ascending/descending passes as detected by the Aquarius radiometers and scatterometer. The data is available for ascending (northward) and descending (southward) passes of the satellite only and ascending/descending passes combined. The values stored in this product are the percentage of radiometer and scatterometer measurements identified as corrupted by interference by the RFI detection algorithms [1,2] within each data point, averaged over one month. An additional RFI flag [3] is used to identify locations where the measured brightness temperature over land exceeds the expected limits of surface emissivity. This flag is not used to remove samples from further processing, but, in generating the radiometer RFI data, 100% RFI is assigned to points where this flag is raised. This product can be used to reproduce the RFI maps available on the Aquarius website at University of Maine (https://aquarius.umaine.edu/cgi/gal_radiometer.htm for the radiometer, and https://aquarius.umaine.edu/cgi/gal_scatterometer.htm for the scatterometer), by plotting the variables Rad_RFI_percent_AscDes_AllBeams and Scat_RFI_percent_AscDes_AllBeams on the latitude/longitude grid. Additionally, the user can generate maps by using only a particular beam or only ascending passes, for example. All combinations of beams and ascending/descending passes are possible. This product contains information about RFI, but it is also relevant for the retrieved Sea Surface Salinity (SSS). Over the ocean, the RFI percentage in this product corresponds to the amount of raw measurements discarded due to RFI contamination before SSS retrieval. Therefore, maps of the RFI percentage can give the user an indication of where RFI is more likely to have affected the quality of SSS retrievals, for a particular month, or for a series of months.

AQUARIUS_L4_OISSS_IPRC_7DAY_V5

The IPRC/SOEST Aquarius OI-SSS v5 product is a level 4, near-global, 0.5 degree spatial resolution, 7-day, optimally interpolated salinity dataset based on version 5.0 of the AQUARIUS/SAC-D level 2 mission data. This is a PI led dataset produced at the International Pacific Research Center (IPRC) at the University of Hawaii (Manoa) School of Ocean and Earth Science and Technology. The optimal interpolation (OI) mapping procedure used to create this product corrects for systematic spatial biases in Aquarius SSS data with respect to near-surface in situ salinity observations and takes into account available statistical information about the signal and noise, specific to the Aquarius instrument. Bias fields are constructed by differencing in situ from Aquarius derived SSS fields obtained separately using ascending and descending satellite observations for each of the three Aquarius beams, and by removal of small-scale noise and low-pass filtering along-track using a two-dimensional Hanning window procedures prior to application of the OI algorithm. Additional enhancements for this new version of the product include: 1) The V5.0 (end-of mission) version of Aquarius Level-2 (swath) SSS data are used as input data for the OI SSS analysis. 2) The source of the first guess fields has changed from the APDRC Argo-derived SSS product to the average of four different in-situ based SSS products. 3) The bias correction algorithm has changed to adjust SSS retrievals for large-scale systematic biases on a repeat-track basis. 4) New, less restrictive thresholds are implemented to filter observations for land and ice contamination, thus improving coverage in the coastal areas and semi-enclosed seas. 5) Level-2 RFI masks for descending and ascending satellite passes are used to discard observations in specific geographic zones where excessive ascending-descending differences are observed due to contamination from undetected RFI. The Aquarius instrument is onboard the AQUARIUS/SAC-D satellite, a collaborative effort between NASA and the Argentinian Space Agency Comision Nacional de Actividades Espaciales (CONAE). The instrument consists of three radiometers in push broom alignment at incidence angles of 29, 38, and 46 degrees incidence angles relative to the shadow side of the orbit. Footprints for the beams are: 76 km (along-track) x 94 km (cross-track), 84 km x 120 km and 96km x 156 km, yielding a total cross-track swath of 370 km. The radiometers measure brightness temperature at 1.413 GHz in their respective horizontal and vertical polarizations (TH and TV). A scatterometer operating at 1.26 GHz measures ocean backscatter in each footprint that is used for surface roughness corrections in the estimation of salinity. The scatterometer has an approximate 390km swath. The Aquarius polar orbit is sun synchronous at 657 km with a 6 pm, ascending node, and has a 7-Day repeat cycle.

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NASA ARCSIX Project

An inventory of NASA's airborne and field campaigns for Earth Science

ARCSIX_Analysis_LaRC-G3_Data

ARCSIX_Analysis_LaRC-G3_Data is the analysis data collected onboard the LaRC G-III aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?

ARCSIX_Dropsondes_LaRC-G3_Data

ARCSIX_Dropsondes_LaRC-G3_Data is the dropsonde data collected onboard the LaRC G-III aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the Airborne Vertical Atmosphere Profiling System (AVAPS) is featured in this collection. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?

ARCSIX_AircraftRemoteSensing_LaRC-G3_HALO_Data

ARCSIX_AircraftRemoteSensing_LaRC-G3_HALO_Data is the High Altitude Lidar Observatory (HALO) data collected onboard the LaRC G-III aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?

ARCSIX_MetNav_AircraftInSitu_LaRC-G3_Data

ARCSIX_MetNav_AircraftInSitu_LaRC-G3_Data is the in-situ meteorology and navigation data collected onboard the LaRC G-III aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the N520NA Meteorological and Navigation Facility Instrumentation is featured in this collection. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?

ARCSIX_Cloud_AircraftInSitu_Learjet_Data

ARCSIX_Cloud_AircraftInSitu_Learjet_Data is the in-situ cloud data collected onboard the Learjet aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the 2D-Gray Particle Probe (2D-Gray), 2D-Stereo Particle Probe (2DS), Fast Cloud Droplet Probe (FCDP), High Volume Precipitation Spectrometer (HVPS), and the Nevzorov Water Vapor Probe are featured in this collection. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?

ARCSIX_MetNav_AircraftInSitu_Learjet_Data

ARCSIX_MetNav_AircraftInSitu_Learjet_Data is the in-situ meteorology and navigation data collected onboard the Learjet aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?

ARCSIX_AircraftRemoteSensing_Learjet_KPR_Data

ARCSIX_AircraftRemoteSensing_Learjet_KPR_Data is the Ka-Band Probe Radar-Radiometer (KPR) data collected onboard the Learjet aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?

ARCSIX_AircraftRemoteSensing_P3B_MARLi_Data

ARCSIX_AircraftRemoteSensing_P3B_MARLi_Data contains data collected by the Multi-function Airborne Raman Lidar (MARLi) onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?

ARCSIX_AircraftRemoteSensing_P3B_RSP_Data

ARCSIX_AircraftRemoteSensing_P3B_RSP_Data contains data collected by the Research Scanning Polarimeter (RSP) onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?

ARCSIX_Analysis_P3B_Data

ARCSIX_Analysis_P3B_Data is the analysis data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?

ARCSIX_Aerosol_AircraftInSitu_P3B_Data

ARCSIX_Aerosol_AircraftInSitu_P3B_Data is the in-situ aerosol data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the Nucleation Mode Aerosol Size Spectrometer, Portable Optical Particle Spetrometer (POPS), Ultra-High Sensitivity Aerosol Spectrometer (UHSAS), Time-of-Flight Aerosol Mass Spectrometer (ToF-AMS), Differential Aerosol Sizing and Hygroscopicity Spectrometer Probe (DASH-SP), Fast Integrated Mobility Spectrometer (FIMS), Aerodynamic Particle Sizer (APS), Cloud Condensation Nuclei Counter (CCN), TSI Condensation Particle Counter 3772 (TSI CPC-3772), TSI-3563 Nephelometer, and the Single Particle Soot Photometer (SP2) are featured in this collection. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?

ARCSIX_Cloud_AircraftInSitu_P3B_Data

ARCSIX_Cloud_AircraftInSitu_P3B_Data is the in-situ cloud data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the 2D-Stereo Particle Probe (2DS), Fast Cloud Droplet Probe (FCDP), High Volume Precipitation Spectrometer (HVPS), and the Continuous Flow Diffusion Chamber (CFDC) are featured in this collection. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?

ARCSIX_MetNav_AircraftInSitu_P3B_Data

ARCSIX_MetNav_AircraftInSitu_P3B_Data is the in-situ meteorology and navigation data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the Diode Laser Hygrometer (DLH) and the UNS-1Fw Flight Management System are featured in this collection. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?

ARCSIX_Radiation_AircraftInSitu_P3B_Data

ARCSIX_Radiation_AircraftInSitu_P3B_Data is the in-situ radiation data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the Broadband Radiometer (BBR), Hyper-Spectral Radiometer, Solar Spectral Flux Radiometers (SSFR), and the G-band Vapor Radiometer (GVR) are featured in this collection. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?

ARCSIX_TraceGas_AircraftInSitu_P3B_Data

ARCSIX_TraceGas_AircraftInSitu_P3B_Data is the in-situ trace gas data collected onboard the P-3B aircraft during the Arctic Radiation-Cloud-Aerosol-Surface Interaction EXperiment (ARCSIX) campaign. Data from the PICARRO G2401-m Gas Concentration Analyzer and the 2B Technologies Model 205 Ozone Monitor are featured in this collection. Data collection for this product is complete. The ARCSIX campaign is a NASA field investigation aimed at quantifying the contributions of surface properties, clouds, aerosol particles, and precipitation to the Arctic summer surface radiation budget and sea ice melt during the early melt season. Based out of Greenland, ARCSIX completed two deployments from May – June 2024 and July - August 2024 utilizing the NASA P-3B, LaRC G-III, and SPEC-Learjet aircraft. The P-3B was equipped with in situ and remote sensing payloads to acquire measurements of aerosols, cloud, and radiation properties. The high-flying LaRC G-III was equipped with remote sensing instrumentation, including the HALO, and HSRL, along with the AVAPS dropsonde system. The SPEC-Learjet acquired measurements of cloud microphysics. Data were also collected at the Thule High Arctic Atmospheric Observatory (THAAO) in Pituffik, Greenland. The primary objective of ARCSIX was to enhance long-term space-based monitoring and predictive capabilities of Arctic sea ice, cloud, and aerosols by validating and improving remote sensing algorithms and model parameterizations in the Arctic. ARCSIX science questions focused on examining the impact of the predominant summer Arctic cloud types on the radiative surface energy budget, what processes control the evolution and maintenance of the predominant cloud types in the summertime Arctic, and how do the two-way interactions between surface properties and atmospheric forcings affect sea ice evolution?

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

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NASA ASCENDS Airborne Project

This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2) over California and Nevada on February 10-11, 2016. Measurements were taken onboard a DC-8 aircraft during this Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) airborne deployment. CO2 was measured with NASA's Atmospheric Vertical Observations of CO2 in the Earth's Troposphere (AVOCET) instrument while over California and Nevada. The objective of this deployment was to assess the performance of the 2016 version of the CO2 Sounder LiDAR. The two flights were flown to compare results from an experimental LiDAR sensor with the AVOCET instrument. Aircraft navigation and flight meteorological data are also provided. The data are provided in ICARTT and comma-separated values (CSV) formats.

ASCENDS_LAS_IN_Sept_2014_2116

This dataset provides in situ airborne measurements of atmospheric carbon dioxide (CO2) over Indianapolis, Indiana (IN) on September 3, 2014 during the morning commuter period with heavy traffic emissions. Stationary source emissions are also included. The observed CO2 plume downwind of the urban area, along with the prevailing wind speed and direction, enabled estimations of emission rates. CO2 was measured with an airborne CO2 Laser Absorption Spectrometer (JPL CO2LAS) developed at NASA's Jet Propulsion Laboratory (JPL) to demonstrate the airborne Integrated Path Differential-Absorption (IPDA) lidar technique as a stepping stone to a capability for global measurements of CO2 concentrations from space. The CO2LAS measures the weighted, column averaged carbon dioxide between the aircraft and the ground using a continuous-wave heterodyne technique. The instrument operates at a 2.05 micron wavelength optimized for enhancing sensitivity to boundary layer carbon dioxide. Measurements were taken onboard a DC-8 aircraft during this Active Sensing of CO2 Emissions over Nights, Days and Seasons (ASCENDS) airborne deployment. The data are provided in HDF-5 format.

Data Discovery

Explore this data using NASA's Earthdata Search, a comprehensive tool for discovering and visualizing Earth science datasets.

Data Access

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NASA ASIA-AQ Project

An inventory of NASA's airborne and field campaigns for Earth Science

ASIA-AQ_Analysis_DC8_Data

ASIA-AQ_Analysis_DC8_Data are analysis flag files derived from data products onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data flags identify plumes likely influenced by biomass burning and other combustion processes as well as identifies different air mass layers. Data collection for this product is complete. The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.

ASIA-AQ_Aerosol_AircraftInSitu_DC8_Data

ASIA-AQ_Aerosol_AircraftInSitu_DC8_Data is the in-situ aerosol data collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the Transmission Electron Microscopy (TEM), Aerosol Mass Spectrometer (AMS), Single Particle Soot Photometer (DMT SP2), Ultra-High Sensitivity Aerosol Spectrometer (DMT UHSAS), Scanning Mobility Particle Sizer (SMPS), TSI-3563 Nephelometer, Cloud Condensation Nuclei (CCN) counter, and Condensation Particle Counter (CPC) are featured in this collection. Data collection for this product is complete. The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.

ASIA-AQ_Cloud_AircraftInSitu_DC8_Data

ASIA-AQ_Cloud_AircraftInSitu_DC8_Data is the in-situ cloud data collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the Cloud Droplet Probe (CDP), and Cloud Particle Spectrometer with Polarized Detection (CPSPD) are featured in this collection. Data collection for this product is complete. The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.

ASIA-AQ_MetNav_AircraftInSitu_DC8_Data

ASIA-AQ_MetNav_AircraftInSitu_DC8_Data is the in-situ meteorology and navigation data collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the Diode Laser Hygrometer (DLH) and the Meteorological Measurement System (MMS) are featured in this collection. Data collection for this product is complete. The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.

ASIA-AQ_TraceGas_AircraftInSitu_DC8_Data

ASIA-AQ_TraceGas_AircraftInSitu_DC8_Data is the in-situ trace gas data collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the Trace Organic Gas Analyzer (TOGA), Whole Air Sampler (WAS), Quantum Cascade Tunable Infrared Differential Absorption Spectrometer (QC-TILDAS), Chemical Ionization Time-of-Flight Mass Spectrometer (CIT-ToF-CIMS), Differential Absorption CO, CH4, N2O Measurements (DACOM), LI-7000 Closed Path CO2/H2O Gas Analyzer (LI-7000), Los Gatos Research CO/CO2/H2O Analyzer (LGR), Airborne Cavity Enhanced Spectrometer (ACES), MIRO Multi-compound Gas Analyzer (MIRO MGA), Compact Airborne Nitrogen diOxide Experiment (CANOE), Rapid Ozone Experiment (ROZE), Proton Transfer Mass Spectrometer (PTR-MS), In Situ Airborne Formaldehyde (ISAF), and Open-Path Ammonia Laser Spectrometer (OPALS) are featured in this collection. Data collection for this product is complete. The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.

ASIA-AQ_jValue_AircraftInSitu_DC8_Data

ASIA-AQ_jValue_AircraftInSitu_DC8_Data is the in-situ photolysis rates collected onboard the DC-8 aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data from the CCD-based Actinic Flux Spectroradiometer (CAFS) is featured in this collection. Data collection for this product is complete. The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.

ASIA-AQ_AircraftRemoteSensing_LaRC-G3_GCAS_Data

ASIA-AQ_AircraftRemoteSensing_LaRC-G3_GCAS_Data is the Geostationary Coastal and Air Pollution Event (GEO-CAPE) Airborne Simulator Data (GCAS) data collected onboard the NASA LaRC G-III aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data collection for this product is complete. The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.

ASIA-AQ_AircraftRemoteSensing_LaRC-G3_HSRL2_Data

ASIA-AQ_AircraftRemoteSensing_LaRC-G3_HSRL2_Data is the High Spectral Resolution Lidar (HSRL) data collected onboard the NASA LaRC G-III aircraft during the Airborne and Satellite Investigation of Asian Air Quality (ASIA-AQ) campaign. Data collection for this product is complete. The ASIA-AQ campaign was an international cooperative field study designed to address local air quality challenges. Conducted from January-March 2024, ASIA-AQ deployed multiple aircraft to collect in situ and remote sensing measurements, along with numerous ground-based observations and modeling assessments. Data was collected over four countries including, the Philippines, Taiwan, South Korea and Thailand and flights were conducted in full partnership with local scientists and environmental agencies responsible for air quality monitoring and assessment. One of the primary goals of ASIA-AQ was to contribute improving integration of satellite observations with existing air quality ground monitoring and modeling efforts across Asia. Air quality observations from satellites are evolving with new capabilities from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS), which conducts hourly measurements to provide a new view of air quality conditions from space that complements and depends upon ground-based monitoring efforts of countries in its field of view. ASIA-AQ science goals focused on satellite validation and interpretation, emissions quantification and verification, model evaluation, aerosol chemistry, and ozone chemistry.

Data Discovery

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Data Access

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NASA ASTER GED Project

The AG1kmB Version 3 dataset was decommissioned as of December 14, 2016. Users are encouraged to use the ASTER Global Emissivity Dataset 1-kilometer AG1km dataset in HDF5. The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) MOD07 atmospheric profiles and the MODerate spectral resolution TRANsmittance (MODTRAN) 5.2 radiative transfer model. This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. The HDF5 version of the data are available for distribution, please see AG1km for more information. The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product. Knonw Issues * Known issues are provided in Section 4 of the User Guide.

AG100B

The AG100B Version 3 dataset was decommissioned as of December 14, 2016. Users are encouraged to use the ASTER Global Emissivity Dataset 100-meter AG100 Version 3 dataset in HDF5. The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) MOD07 atmospheric profiles and the MODerate Spectral resolution TRANsmittance (MODTRAN) 5.2 radiative transfer model. This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. The HDF5 version of the data are available for distribution, please see AG100 for more information. The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product. Known Issues: Known Issues are provided in Section 4 of the ASTER GED User Guide.

AG1km

The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) MOD07_L2 atmospheric profiles and the MODerate Spectral resolution TRANsmittance (MODTRAN 5.2) radiative transfer model. This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product. Known Issues: Known issues are provided in Section 4 of the User Guide.

AG100

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) land surface temperature and emissivity (LST&E) data products are generated using the ASTER Temperature Emissivity Separation (TES) algorithm with a Water Vapor Scaling (WVS) atmospheric correction method using Moderate Resolution Imaging Spectroradiometer (MODIS) MOD07_L2 atmospheric profiles and the MODerate spectral resolution TRANsmittance (MODTRAN 5.2 radiative transfer model). This dataset is computed from all clear-sky pixels of ASTER scenes acquired from 2000 through 2008. AG100 data are available globally at spatial resolution of 100 meters. The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product. Known Issues: Known issues are provided in Section 4, starting on page 8 of the User Guide.

AG5KMMOH

The AG5KMMOH Version 4 dataset was decommissioned as of December 14, 2016. Users are encouraged to use Version 4.1 ASTER Global Emissivity Dataset, Monthly, 0.05 degree, HDF5. The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) is a collection of monthly files for each year of global emissivity. The ASTER GED data products are generated for 2000 through 2015 using the ASTER Temperature Emissivity Separation (TES) algorithm atmospheric correction method. This algorithm method uses Moderate Resolution Imaging Spectroradiometer (MODIS) Atmospheric Profiles product MOD07 and the MODTRAN 5.2 radiative transfer model along with the snow cover data from the standard monthly MODIS/Terra snow cover monthly global 0.05 degree product MOD10CM, and vegetation information from the MODIS monthly gridded Normalized Difference Vegetation Index (NDVI) product MOD13C2. The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product.

AG5KMMOH

The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) is a collection of monthly files (see known issues for gaps) for each year of global emissivity. The ASTER GED data products are generated for 2000 through 2015 using the ASTER Temperature Emissivity Separation (TES) algorithm atmospheric correction method. This algorithm method uses Moderate Resolution Imaging Spectroradiometer (MODIS) Atmospheric Profiles product MOD07 and the MODerate spectral resolution TRANsmittance (MODTRAN) 5.2 radiative transfer model along with the snow cover data from the standard monthly MODIS/Terra snow cover monthly global 0.05 degree product MOD10CM, and vegetation information from the MODIS monthly gridded NDVI product MOD13C2. ASTER GED Monthly V041 data products are offered in Hierarchical Data Format 5 (HDF5). The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product. Known Issues: Since ASTER GED Monthly V041 uses MOD10CM as an input, there will be some months that were unable to be created due to missing data. Please see the MODIS/Terra Data Outages Webpage for more information.

AG5KMMON

The AG5KMMOH Version 4 dataset was decommissioned as of December 14, 2016. Users are encouraged to use Version 4.1 of ASTER Global Emissivity Dataset, Monthly, 0.05 degree, HDF5. The Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) is a collection of monthly files (see known issues for gaps) for each year of global emissivity. The ASTER GED data products are generated for 2000 through 2015 using the ASTER Temperature Emissivity Separation (TES) algorithm atmospheric correction method. This algorithm method uses Moderate Resolution Imaging Spectroradiometer (MODIS) Atmospheric Profiles product MOD07 and the MODerate spectral resolution TRANsmittance (MODTRAN) 5.2 radiative transfer model along with the snow cover data from the standard monthly MODIS/Terra snow cover monthly global 0.05 degree product MOD10CM, and vegetation information from the MODIS monthly gridded Normalized Difference Vegetation Index (NDVI) product MOD13C2. The National Aeronautics and Space Administration’s (NASA) Jet Propulsion Laboratory (JPL), California Institute of Technology, developed the ASTER GED product.

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NASA ATDD Project

This is a subset of AMSR-E rain rate product along CloudSat field of view track. The goal of the subset is to select and return AMSR-E data that are within -100 km across the CloudSat track. Thus resultant subset swath is 45 pixels cross-track. Apart from that, all efforts are made to preserve the original HDF-EOS formatting of the source full-sized data. The Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) instrument on the NASA EOS Aqua satellite provides global passive microwave measurements of terrestrial, oceanic, and atmospheric variables for the investigation of water and energy cycles. The original, full-sized, product is Level-2B swath product (AE_Rain), and it contains instantaneous measurements of rain rate and rain type (convective vs. stratiform), generated from Level-2A brightness temperatures (AE_L2A). The Goddard Space Flight Center (GSFC) Profiling algorithm determines rain rate and type over ocean areas, and a Modified GSFC Profiling algorithm over land. Data are stored in HDF-EOS (HDF4) format, and are available from 18 June 2002 until the AMSR-E instrument was turned off due to antenna problems in October 2011.

MAC04S0

This is the narrow-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the narrow-swath subset is to select and return MODIS data that are within +-5 km across the CloudSat track. I.e. the resultant MODIS subset swath is sought to be about 10 km cross-track.However, the original MYD04_L2 has 10-km pixels. Thus, MAC04S0 cross-track width is 2 pixels, the closest on either side of CloudSat track, and the resultant cross-track swath width is about 20 km.Along-track, all MODIS pixels from the original product are preserved. In the stardard product, the MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties (e.g., optical thickness and size distribution), mass concentration, look-up table derived reflected and transmitted fluxes, as well as quality assurance and other ancillary parameters, globally over ocean and near globally over land. (The shortname for this product is MAC04S0).

MAC04S1

This is the wide-swath MODIS/Aqua subset along CloudSat field of view track. The goal of the wide-swath subset is to select and return MODIS data that are within +-100 km across the CloudSat track. I.e. the resultant MODIS subset swath is sought to be about 200 km cross-track. However, the original MYD04_L2 has 10-km pixels. Thus, MAC04S1 cross-track width is 21 pixels, and the resultant cross-track swath width is about 200 km. Along-track, all MODIS pixels from the original product are preserved. In the standard product, the MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties (e.g., optical thickness and size distribution), mass concentration, look-up table derived reflected and transmitted fluxes, as well as quality assurance and other ancillary parameters, globally over ocean and near globally over land. (The shortname for this product is MAC04S1).

MAM04S0

This is the MODIS/Aqua subset along MLS field of view track. The goal of the subset is to select and return MODIS data that are within +-100 km across the MLS track. I.e. the resultant MODIS subset swath is sought to be about 200 km cross-track. However, the original MYD04_L2 has 10-km pixels. Thus, MAM04S0 cross-track width is 21 pixels, and the resultant cross-track swath width is about 200 km. Along-track, all MODIS pixels from the original product are preserved. In the stardard product, the MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties (e.g., optical thickness and size distribution), mass concentration, look-up table derived reflected and transmitted fluxes, as well as quality assurance and other ancillary parameters, globally over ocean and near globally over land. (The shortname for this product is MAM04S0).

MAM03S0

This is the MODIS/Aqua subset along the Microwave Limb Sounder (MLS) field of view track. The goal of the subset is to select and return MODIS data that are within +-100 km across the MLS track. I.e. the resultant MODIS subset swath is about 200 km cross-track. Thus, MAM03S0 cross-track width is 201 pixels. Along-track, all MODIS pixels from the original product are preserved. In the standard product, geolocation fields are calculated for each 1 km MODIS Instantaneous Field of Views (IFOV) for all orbits daily. The locations and ancillary information corresponds to the intersection of the centers of each IFOV from 10 detectors in an ideal 1 km band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit, the instrument telemetry and the digital elevation model. The geolocation fields include geodetic Latitude, Longitude, surface height above geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/sea mask for each 1 km sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors of any of the 36 MODIS bands. This product is used as input by a large number of subsequent MODIS products, particularly the products produced by the Land team. (The shortname for this product is MAM03S0).

OMCLDO2_CPR

This the OMI/Aura Cloud Pressure and Fraction (O2-O2 Absorption) subset along CloudSat track, for the purposes of the A-Train mission. The original product uses the DOAS technique method. This level-2 global cloud product at the pixel resolution (13x24 km2 at nadir) is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track. This product contains cloud pressure, cloud fraction, slant column O2-O2 and O3, ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this Level-2 OMI cloud pressure and fraction (O2-O2 absorption) subset along CloudSat track product is OMCLDO2_CPR)

OMCLDRR_CPR

This is the OMI/Aura Cloud Pressure and Fraction (Raman Scattering) subset along CloudSat tracks, for the purposes of the A-Train mission. The original data product uses the Rotational Raman Scattering method. This level-2 global cloud product provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). The goal of this subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this Level-2 OMI cloud pressure and fraction subset along CloudSat tracks product is OMCLDRR_CPR)

OMAERUV_CPR

This is a CloudSat-collocated subset of the original OMI product OMAERUV, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated OMI Level 2 near-UV aerosol subset is OMAERUV_CPR_003)

OMNO2_CPR

This is a CloudSat-collocated subset of the original product OMNO2, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated OMI Level 2 NO2 subset is OMNO2_CPR_V003)

OMTO3_CPR

This is a CloudSat-collocated subset of the original product OMTO3, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated OMI Level 2 Total Ozone Column subset is OMTO3_CPR_V003)

OMSO2_CPR

This is a CloudSat-collocated subset of the original product OMSO2, for the purposes of the A-Train mission. The goal of the subset is to select and return OMI data that are within +/-100 km across the CloudSat track. The resultant OMI subset swath is sought to be about 200 km cross-track of CloudSat. Even though collocated with CloudSat, this subset can serve many other A-Train applications. (The shortname for this CloudSat-collocated subset of the original product OMSO2 Product is OMSO2_CPR_V003) This document describes the original OMI SO2 product (OMSO2) produced from global mode UV measurements of the Ozone Monitoring Instrument (OMI). OMI was launched on July 15, 2004 on the EOS Aura satellite, which is in a sun-synchronous ascending polar orbit with 1:45pm local equator crossing time. The data collection started on August 17, 2004 (orbit 482) and continues to this day with only minor data gaps. The minimum SO2 mass detectable by OMI is about two orders of magnitude smaller than the detection threshold of the legacy Total Ozone Mapping Spectrometer (TOMS) SO2 data (1978-2005) [Krueger et al 1995]. This is due to smaller OMI footprint and the use of wavelengths better optimized for separating O3 from SO2. The product file, called a data granule, covers the sunlit portion of the orbit with an approximately 2600 km wide swath containing 60 pixels per viewing line. During normal operations, 14 or 15 granules are produced daily, providing fully contiguous coverage of the globe. Currently, OMSO2 products are not produced when OMI goes into the "zoom mode" for one day every 452 orbits (~32 days). For each OMI pixel we provide 4 different estimates of the column density of SO2 in Dobson Units (1DU=2.69x10^16 molecules/cm2) obtained by making different assumptions about the vertical distribution of the SO2. However, it is important to note that in most cases the precise vertical distribution of SO2 is unimportant. The users can use either the SO2 plume height, or the center of mass altitude (CMA) derived from SO2 vertical distribution, to interpolate between the 4 values: 1)Planetary Boundary Layer (PBL) SO2 column (ColumnAmountSO2_PBL), corresponding to CMA of 0.9 km. 2)Lower tropospheric SO2 column (ColumnAmountSO2_TRL), corresponding to CMA of 2.5 km. 3)Middle tropospheric SO2 column, (ColumnAmountSO2_TRM), usually produced by volcanic degassing, corresponding to CMA of 7.5 km, 4)Upper tropospheric and Stratospheric SO2 column (ColumnAmountSO2_STL), usually produced by explosive volcanic eruption, corresponding to CMA of 17 km. The accuracy and precision of the derived SO2 columns vary significantly with the SO2 CMA and column amount, observational geometry, and slant column ozone. OMI becomes more sensitive to SO2 above clouds and snow/ice, and less sensitive to SO2 below clouds. Preliminary error estimates are discussed below (see Data Quality Assessment). OMSO2 files are stored in EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMSO2 data product is about 9 Mbytes.

PARASOLRB_CPR

This is the POLDER/Parasol Level-2 Radiation Budget Subset, collocated with the CloudSat track. The subset is processed at the A-Train Data Depot of the GES DISC, NASA. The algorithm first converts the original POLDER binary data, which is Level-2 but nevertheless in a sinusoidal grid, into HDF4 format, and thus stores the full-sized data in HDF4. Then, it calculates the CloudSat ground track coordinates, and proceeds to extract the closest POLDER grid cells. Along with the extraction, the algorithm re-orders the subset grid cells in a line-by-line fashion, so that the output subset is in array format and resembles a swath. This array has a cross-track dimension of 11 columns. That makes about 200-km-wide coverage. All original parameters are preserved in the subset. As it is collocated with CloudSat, the subset is automatically collocated with CALIPSO as well.

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NASA ATLAS Project

The Shuttle Solar Backscatter Ultraviolet (SSBUV) Level-2 Ozone data are available for eight space shuttle missions flown between 1989 and 1996. SSBUV, a successor to the SBUV flown on the Nimbus-7 satellite, is nearly identical to the SBUV/2 instruments flying on the NOAA satellites. Data are available in the ASCII AMES text format. Ozone profiles of the upper atmosphere and total column ozone values are available for the following time periods: Flight #1: 1989 October 19, 20, 21. Flight #2: 1990 October 7, 8, 9. Flight #3: 1991 August 3, 4, 5, 6. Flight #4: 1992 March 29, 31. Flight #5: 1993 April 9, 11, 13, 15, 16. Flight #6: 1994 March 14, 15, 17. Flight #7: 1994 November 5, 7, 10, 13. Flight #8: 1996 January 12, 16, 18. SSBUV measures spectral ultraviolet radiances backscattered by the earth's atmosphere. For the ozone measurements the instrument steps over wavelengths between 252.2 and 339.99 nm while viewing the earth in the nadir position (50 km x 50 km footprint at nadir) at 19 pressure levels between 0.3 mb and 100 mb.

SSBUVIRR

The Shuttle Solar Backscatter Ultraviolet (SSBUV) level-2 irradiance data are available for eight space shuttle missions flown between 1989 and 1996. SSBUV, a successor to the SBUV flown on the Nimbus-7 satellite, is nearly identical to the SBUV/2 instruments flown on the NOAA polar orbiting satellites. Data are available in an ASCII text format. UV irradiance data are available for the following days from the eight missions: Flight #1: 1989 October 19, 20, 21 Flight #2: 1990 October 7, 8, 9 Flight #3: 1991 August 3, 4, 5, 6 Flight #4: 1992 March 29, 30 Flight #5: 1993 April 9, 11, 13, 15, 16 Flight #6: 1994 March 14, 15, 17 Flight #7: 1994 November 5, 7, 10, 13 Flight #8: 1996 January 12, 16, 18 The Shuttle SBUV (SSBUV) instrument measured solar spectral UV irradiance during the maximum and declining phase of solar cycle 22. The SSBUV data accurately represent the absolute solar UV irradiance between 200-405 nm, and also show the long-term variations during eight flights between October 1989 and January 1996. These data have been used to correct long-term sensitivity changes in the NOAA-11 SBUV/2 data, which provide a near-daily record of solar UV variations over the 170-400 nm region between December 1988 and October 1994. These data demonstrate the evolution of short-term solar UV activity during solar cycle 22.

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NASA ATOMIC Project

An inventory of NASA's airborne and field campaigns for Earth Science

SAILDRONE_ATOMIC

Saildrone is a wind and solar powered unmanned surface vehicle (USV) capable of long distance deployments lasting up to 12 months and providing high quality, near real-time, multivariate surface ocean and atmospheric observations while transiting at typical speeds of 3-5 knots. The drone is autonomous in that it may be guided remotely from land while being completely wind driven. The saildrone ATOMIC (Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign) campaign involved the deployment of a fleet of saildrones, jointly funded by NASA and NOAA, in the Atlantic waters offshore of Barbados over a 45 day period from 17 January to 2 March 2020. The goal was to understand the Ocean-Atmosphere interaction particularly over the mesoscale ocean eddies in that region. The saildrones were equipped with a suite of instruments that included a CTD, IR pyrometer, fluorometer, dissolved oxygen sensor, anemometer, barometer, and Acoustic Doppler Current Profiler (ADCP). Additionally, four temperature data loggers were positioned vertically along hull to provide further information on thermal variability near the ocean surface. This Saildrone ATOMIC dataset is comprised of two data files for each of the three NASA-funded saildrones deployed, one for the surface observations and one for the ADCP measuements. The surface data files contain saildrone platform telemetry and near-surface observational data (air temperature, sea surface skin and bulk temperatures, salinity, oxygen and chlorophyll-a concentrations, barometric pressure, wind speed and direction) spanning the entire cruise at 1 minute temporal resolution. The ADCP files for each saildrone are at 5 minute resolution for the duration of the deployments. All data files are in netCDF format and CF/ACDD compliant consistent with the NOAA/NCEI specification.

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NASA ATom Project

This dataset provides observations collected during eleven airborne campaigns from 2006–2017 and associated input and output from nine widely used chemical transport models (CTMs). The airborne campaigns include ARCTAS-A, ARCTAS-B, ATom-1 and ATom-2, CalNex, DC3, INTEX-B, KORUS-AQ, MILAGRO, SEAC4RS, and WINTER, and they sampled mainly tropospheric air over the conterminous U.S. and the state of Alaska, Mexico, Canada, Greenland, and South Korea and remote areas over the Arctic, Pacific, Southern, and Atlantic Oceans. The CTMs are the AM4.1, CCSM4, GEOS-5, GEOS-Chem TOMAS, GEOS-Chem v10, GEOS-Chem v12, GISS-MATRIX, GISS-ModelE, and TM4-ECPL-F, and the output includes sulfate, nitrate, temperature, specific humidity, mixing ratio of ammonium, the volume mixing ratio of nitric acid, surface pressure, gas-phase ammonia, gas-phase nitric acid, pressure, total ammonium, etc. The observations were collected in-situ from a variety of instruments, including the Aerosol Microphysical Properties (AMP), HR Aerodyne Aerosol Mass Spectrometer (AMS), CIT Chemical Ionization Mass Spectrometer (CIMS), diode laser hygrometer (DLH), a mist chamber/ion chromatography system (MC/IC), Particle Analysis by Laser Mass Spectrometer (PALMS), Single Particle Soot Photometer (SP2), and UCI Whole Air Sampler (WAS). In-situ data also include latitude, longitude, and pressure. These observations were used to investigate how aerosol pH and ammonium balance change from polluted to remote regions, such as over oceans, and were compared to predictions from the CTMs.

ATom_CAFS_Instrument_Data_V2_1933

This dataset contains actinic flux and photolysis frequencies for photodissociation reactions for a variety of chemical species during the four ATom campaigns. Spectrally resolved actinic flux was measured by the down- and up-welling Charged-coupled device Actinic Flux Spectroradiometers (CAFS) from approximately 280-650 nm. Photolysis frequencies were calculated from the actinic flux and published cross sections and quantum yield values for atmospherically relevant molecules. Solar radiation drives the chemistry of the atmosphere, including the evolution of ozone, greenhouse gases, biomass burning, and other anthropogenic and natural trace constituents.

ATom_SOAP_Instrument_Data_1898

This dataset contains one-second aerosol extinction and absorption measurements from the Spectrometers for Optical Aerosol Properties (SOAP) instrument aboard the NASA DC-8 aircraft during the ATom-4 campaign that occurred in 2018. SOAP is a compact, low maintenance instrument that measures aerosol extinction and absorption at 532 nm. Aerosol extinction is measured by cavity ringdown spectroscopy and aerosol absorption by photoacoustic spectroscopy. Extinction is measured with sufficient precision and accuracy for the remote atmosphere. The absorption measurements are valid only in strongly absorbing cases, such as in dilute plumes from wildfire smoke. The absorption and extinction of visible light by aerosol particles is a major component of the earth's radiation budget, strongly affecting climate. Highly absorbing particles directly heat the atmosphere, while particles that scatter light tend to cool the atmosphere. Extinction is the sum of absorption and scattering; in most cases scattering represents >90% of extinction, with absorption making up the remainder. These aerosol-radiation interactions also alter air temperature and the rates of photochemical reactions.

ATom_HIPPO_ORCAS_1788

This dataset provides calculated age of air (AoA) and the argon/nitrogen (Ar/N2) ratio (per meg) from stratospheric flask samples and simultaneous high-frequency measurements of nitrous oxide (N2O), carbon dioxide (CO2), ozone (O3), methane (CH4), and carbon monoxide (CO) compiled from three airborne projects. The trace gases were used to identify 235 flask samples with stratospheric influence collected by the Medusa Whole Air Sampler and to calculate AoA using a new N2O-AoA relationship developed using a Markov Chain Monte Carlo algorithm. The data span a wide range of latitudes poleward of 40 degrees in both the Northern and Southern Hemispheres and cover the period 2009-01-10 to 2018-05-21.

ATom_nav_1613

This dataset provides flight track and aircraft navigation data from the NASA Atmospheric Tomography Mission (ATom). Flight track information is available for the four ATom campaigns: ATom-1, ATom-2, ATom-3, and ATom-4. Each ATom campaign consists of multiple individual flights and flight navigational information is recorded in 10-second intervals. Data available for each flight includes research flight number, date, and start and stop time of each 10-second interval. In addition, latitude, longitude, altitude, pressure and temperature is included at each 10-second interval. NASA's ATom campaign deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. During each campaign, flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. One intended use of this flight track data is to facilitate to mapping model results from global models onto the precise ATom flight tracks for comparison.

ATom_Rad_Measurements_ARMAS_1906

This dataset contains Level 2 (L2) absorbed radiation dose rates in silicon from the Automated Radiation Measurements for Aerospace Safety (ARMAS) system along ATom flight paths for the ATom-1 campaign conducted in July and August 2016. Absorbed dose rates measure how much energy is deposited in matter by ionizing radiation per unit time. The radiation sources can be from galactic cosmic rays, solar energetic particles, or Van Allen radiation belt energetic particles. Radiation can have adverse effects on human tissue and aerospace electronics, as well as profound effects on chemical species in the atmosphere, making them important to consider in atmospheric modeling and analyses. In this context, the derived ambient equivalent dose rates are also provided and relate the absorbed dose in human tissue to the effective biological damage of the radiation through a radiation weighting factor. In addition, visualizations of absorbed radiation dose for ATom-1 flight paths are included. The visualizations show the absorbed dose rates in silicon in the upper panel and the 3D representation of the flight in the bottom panel.

ATom_FlightTrack_Influences_1889

This dataset contains back trajectories, boundary layer influences, and convective influences of air parcels along NASA DC-8 aircraft's flight tracks during the four ATom campaigns that occurred from 2016 to 2018. Back trajectories were interpolated using National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) and Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2) meteorology. Back trajectory analysis determines the origin of air masses by modeling the path of an air parcel backward in time. It can be used to better understand the sources of atmospheric compounds. Boundary layer Influences were determined based on 30 Day Back Trajectories. The atmospheric boundary layer is the lowest part of the troposphere that is directly influenced by earth's surface. The boundary layer influences wind patterns and thus the dispersal of pollutants and other atmospheric compounds of interest. Convective influences were based on 10 Day Back Trajectories and NASA Langley cloud products. Convective influences model the effects of convection on the movement of water vapor through the atmosphere, which influences cloud behavior.

ATom_Carbon_Aerosol_Loadings_1618

This dataset provides black carbon (BC) mass mixing ratios (in units of ng BC / kg air) measured during NASA's Atmospheric Tomography (ATom)-1 flight campaign during July and August 2016. The BC-core masses of BC-containing aerosol particles were measured using a Single Particle Soot Photometer (SP2). Conversion to mass mixing ratio (MMR) is achieved by monitoring sample flow. Influences in air mass composition were determined using the Particle Analysis by Laser Mass Spectrometry (PALMS) instruments. Also included here are data from the Cloud, Aerosol and Precipitation Spectrometer (CAPS) instrument which are used to identify measurements taken while in clouds. Finally, the associated latitude, longitude, altitude, and the timestamp of each measurement are included. All data are at ten seconds resolution. ATom-1 flights originated from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America.

ATom_CESM2_1878

This dataset contains CAM-chem (Community Atmosphere Model with Chemistry) model outputs along ATom flight tracks. CAM-chem is a component of the Community Earth System Model Version 2 (CESM2) and is used for simulations of global tropospheric and stratospheric atmospheric composition and for studies of chemistry-climate interactions. In general, CAM-chem uses the MOZART chemical mechanism, with various choices of complexity for tropospheric and stratospheric chemistry. For this dataset, CAM-chem used the MOZART-TS1 chemical mechanism, and the model was nudged to reanalysis meteorology from MERRA2.

ATom_CAPSVienna_Data_1981

This dataset contains cloud type and coarse aerosol contents measured by the University of Vienna's second-generation Cloud Aerosol and Precipitation Spectrometer (CAPS) instrument mounted to the NASA DC-8 aircraft during the four ATom campaigns that occurred from 2016 to 2018. CAPS measures particle size distributions in a size range between nominally 0.5 micrometers and 960 micrometers. The sizes range between approximately 0.5 and 50 micrometers is covered by the optical particle counter component of CAPS-the Cloud and Aerosol Spectrometer with Depolarization Detection (CAS-DPOL). The sizes range from 15 to 930 micrometers is measured with the optical array probe called Cloud imaging Probe (CIP). Cloud types are determined using an algorithm developed to detect and classify clouds using measurements of CAPS. Relative humidity and temperature are considered by the algorithm. The cloud indicator provides a classification on a 1 Hz basis and separates data in cloud-free, aerosol-cloud transition regime (ACTR), liquid clouds, clouds in the mixed-phase temperature regime (MPTR), and cirrus clouds. The coarse aerosol product provides cloud and aerosol particle number concentrations at standard pressure (1013.25 hPa) and standard temperature (273.15 K) in selected size ranges. Particle sizes refer to ammonium sulfate optical equivalent diameter (m=1.52 + 0.0i).

ATom_Picarro_Instrument_Data_1732

This dataset contains atmospheric measurements of CO2, CH4, and CO mixing ratios made with a Picarro G2401 spectrometer during the four ATom campaigns. Picarro G2401 uses Wavelength-Scanned Cavity Ring Down Spectroscopy (WS-CRDS), a time-based measurement utilizing a near-infrared laser to measure a spectral signature of the molecule. For the ATom mission, the Picarro instrument was modified in the laboratory to operate across the full pressure altitude range of flight campaigns. The instrument was also modified to have a shorter measurement interval.

ATom_Mapping_OH_Troposphere_1669

This dataset provides profile-integrated column densities of formaldehyde (HCHO), hydroxyl (OH), and OH production rates, diel tropospheric mean OH concentrations, and uncertainties that were derived from direct observation data from selected profiles of NASA Atmospheric Tomography (ATom) mission 1 and 2 flights for the period July 29, 2016 to February 21, 2017. These calculated products were combined with coincident HCHO column retrievals from the Ozone Monitoring Instrument (OMI) to scale and extend the profile results to a global gridded (0.5 deg latitude x 0.625 deg longitude) product. In addition to OMI formaldehyde column data, model output products from the Global Modeling Initiative (GMI) including average tropopause height, scaling factor, column air mass, and column-average formaldehyde photolysis frequency are provided. The GMI model output products were used in calculations and are included for user convenience.

ATom_Aerosol_Properties_V2_2111

This dataset contains comprehensive measurements of aerosol microphysical, chemical, and optical properties derived for both dry and ambient conditions from in situ measurements made during the four ATom campaigns. The dataset includes composition-resolved size distributions the integrated mass of sulfate, organics, nitrate, sea salt, dust, black carbon, and other compounds in coarse and fine fractions; extinction and absorption coefficients from each species at both dry and ambient conditions; asymmetry parameters; Angstrom exponents; and fitted lognormal functions to describe the size distribution. Optical parameters are calculated for 10 wavelengths from the near UV to the near IR, and size distributions range from 3 nm to 50 um in diameter. One file contains these data at 1-minute time intervals. Another file contains a subset of these data averaged into 1-km vertical bins for each vertical profile the aircraft made, as well as composition-resolved integrated aerosol optical depth derived from each profile. The concentration of cloud condensation nuclei is calculated for 5 supersaturations.

ATom_FullModel_DataStream_1877

This dataset provides Modeling Data Stream (MDS) and Reactivity Data Stream (RDS) products for each of the four ATom campaigns conducted from 2016 to 2018. MDS files contain the atmospheric constituents needed to model the RDS of the air parcels along ATom flight paths. The MDS is a continuous data stream (every 10 seconds) of the atmospheric content of these key chemical species derived from the in-situ measurements collected along ATom flight paths (as reported in the comprehensive related dataset ATom: Merged Atmospheric Chemistry, Trace Gases, and Aerosols). Values for chemical species measured by multiple instruments were selected from the instrument with better coverage and/or greater precision. Missing values were filled using interpolation for short gaps. For long gaps owing to instrument failure, values were estimated using multiple linear regressions from comparable parallel flights from other ATom campaigns. All species were flagged for instrument source and values were flagged for gap-filling status. In combination, MDS and RDS provide, in essence, a photochemical climatology for each air parcel along ATom flight paths containing the reactive species that control the loss of methane and the production and loss of ozone.

ATom_Forward_Flight_Videos_1938

This dataset contains images taken from the front of the NASA DC-8 aircraft during the first three ATom campaigns from 2016-2017. Images were taken with an Axis P1357 High Definition camera with a Theia TH138A wide-angle lens. These images were then stitched together at a 10-second frequency into an MP4 (.mp4) video for each flight. The forward camera shows the visible atmosphere that DC-8 flew through, allowing the in situ measurements to be placed in the context of cloud fields, smoke and haze layers, and boundary layers.

ATom_Clouds_Aerosols_2250

This dataset is the basis for the development of the Cloud Indicator, a novel algorithm that automatically detects and classifies measurement periods inside clouds. The included data were used in the analysis and development of figures for the related publication. The Cloud Indicator algorithm was developed based on particle size distribution measurements from a second-generation Cloud, Aerosol, and Precipitation Spectrometer (CAPS) combined with measurements of relative humidity and temperature from other sensors, to automatically detect flight sequences in clouds and classify the cloud type. Measurements were collected on 2016-08-20 as part of the Atmospheric Tomography Mission (ATom-1) Campaign and on 2017-04-20 as part of the Absorbing aerosol layers in a changing climate: aging, LIFEtime and dynamics (A-LIFE) project. As an additional criterion for the Cloud Indicator, a cloud-aerosol volume factor was established to ensure a precise and robust distinction between clouds and aerosol layers such as mineral dust or biomass burning to reduce misclassifications. Data are provided in netCDF (.nc) format.

ATom_Mineral_Dust_Cirrus_Cloud_2006

This dataset provides: (1) In situ dust aerosol concentration measurements over remote tropical Pacific and Atlantic Oceans by NOAA Particle Analysis by Laser Mass Spectrometry (PALMS) airborne single-particle mass spectrometer combined with Aerosol Microphysical Properties (AMP) aerosol size spectrometers. Measurements were made aboard the NASA DC8 aircraft during the four ATom campaigns that occurred from 2016 to 2018 (2) Model output of dust and meteorology from the CESM global transport model extracted at the time and location of the aircraft; (3) Model output of dust, other aerosol, and meteorology from the GEOS global transport model extracted at the time and location of the aircraft; (4) CESM model global output of dust and meteorology for dust emitted by specific source regions; (5) NCEP Global Forecast System forward trajectories of air parcels initiated at the time and location of the aircraft; and (6) The location and properties of cirrus clouds formed along the forward trajectories simulated using a parcel model. These data have been applied to better understand the role of mineral dust in cirrus cloud formation.

Flight_Environment_Parameters_1909

This dataset contains flight dynamics and environmental parameters (often referred to as housekeeping) specific to the DC-8 aircraft as collected from an assortment of instruments across all four ATom campaigns flown from 2016 through 2018. Measurements include aircraft position, altitude, speed, wind parameters, air temperature, and atmospheric and cabin pressure. These data can be used to understand the interior and exterior conditions and positioning of the DC-8 aircraft at 1-second resolution.

Interpolated_Met_Products_1876

This dataset provides modeled meteorological conditions and tagged-CO tracer concentrations along ATom flight paths derived from the Goddard Earth Observing System Version 5 (GEOS-5) data assimilation products from the Global Modeling and Assimilation Office (GMAO) at NASA's Goddard Space Flight Center. The GMAO "GEOS fp" forward processing system ingests satellite, ground-based, and airborne data, using a sophisticated model along with the data's statistical properties to obtain global three-dimensional data gridded fields at regular time intervals. These data are from the GMAO model output that were fitted to the ATom flight tracks by interpolating the GMAO model output to the horizontal ATom flight tracks for each of the 4 ATom Deployments. The dataset also provides tagged-CO tracer concentrations, which represent the contribution of specific regional sources to the total simulated CO. The data products produced are consistent with both the original measurements and the physical laws governing the atmosphere. To provide some meteorological context for the ATom flights, the GEOS5 gridded data are interpolated in space and time to the flight tracks.

ATom_Photolysis_Rates_1651

This dataset provides the results from nine global chemistry-climate or chemistry-transport models that estimated gridded values of atmospheric photolytic rates (J values) for ozone (O3), designated J-O1D, and nitrogen dioxide (NO2), designated J-NO2, under cloudy and clear sky scenarios. Each model produced global 4-D fields (latitude by longitude by pressure for 24 hours) for one day in mid-August 2016 (nominally) of results from two simulations: first using their standard treatment of clouds (all sky or cloudy) and a second with clouds and aerosols removed (clear sky). Model resolution ranges from 0.5 to 2.5 degrees. Observed J-O1D and J-NO2 values from the first ATom deployment (29 July - 23 August 2016) were collected with the Charged-coupled device Actinic Flux Spectroradiometer (CAFS) instrument. The ATom CAFS measurements are 3-second averages along the flight path for selected remote areas over the tropical and northern Pacific Ocean. Both all-sky (cloudy) and synthesized clear-sky J values are provided. (3) Additional data are included for clouds and ozone column plus other cloudy and clear sky parameters for the same remote areas of the tropical and northern Pacific Ocean. These auxiliary data are provided for use with included MATLAB scripts to reproduce the plots and analyses performed in the related publication by Hall et al. (2018). Note that while the analyses in the related publication were limited to the Pacific basin, the global model data are archived with this dataset.

ATom_GlobalModelInitiative_CTM_1897

This dataset contains Global Modeling Initiative (GMI) Chemical Transport Model (CTM) outputs from the four Atom campaigns. GMI simulations of the ATom flight periods have a horizontal resolution of 1.0 x 1.25 degrees, with output every 15 minutes. The ICARTT files are generated by spatially and temporally interpolating the output to the ATom flight track. Vertical interpolation is linear in log-pressure. The netCDF files provide three-dimensional (3D) GMI simulation output for the region surrounding the flight track every 15 minutes at the original model resolution. GMI is a 3-D CTM that includes full chemistry for both the troposphere and stratosphere. GMI simulates the concentrations of many of the species measured during ATom.

ATom_DLH_Instrument_Data_V2_1937

This dataset provides the concentrations of water measured by the Diode Laser Hygrometer (DLH) flown on the NASA DC-8 during the ATom 1-4 campaigns from 2016 - 2018. The DLH measures the water vapor in the atmosphere by wavelength modulated differential absorption spectroscopy of an isolated rovibrational line. The measurements include water vapor mixing ratio in parts-per-million-by-volume (ppmv) and relative humidity in percent. Relative humidity, both with respect to liquid water and with respect to ice, are quantities derived from measurements of water vapor mixing ratio as well as ambient temperature and pressure.

ATom_CIT_Instrument_Data_V2_1927

This dataset provides the concentrations of gas-phase organic and inorganic analytes measured by the California Institute of Technology (CIT) Chemical Ionization Mass Spectrometer (CIMS), or CIT-CIMS, flown on the NASA DC-8 aircraft during the four ATom campaigns. The CIT-CIMS employs CF3O-ion chemistry with two independent mass spectrometers (compact time-of-flight and triple quadrupole) to enable sensitive and specific measurements of atmospheric trace gases. The measurements include hydrogen peroxide (H2O2), hydrogen cyanide (HCN), nitric acid (HNO3), methyl hydrogen peroxide (CH3OOH), peroxyacetic acid (C2O3H4), peroxynitric acid (HO2NO2), and sulfur dioxide (SO2), in units of parts-per-trillion-by-volume.

ATom_Aerosols_Meteorology_1684

This dataset provides (1) the results of in situ aerosol particle property measurements collected over remote tropical areas of both Pacific and Atlantic Oceans during the NASA airborne Atmospheric Tomography (ATom) campaigns for ATom-1 and ATom-2 and (2) modeled outputs of comparable aerosol properties, atmospheric chemistry and meteorology at 70 m resolution from four chemical-transport models matched to the location and time of the aircraft measurements.

Airborne_Insitu_Measurements_1784

This dataset provides results of selected in-situ measurements of airflow and aerosol particles collected during the following airborne campaigns: NASA Atmospheric Tomography (ATom), Saharan Aerosol Long-range Transport and Aerosol-Cloud-interaction Experiment (SALTRACE), and Absorbing aerosol layers in a changing climate: aging, lifetime and dynamics (A-LIFE). The airborne campaigns were conducted between 2013-06-10 and 2018-05-21. Depending upon the aircraft instrumentation per flight and campaign, the data include aircraft position, relative humidity, temperature, pressure, angle of attack (AOA), the probe location, true and probe air speeds, and aerosol particle diameters as extracted from Cloud Imaging Probe (CIP) images for the ATom and A-LIFE flights. Also provided are the results of combining the airborne data with numerical modeling to simulate particle sampling efficiency. Simulations investigated how airflow around wing-mounted instruments affected sampling efficiency and the induced errors for different realistic flight conditions.

ATom_WAS_Instrument_Data_1751

This dataset provides atmospheric concentrations of halocarbons and hydrocarbons measured by the UC-Irvine Whole Air Sampler (WAS) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The analysis of samples from the UCI WAS provides measurements of more than 50 trace gases, including C2-C10 NMHCs, C1-C2 halocarbons, C1-C5 alkyl nitrates, and selected sulfur compounds. Species were identified and measured using an established technique of airborne whole air sampling followed by laboratory analysis using gas chromatography (GC) with flame ionization detection (FID), and mass spectrometric detection (MSD). The ATom mission deployed an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018.

ATom_NOyO3_Instrument_Data_1734

This dataset provides in situ concentrations of nitric oxide (NO), nitrogen dioxide (NO2), total reactive nitrogen oxides (NOy), and ozone (O3) measured by the NOAA Nitrogen Oxides and Ozone (NOyO3) 4-channel chemiluminescence (CL) instrument during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. NOyO3 provides fast-response, specific, high precision, and calibrated measurements of nitrogen oxides and ozone at a spatial resolution of better than 100 m. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate.

ATom_SP2_Instrument_Data_1672

This dataset provides the refractory black carbon mass concentration at one-second resolution measured by the Single Particle Soot Photometer (NOAA SP2) instrument during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The SP2 is a laser-induced incandescence instrument primarily used for measuring the black carbon mass content of individual particles.

ATom_AO2_Instrument_Data_V2_1880

This dataset provides in situ atmospheric oxygen and carbon dioxide concentrations measured by the NCAR Airborne Oxygen Instrument (AO2) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The AO2 Instrument measures O2 concentration using a vacuum-ultraviolet absorption technique. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for a systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate.

ATom_AMP_Instrument_Data_1671

This dataset provides the number, surface area, and volume concentrations and size distributions of dry aerosol particles measured by the Aerosol Microphysical Properties (AMP) instrument package during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. Five instruments--two nucleation-mode aerosol size spectrometers (NMASS), two ultra-high sensitivity aerosol spectrometers (UHSAS), and a laser aerosol spectrometer (LAS)--comprise the AMP package. The AMP payload provides size distributions with up to one-second time resolution for dry aerosol particles between 0.003 and 4.8 microns in diameter.

ATom_GT_CIMS_Instrument_Data_1715

This dataset provides measurements of two important components of photochemical smog - peroxyacetyl nitrate (PAN) and peroxyl propionyl nitrate (PPN)- measured by the Georgia Tech Chemical Ionization Mass Spectrometer (GT-CIMS) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The GT-CIMS measures reactive nitrogen species in the lower atmosphere. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate.

ATom_HR-AMS_Instrument_Data_1716

This dataset provides the atmospheric concentrations of separated ions from inorganic and organic species measured by the High-Resolution Aerosol Mass Spectrometer (HR-AMS) collected during flights of the NASA ATom Mission. Data are available from all four ATom Campaigns. The HR-AMS detects non-refractory submicron aerosol composition by impaction on a vaporizer at 600 degrees C, followed by electron ionization and time-of-flight mass spectral analysis. The measurements include chemically speciated submicron non-refractory particulate mass at a one second and 60 second resolution, and the size distribution of chemically speciated submicron non-refractory particulate mass at 60 second resolution.

NOAA_ToF_CIMS_Instrument_Data_1921

This dataset provides the mixing ratios of reactive nitrogen and halogen species measured by the NOAA Iodide Ion Time-of-Flight Chemical Ionization Mass Spectrometer (NOAA CIMS) during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission for ATom-3 and ATom-4 campaigns. The NOAA CIMS uses chemical ionization mass spectrometric detection of gas phase organic and inorganic analytes via I- adduct formation. Measurements for ATom include N2O5 (dinitrogen pentoxide), ClNO2 (chloro nitrite), Cl2 (Chlorine), HCOOH (formic acid), C2H4O3S (hydroperoxymethyl thioformate), BrCl (bromine monochloride), BrCN (cyanogen bromide), and BrO (bromine monoxide). ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2-13 km altitude. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate.

ATom_PFP_Instrument_Data_1746

This dataset provides mole fractions of atmospheric trace gases measured by the Programmable Flask Package (PFP) Whole Air Sampler during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The PFP whole air sampler provides a means of automated or manual filling of glass flasks. The sampler is designed to remove excess water vapor from the sampled air and compress it without contamination into ~1-liter volumes. These flasks are analyzed at the NOAA's Global Monitoring Division laboratory for trace gases and at the INSTAR's Staple Isotope Lab laboratory for isotopes of methane. Analysis of standardized PFP samples can measure more than 60 trace gases including N2O, SF6, H2, CS2, OCS, CO2, CH4, CO, CFCs, HCFCs, HFCs, Solvents, Methyl Halides, Hydrocarbons and Perfluorocarbons. The ATom mission deployed an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018.

ATom_ISAF_Instrument_Data_1730

This dataset provides the atmospheric volume mixing ratio of formaldehyde measured during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. The NASA In Situ Airborne Formaldehyde (ISAF) instrument, based at the Goddard Space Flight Center, measures formaldehyde on high-altitude NASA aircraft. The instrument uses laser-induced fluorescence (LIF) to obtain the high detection sensitivity needed to detect formaldehyde in the upper troposphere and lower stratosphere where abundances are 10 parts per trillion. LIF also enables a fast time response needed to measure the abundance of formaldehyde in the finely structured outflow of convective storms. These measurements of formaldehyde will be used elucidate mechanisms of convective transport and quantify the effects of boundary layer pollutants on the ozone photochemistry and cloud microphysics of the upper atmosphere.

ATom_Particulate_Iodine_1773

This dataset provides mass concentrations of particulate iodine as measured by the High-Resolution Aerosol Mass Spectrometer (HR-AMS) during the first two deployments of the NASA Atmospheric Tomography airborne missions (ATom-1 and ATom-2) in 2016 and 2017, respectively. The data provided in this dataset result from a reanalysis of the initial HR-AMS data based on post-mission calibrations and are reported at 1-minute resolution. The dataset also includes the fractions of the main ions (I+, HI+, and I2+) that can be used to ascertain the oxidation state of iodine in particles. Each observation includes an air mass classification flag (tropospheric or stratospheric conditions) based on collocated in situ water vapor and ozone measurements and positional data from the HR-AMS data feed.

ATom_Medusa_Instrument_Data_V2_1881

This dataset provides O2/N2, CO2, Ar/N2, and stable isotope ratios of CO2 measured in flasks collected by the Medusa Whole Air Sampler during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. ATom deployed an extensive gas and aerosol payload on the NASA DC-8 aircraft for a systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Medusa collected 32 cryogenically dried, flow, and pressure-controlled samples per flight. The samples are collected by an automated sampler into 1.5 L glass flasks that integrate over 25 seconds. Medusa provides discretely-sampled comparisons for onboard in situ O2/N2 ratio and CO2 measurements and unique measurements of Ar/N2 and 13C, 14C, and 18O isotopologues of CO2. Medusa flasks are analyzed on a sector-magnet mass spectrometer and a LiCor non-dispersive infrared CO2 analyzer by the Scripps O2 Program at Scripps Institution of Oceanography.

ATom_SP2_LAM_FeOx_MMR_1828

This dataset provides mass mixing ratios and number density of light-absorbing metallic aerosols (LAM) in the size range 180-1290 nm obtained with the NOAA Single Particle Soot Photometer (SP2) during the four deployments of the NASA Atmospheric Tomography (ATom) airborne mission from 2016-2018. The NOAA SP2 detects light absorbing aerosols, such as black carbon (BC), via laser-induced incandescence to provide real-time in situ quantification of refractory aerosol mass and number density. The percent of LAM aerosols attributed to anthropogenic iron oxides (FeOx) by mass is also provided.

ATom_ATHOS_Instrument_Data_V2_1930

This dataset provides the mixing ratios of hydrogen oxides measured by the Airborne Tropospheric Hydrogen Oxides Sensor (ATHOS) during the ATom 1-4 campaigns. ATHOS uses laser-induced fluorescence (LIF) to measure hydroxide (OH) and hydroperoxyl (HO2) simultaneously. The measurements include OH and HO2 mixing ratios and the OH interference determined by chemical removal of OH. The reactivity of OH is measured by the OH Reactivity (OHR) instrument using the discharge flow method and is integrated into the ATHOS electronics. These data provide insights into the oxidative state of the global atmosphere. These data are useful for testing the oxidation chemistry in models and other analytical methods being developed to deduce the atmosphere's oxidative state.

ATom_MMS_Instrument_Data_1731

This dataset contains measurements from the Meteorological Measurement System (MMS) instrument from the four ATom campaigns. MMS is a state-of-the-art instrument for measuring accurate, high resolution in situ airborne state parameters (pressure, temperature, turbulence index, and the 3-dimensional wind vector). These key measurements enable our understanding of atmospheric dynamics, chemistry, and microphysical processes. The MMS is used to investigate atmospheric mesoscale (gravity and mountain lee waves) and microscale (turbulence) phenomena. An accurate characterization of the turbulence phenomenon is important for the understanding of dynamic processes in the atmosphere, such as the behavior of buoyant plumes within cirrus clouds, diffusions of chemical species within wake vortices generated by jet aircraft, and microphysical processes in breaking gravity waves. Accurate temperature and pressure data are needed to evaluate chemical reaction rates as well as to determine accurate mixing ratios. Accurate wind field data establish a detailed relationship with the various constituents and the measured wind also verifies numerical models used to evaluate air mass origin.

ATom_PALMS_Instrument_Data_1733

This dataset contains single-particle aerosol composition as measured by the Particle Analysis by Laser Mass Spectrometry (PALMS) instrument during the four ATom campaigns from 2016-2018. Single aerosol particles are classified into several particle types, including: mixed sulfate/organic nitrate, biomass burning, elemental carbon, mineral/metallic, meteoric material, alkali salt, sea salt, heavy oil combustion, and others. Particle types are reported as raw number fractions and as absolute mass concentrations. PALMS measures aerosol composition for particles from diameter ~100 to 5000 nm, with most of the particle data in the size range ~150 to 3000 nm. Also included are absolute aerosol concentrations measured by a modified Laser Aerosol Spectrometer (LAS). Integrated number, surface area, and volume concentrations from LAS are reported over multiple size ranges.

ATom_QCLS_Instrument_Data_V2_1932

This dataset provides atmospheric concentrations of CO2, CH4, CO, and N2O measured by the Harvard Quantum Cascade Laser System (QCLS) instruments during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. The QCLS (DUAL and CO2) instrument package contains two separate optical assemblies and calibration systems, and a common data system and power supply. The QCLS DUAL instrument simultaneously measures CO, CH4, and N2O concentrations, in situ, using two thermoelectrically cooled pulsed-quantum cascade lasers light sources, a multiple pass absorption cell, and two liquid nitrogen-cooled solid-state detectors. The QCLS CO2 instrument measures CO2 concentrations in situ using a thermoelectrically cooled pulsed-quantum cascade laser light source, gas cells, and liquid nitrogen cooled solid-state detectors. The CO2 mixing ratio of air flowing through the sample gas cell is determined by measuring absorption from a single infrared transition line at 4.32 microns relative to a reference gas of known concentration.

ATom_UCATS_Instrument_Data_1750

This dataset, collected with the Unmanned Aircraft Systems (UAS) Chromatograph for Atmospheric Trace Species (UCATS), provides atmospheric concentrations of nitrous oxide (N2O), sulfur hexafluoride (SF6), methane (CH4), hydrogen (H2), carbon monoxide (CO), water vapor (H2O), and ozone (O3). The UCATS system is three different instruments in one enclosure: a two-channel chromatograph with electron capture detectors (one measures N2O and SF6, the other measures CH4, H2 and CO), a tunable diode laser instrument for H2O, and a dual-beam O3 photometer.

ATom_SAGA_Instrument_Data_1748

Soluble acidic gases and aerosols (SAGA) were collected with two related installations; a mist chamber/ion chromatography (MC/IC) system and a paired bulk aerosol system. The MC/IC system measures in situ atmospheric distributions of nitric acid (plus < 1 um NO3 aerosol) and fine (< 1 um) aerosol sulfate at an approximately 80-second interval. The paired bulk aerosol system collects particulates onto filters for subsequent analysis. Collected filters were first extracted with water to obtain the water-soluble (WS) constituents and then extracted again using methanol to collect the methanol soluble (MS) fraction. The light absorption of filtered extracts was measured from 300 to 700 nm. Ion chromatography on aqueous extracts of the bulk aerosol samples collected on Teflon filters were used to quantify soluble ions (Cl-, Br-, NO3-, SO42-, C2O42-, Na+, NH4+, K+, Ca+, and Mg+). The SAGA system is provided by the University of New Hampshire (UNH).

ATom_merge_1581

This dataset provides information on greenhouse gases and human-produced air pollution, including atmospheric concentrations of carbon dioxide (CO2), methane (CH4), tropospheric ozone (O3), and black carbon (BC) aerosols, collected during airborne campaigns conducted by NASA's Atmospheric Tomography (ATom) mission. This dataset includes merged data from all instruments plus additional data such as numbered profiles and distance flown. Merged data have been created for seven different sampling intervals. In the case of data obtained over longer time intervals (e.g. flask data), the merge files provide (weighted) averages to match the sampling intervals. ATom deploys an extensive gas and aerosol payload on the NASA DC-8 aircraft for a systematic, global-scale sampling of the atmosphere, profiling continuously from 0.2 to 12 km altitude. Flights occurred in each of 4 seasons from 2016 to 2018. Flights originate from the Armstrong Flight Research Center in Palmdale, California, fly north to the western Arctic, south to the South Pacific, east to the Atlantic, north to Greenland, and return to California across central North America. ATom establishes a single, contiguous, global-scale dataset. This comprehensive dataset will be used to improve the representation of chemically reactive gases and short-lived climate forcers in global models of atmospheric chemistry and climate. Profiles of the reactive gases will also provide critical information for the validation of satellite data, particularly in remote areas where in situ data is lacking. Complete aircraft flight information including, but not limited to, latitude, longitude, and altitude are also provided. This data release provides results from all instruments on all four ATom flight campaigns.

ATom_NMASS_Data_1607

This dataset provides extensive calibration and in-flight performance data for two nucleation mode aerosol size spectrometer (NMASS) instruments utilized in the NASA Atmospheric Tomography Mission (ATom). Each NMASS has five condensation particle counters (CPCs) that detect particles above a different minimum size, determined by the maximum vapor supersaturation encountered by the particles. Operated in parallel, the CPCs provide continuous concentrations of particles in different cumulative size classes between 3 and 60 nm. Knowing the response function of each CPC, numerical inversion techniques were applied to recover size distributions from the continuous concentrations. Data provided include: NMASS counting efficiencies and diameters of calibration aerosols, inverted particle size distributions; comparisons of NMASS and Scanning Mobility Particle Sizer (SMPS) results; and performance at flows, temperatures, and pressures measured by both NMASSs and comparison with Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) concentrations collected on board the NASA DC-8 aircraft during an ATom flight in February 2017.

ATom_CO_GEOS_1604

This dataset contains carbon monoxide (CO) observations at 10-second intervals from flights during the ATom-1 campaign in 2016 and simulated CO concentrations from the Goddard Earth Observing System version 5 (GEOS-5) model for the corresponding locations along the ATom flight tracks. The Atmospheric Tomography Mission (ATom) is a NASA Earth Venture Suborbital-2 mission studying the impact of human-produced air pollution on greenhouse gases and on chemically reactive gases in the atmosphere. The airborne observations were collected using the Quantum Cascade Laser System (QCLS) instrument, a high-frequency laser spectroscopy instrument for in situ atmospheric gas sampling. This dataset provides a direct comparison of observational and simulated CO that will be used to inform future atmospheric modeling experiments. The dataset also contains simulated tagged-CO tracer concentrations, which represent the contribution of specific regional sources to the total simulated CO. This dataset contributes to one of the ATom mission objectives to create an observation-based chemical climatology of important atmospheric constituents and their reactivity in the remote troposphere.

ATom_Organic_Aerossols_1795

This dataset provides airborne in situ observations of submicron organic aerosol (OA) mass concentrations during the first (mid-2016) and second (early-2017) global deployments of the Atmospheric Tomography Mission (ATom), as well as modeled submicron OA mass concentrations along the flight tracks from global chemistry models that implement a variety of commonly used representations of OA sources and chemistry. In situ observations include non-refractory submicron aerosols measured by the High-Resolution Aerosol Mass Spectrometer (HR-AMS), aerosol volume concentrations measured by the Aerosol Microphysical Properties package (AMP), black carbon mass content measured by the Single Particle Soot Photometer (NOAA SP2), and refractory and non-refractory aerosol composition measured by the Particle Analysis By Laser Mass Spectrometry (PALMS). Both observed and modeled data are provided at a 60-second temporal resolution. The data are provided in netCDF format.

ATom_Ozonesonde_InstrumentData_1910

This dataset contains ozone measurements from the Ozonesonde instrument in Antarctica, Hawaii, and Fiji taken during the Atom-4 campaign. The Electrochemical Concentration Cell (ECC) Ozonesonde is a balloon-borne instrument that collects ozone concentrations paired with a radiosonde to collect additional meteorological info along a vertical profile (as a result, unlike other ATom data, this dataset is not associated with DC-8). The balloon can ascend to altitudes of 35 km before bursting. Ozone in the stratosphere helps reduce UV radiation that reaches Earth's surface; however, ozone at ground level can negatively influence respiratory health.

ATom_Simulated_Data_1597

This dataset provides a simulated data stream representative of an Atmospheric Tomography mission (ATom) data collection flight and also modeled reactivities for ozone (O3) production and loss and methane (CH4) loss from six global atmospheric chemistry models: CAM, GEOS-Chem, GFDL, GISS-E2.1, GMI, and UCI. The simulated data include concentrations of selected atmospheric trace gases for 14,880 air parcels along a simulated north-south ATom flight path along 180-degrees longitude over the Pacific basin. Each of the six models produced ozone production and loss and methane loss reactivities initialized using the simulated data beginning with five different days in August (8-01, 8-06, 8-11, 8-16, 8-21). Modeled years for each individual model varied from 1997 to 2016.

ATom_SO2_LIF_Instrument_Data_1890

This dataset provides concentrations of sulfur dioxide (SO2) measured by the Laser Induced Fluorescence Instrumentation for Sulfur Dioxide (SO2-LIF) on the ATom-4 campaign in April and May 2018. The LIF-SO2 instrument detects SO2 at the single-part per trillion level using red-shifted laser-induced fluorescence. Measurements are reported at 1-second intervals along the flight paths. Sources of SO2 atmosphere from natural sources include volcanic eruptions and wildfires; however, most anthropogenic sources, such as fossil fuel combustion, arise. SO2 influences some negative health and environmental impacts and is an important precursor of aerosols in the nucleation of new particles globally.

ATom_PANTHER_Instrument_Data_1914

This dataset contains measurements of various trace gases from the PAN and Trace Hydrohalocarbon ExpeRiment (PANTHER) across the four ATom campaigns. PANTHER uses Electron Capture Detection and Gas Chromatography (ECD-GC) and Mass Selective Detection and Gas Chromatography (MSD-GC) to measure numerous trace gases, including methyl halides, HCFCs, PAN, N2O, SF6, CFC-12, CFC-11, Halon 1211, methyl chloroform, carbon tetrachloride.

ATom_UHSAS_Data_1619

This dataset provides extensive calibration and in-flight performance data for two Ultra-High Sensitivity Aerosol Spectrometers (UHSAS) used for particle size distribution and volatility measurements during the NASA Atmospheric Tomography Mission (ATom) airborne campaign. UHSAS-1 was equipped with a compact thermodenuder operating at 300 degrees C and UHSAS-2 was operated without a thermodenuder to determine the number and volume fraction of volatile particles. Laboratory studies utilized aerosols from limonene ozonolysis (limon), atomization of ammonium sulfate (AS), and atomization of 2-diethylhexyl (dioctyl) sebacate (DOS). Data include: UHSAS detection efficiency, sizing calibration, performance at a range of pressures and at a range of thermodenuder temperatures, comparison of UHSAS-2 and condensation particle counter (CPC) particle number concentrations, comparisons of UHSAS-1 and UHSAS-2 for dry particle number concentration, surface area and volume collected onboard of a NASA DC-8 aircraft during August 2016, and dry aerosol size distributions for thermodenuded and non-thermodenuded instrument collected in February 2017.

Aerosol_Sulfate_LowermostStrat_1868

This dataset consists of (a) selected aerosol and gas-phase observations made on all four deployments of NASA Atmospheric Tomography Mission (ATom), (b) thermodynamic properties related to aerosol formation derived from these measurements, (c) 48-h back trajectories for ATom-4 observations, and (d) output from the Model of Aerosols and Ions in the Atmosphere (MAIA). ATom observations, thermodynamics, and back trajectories were inputs for MAIA model runs. MAIA runs focused on data from ATom-4 deployment, and output includes aerosol formation rates, and ultrafine particle size distributions and number concentrations in the lowermost stratosphere (LMS). ATom 1-4 deployments included all four seasons from 2016 to 2018. This investigation sought to understand how new particle formation (NPF) can occur in the LMS, factors influencing the amount of NPF, and other potential sources of ultrafine aerosols in this region of the atmosphere. The data are provided in comma-separated value (CSV) format.

ATom_TOGA_Instrument_Data_V2_1936

This dataset provides concentrations of volatile organic compounds (VOCs) measured by the Trace Organic Gas Analyzer (TOGA) during the four ATom campaigns. These data are relevant to the impact of human-produced air pollution on greenhouse gases and on chemically reactive gases in the atmosphere. Specific data were obtained for radical precursors, tracers of anthropogenic and biogenic activities, tracers of urban and biomass combustion emissions, products of oxidative processing, precursors to aerosol formation, and compounds important for aerosol modification and transformation. TOGA measures a wide range of VOCs with high sensitivity (ppt or lower), frequency (2-minutes), accuracy (often 15% or better), and precision (<3%).

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NASA AVIRIS Project

This dataset provides attributed geospatial and tabular information for identifying and querying flight lines of interest for the Airborne Visible InfraRed Imaging Spectrometer-Classic (AVIRIS-C) and Airborne Visible InfraRed Imaging Spectrometer-Next Generation (AVIRIS-NG) Facility Instrument collections. It includes attributed shapefile and GeoJSON files containing polygon representation of individual flights lines for all years and separate KMZ files for each year. These files allow users to visualize and query flight line locations using Geographic Information System (GIS) software. Tables of AVIRIS-C and AVIRIS-NG flight lines with attributed information include dates, bounding coordinates, site names, investigators involved, flight attributes, associated campaigns, and corresponding file names for associated L1B (radiance) and L2 (reflectance) files in the AVIRIS-C and AVIRIS-NG Facility Instrument Collections. Tabular information is also provided in comma-separated values (CSV) format.

AV3_L1B_RDN_2356

This dataset contains Level 1B (L1B) calibrated radiance images as well as observational geometry and illumination parameters from the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-3 is a spectral mapping system that measures reflected radiance at 7.4-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 390-2500 nm. The AVIRIS-3 sensor has a 40 degree instantaneous field of view with 1234 pixels, providing altitude dependent ground sampling distances from 12 m to sub meter range. This spectrometer measures radiance from surface and atmosphere and is extremely similar in design to the orbital Earth Surface Mineral Dust Source Investigation (EMIT) spectrometer. AVIRIS-3 has been designed to fly on a variety of aircraft platforms including the King Air B-200, Gulfstream III, Gulfstream V, and ER-2. For each flight line, two file types are included: calibrated radiance (RDN) and orthocorrected observation geometry and illumination (ORT) in netCDF format. Both file types include a geolocation lookup table (GLT) for georeferencing pixels in UTM and geographic coordinates. A band mask file indicates whether wavelengths were interpolated on a per pixel basis. In addition, ancillary files for each flight line are provided, including a quick look image in JPEG format and text files in YAML format that document processing algorithms and parameters used during production.

AV3_L2A_RFL_2357

This dataset contains Level 2A (L2A) surface reflectance images from the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-3 is a spectral mapping system that measures reflected radiance at 7.4-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 390-2500 nm. Surface hemispherical directional reflectance was derived from calibrated radiance using an optimal estimation algorithm. For each flight line, two file types are included: orthocorrected surface reflectance (RFL_ORT) and orthocorrected reflectance uncertainty (UNC_ORT) in netCDF format. Both file types include data projected in a UTM coordinate system. In addition, ancillary files for each flight line are provided, including a quick look image in GeoTIFF format and text files in YAML format that document processing algorithms and parameters used during production.

AV3_L2B_GHG_2358

This dataset contains Level 2B (L2b) enhancements of greenhouse gasses (GHG) derived from imagery collected by the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument. Products include methane and carbon dioxide enhancements, each with per-pixel uncertainties and sensitivities to the background. Concentration enhancements are estimated from radiance measurements using a column-wise adaptive matched filter approach, which searches each pixel's radiance spectrum for deviations that are characteristic of a GHG's absorption spectrum. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-3 is a spectral mapping system that measures reflected radiance at 7.4-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 390-2500 nm.

AVIRIS-Classic_L1B_Radiance_2155

This dataset contains Level 1B (L1B) orthocorrected, scaled radiance image files as well as files of observational geometry and illumination parameters and supporting sensor band information from the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS-Classic) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-Classic is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-Classic measures reflected radiance in 224 contiguous bands at approximately 10-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 400-2500 nm. The AVIRIS-Classic sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub meter range. AVIRIS-Classic is flown on a variety of aircraft platforms including the Twin Otter, NASA's WB-57, and NASA's high altitude ER-2. Multiple file types are included for each flight line. The primary data files include: orthocorrected calibrated radiance image (img) files, geometric lookup table (glt) and orthocorrected observation geometry and illumination (obs_ort) files. Also included are unprojected files of input geometry (igm), and parameters relating to the geometry of observation and illumination (obs). Additional files provide information on spectral (spc) and radiometric calibration (rcc, gain), spatial resolution (geo), aircraft and sensor position (eph, nav), deployment notes (info), and data processing (plog). Quicklook images (jpeg) and polygon outlines of imagery footprints (kmz) are provided for each flight line. The primary AVIRIS-Classic L1B data are provided in ENVI binary format, which includes a flat binary file accompanied by a header (.hdr) file holding metadata in text format. The ancillary files include JPEG images, maps in Keyhole Markup Language (KML), and calibration files in binary and text formats. This archive currently includes data from 2004, and 2006 - 2025. Additional L1B data will be added as they become available. AVIRIS-Classic supports NASA Science and applications in many areas including plant composition and function, geology and soils, greenhouse gas mapping, and calibration of orbital platforms.

AVIRIS-Classic_L2_Reflectance_2154

This dataset contains Level 2 (L2) orthocorrected reflectance from the Airborne Visible / Infrared Imaging Spectrometer (AVIRIS-Classic) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-Classic is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-Classic measures reflected radiance in 224 contiguous bands at approximately 10-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 400-2500 nm. The AVIRIS-Classic sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub meter range. AVIRIS-Classic is flown on a variety of aircraft platforms including the Twin Otter, NASA's WB-57, and NASA's high altitude ER-2. For each flight line, two types of L2 data files may be included: (a) calibrated surface reflectance and (b) water vapor and optical absorption paths for liquid water and ice. The L2 data are provided in ENVI format, which includes a flat binary file accompanied by a header (.hdr) file holding metadata in text format. This archive currently includes data from 2008 - 2025. Additional AVIRIS-Classic facility instrument L2 data will be added as they become available. AVIRIS-Classic supports NASA Science and applications in many areas including plant composition and function, geology and soils, greenhouse gas mapping, and calibration of orbital platforms.

AVIRIS-NG_L1B_radiance_2095

This dataset contains Level 1B (L1B) orthocorrected, scaled radiance image files as well as files of observational geometry and illumination parameters and supporting sensor band information from the Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The NASA AVIRIS-NG is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-NG measures reflected radiance at 5-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 380-2510 nm. The AVIRIS-NG sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub-meter range. In this dataset, for each flight line, six file types are included: orthocorrected calibrated radiance image (img) files, geometric lookup table (glt) and orthocorrected observation geometry and illumination (obs_ort) files. Also included are unprojected files of input geometry (igm), parameters relating to the geometry of observation and illumination (obs), and orthocorrected locations of each pixel (loc). In addition, ancillary files for the flight line are provided, including quick look images and polygon outlines of imagery footprints. The AVIRIS-NG L1B data are provided in ENVI binary format, which includes a flat binary file accompanied by a header (.hdr) file holding metadata in text format. The ancillary files include JPEG images and maps in Keyhole Markup Language (KML). The AVIRIS-NG is flown on a variety of aircraft platforms including the Twin Otter, the King Air B-200, and NASA's high altitude ER-2. This archive currently includes data from 2014 - 2022. Additional AVIRIS-NG facility instrument L1B data will be added as they become available. AVIRIS-NG supports NASA Science and applications in many areas including plant composition and function, geology and soils, greenhouse gas mapping, and calibration of orbital platforms.

AVIRIS-NG_L2_Reflectance_2110

This dataset contains Level 2 (L2) orthocorrected reflectance from the Airborne Visible / Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) instrument. This is the NASA Earth Observing System Data and Information System (EOSDIS) facility instrument archive of these data. The AVIRIS-NG is a pushbroom spectral mapping system with high signal-to-noise ratio (SNR), designed and toleranced for high performance spectroscopy. AVIRIS-NG measures reflected radiance at 5-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 380-2510 nm. The AVIRIS-NG sensor has a 1 milliradian instantaneous field of view, providing altitude dependent ground sampling distances from 20 m to sub-meter range. For each flight line, two types of L2 data files may be included: (a) calibrated surface reflectance and (b) water vapor and optical absorption paths for liquid water and ice. The L2 data are provided in ENVI format, which includes a flat binary file accompanied by a header (.hdr) file holding metadata in text format. The AVIRIS-NG is flown on a variety of aircraft platforms including the Twin Otter, the King Air B-200, and NASA's high altitude ER-2. This archive currently includes data from 2014 - 2022. Additional AVIRIS-NG facility instrument L2 data will be added as they become available. The AVIRIS-NG supports NASA Science and applications in many areas including plant composition and function, geology and soils, greenhouse gas mapping, and calibration of orbital platforms.

AVIRIS-NG_CH4_CO2_Plumes_2406

This dataset contains enhanced column-integrated methane (CH4) and carbon dioxide (CO2) (concentration lengths) acquired from 211 flight lines across North America imaged by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) between June 26 and August 15, 2023 for NASA's Synergistic TEMPO Air Quality Science (STAQS) campaign. Enhanced CH4 and CO2 concentrations were estimated from the L1B radiance measurements using a cluster matched filter approach. This algorithm searches each pixel's radiance spectrum for deviations that are characteristic of a greenhouses gas's absorption spectrum. A total of 177 greenhouse gas plumes were identified and delineated, of which 74 were CH4 and 103 were CO2. The data are provided in GeoTIFF and JSON formats.

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NASA AVISO Project

This dataset contains absolute dynamic topography (similar to sea level but with respect to the geoid) binned and averaged monthly on 1 degree grids. The coverage is from October 1992 to December 2010. These data were provided by AVISO (French space agency data provider) to support the CMIP5 (Coupled Model Intercomparison Project Phase 5) under the World Climate Research Program (WCRP) and was first made available via the JPL Earth System Grid. The dynamic topography are derived from sea surface height measured by several satellites including Envisat, TOPEX/Poseidon, Jason-1 and OSTM/Jason-2, and referenced to the geoid. Along with this dataset, two additional ancillary data files are included in the same directory which contain the number of observations and standard error co-located on the same 1 degree grids.

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NASA AVUELO Project

An inventory of NASA's airborne and field campaigns for Earth Science

AVUELO_AV3_L2A_Reflectance_2479

This dataset contains Level 2A (L2A) orthocorrected surface reflectance and uncertainty estimates from the Airborne Visible / Infrared Imaging Spectrometer-3 (AVIRIS-3) instrument acquired for the Airborne Validation Unified Experiment: Land to Ocean (AVUELO) project in 2025. This L2A data product was generated with a provisional calibration algorithm. AVUELO aims to advance the validation and calibration of spaceborne imaging spectroscopy (hyperspectral) data for tropical ecosystems by combining airborne imaging spectroscopy for terrestrial and marine sites in Panama and Costa Rica with contemporaneous field measurements and data collected in February 2025. The NASA AVIRIS-3 is a spectral mapping system that measures reflected radiance at ~7.4-nm intervals in the Visible to Shortwave Infrared (VSWIR) spectral range from 390-2500 nm. The AVIRIS-3 sensor has a 40 degree instantaneous field of view with 1,234 pixels, providing altitude dependent ground sampling distances from 12 m to sub meter range. This spectrometer measures radiance from surface and atmosphere and is identical in design to the orbital Earth Surface Mineral Dust Source Investigation (EMIT) spectrometer. AVIRIS-3 was deployed on a King Air aircraft. Surface hemispherical directional reflectance was derived from calibrated radiance using an optimal estimation algorithm. For each flight scene, two file types are included: orthocorrected surface reflectance (RFL_ORT) and orthocorrected reflectance uncertainty (UNC_ORT) in netCDF format. Both file types include data projected in a UTM coordinate system. When available, a version 2 calibration Level 2A (L2A) of orthocorrected surface reflectance and uncertainty data for these same flight lines will be available in an AVIRIS-3 L2A Orthocorrected Surface Reflectance, Facility Instrument Collection, Version 2.

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NASA AfriSAR Project

This data set contains geotagged images collected over Gabon, Africa. The images were taken by the NASA Digital Mapping Camera paired with the Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of a NASA campaign, in collaboration with the European Space Agency (ESA) mission AfriSAR.

AFLVIS1B

This data set contains return energy waveform data over Gabon, Africa. The measurements were taken by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of a NASA campaign, in collaboration with the European Space Agency (ESA) mission AfriSAR.

AFLVIS2

This data set contains surface elevation data over Gabon, Africa. The measurements were taken by the NASA Land, Vegetation, and Ice Sensor (LVIS), an airborne lidar scanning laser altimeter. The data were collected as part of a NASA campaign, in collaboration with the European Space Agency (ESA) mission AfriSAR.

AfriSAR_AGB_Maps_1681

This dataset provides gridded estimates of aboveground biomass (AGB) for four sites in Gabon at 0.25 ha (50 m) resolution derived with field measurements and airborne LiDAR data collected from 2010 to 2016. The sites represent a mix of forested, savannah, and some agricultural and disturbed landcover types: Lope site, within Lope National Park; Mabounie, mostly forested site; Mondah Forest, protected area; and the Rabi forest site, part of the Smithsonian Institution of Global Earth Observatories world-wide network of forest plots. Plot-level biophysical measurements of tree diameter and tree height (or estimated by allometry) were performed at 1 ha and 0.25 ha scales on multiple plots at each site and used to derive AGB for each tree and then summed for each plot. Aerial LiDAR scans were used to construct digital elevation models (DEM) and digital surface models (DSM), and then the DEM and DSM were used to construct a canopy height model (CHM) at 1 m resolution. After checking site-plot locations against the CHM, mean canopy height (MCH) was computed over each 0.25 ha. A single regression model relating MCH and AGB estimates, incorporating local height based on the trunk DBH (HD) relationships, was produced for all sites and combined with the CHM layer to construct biomass maps at 0.25 ha resolution.

AfriSAR_LVIS_Footprint_Cover_1591

This dataset includes footprint-level canopy structure products derived from data collected using NASA's Land, Vegetation, and Ice Sensor (LVIS) during flights over five forested sites in Gabon during February and March 2016. Three types of canopy structure information are included for each flight: 1) vertical profiles of canopy cover fraction in 1-meter bins, 2) vertical profiles of plant area index (PAI) in 1-meter bins, and 3) footprint summary data of total recorded energy, leaf area index, canopy cover fraction, and vertical foliage profiles in 10-meter bins. Canopy structure metrics are provided for each waveform (20-m footprint) collected by the LVIS instrument. These data were collected by NASA as part of the AfriSAR project. AfriSAR is a NASA collaboration with the European Space Agency (ESA), German Aerospace Center (DLR), and the Gabonese Space Agency (AGEOS) that is collecting data useful for deriving forest canopy structure and will help prepare for and calibrate current and upcoming spaceborne missions that aim to gauge the role of forests in Earth's carbon cycle.

Polarimetric_CT_1601

This dataset contains forest vertical structure and associated uncertainty products derived by applying multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) and Polarimetric Coherence Tomographic SAR (PCT or PC-TomoSAR) techniques. The data were collected from multiple repeat-pass flights over Gabonese forests using the Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) instrument in February-March 2016. In addition, supplementary data products based on various intermediate parameters of the UAVSAR data are provided and include radar backscatter, coherence, and viewing and terrain geometry. These data were collected by NASA as part of the joint NASA/ESA AfriSAR campaign.

Afrisar_LVIS_Biomass_VProfiles_1775

This dataset contains gridded forest characterization products derived from full-waveform lidar data acquired by NASA's airborne Land, Vegetation, and Ice Sensor (LVIS) instrument for five forested sites in Gabon, Africa, during the 2016 NASA-ESA AfriSAR campaign. The LVIS lidar instrument was flown over study sites in Lope, Mondah/Akanda, Pongara, Rabi, and Mabouni from February to March 2016. Derived canopy cover, canopy heights, bare ground elevation, plant area index (PAI), and foliage height diversity (FHD), and respective uncertainties are provided at a 25 m resolution for each of the five study sites. Aboveground biomass density (AGBD) and uncertainty were modeled at 50 m and 100 m resolutions for the Lope, Mondah, and Mabounie sites using field inventory data and waveform height and cover metrics. Lidar grid cell data collection statistics (i.e., number of shots and flight lines) and a data mask are also included. This research leverages high-quality forest inventory datasets collected during the AfriSAR campaign for one of the least studied and most unique forest ecosystems in the world.

AfriSAR_Mondah_Field_Data_1580

This dataset provides plot-level estimates of basal area, aboveground biomass, number of trees, maximum tree height, and basal-area-weighted wood specific gravity that were derived from observations of nearly 6,700 individual trees including tree family, species, DBH, the height of each tree, and their x, y location within 25 x 25 m subplots. These field data were collected from 15 1-hectare plots located across the Mondah Forest of Gabon as part of the AfriSAR Campaign in 2016. These biophysical and biomass data were used for training models to derive the AfriSAR remote sensing-based aboveground biomass products.

Polarimetric_height_profile_1577

This dataset provides height profiles derived from UAVSAR (Uninhabited Aerial Vehicle Synthetic Aperture Radar; JPL) data acquired over Lope National Park and Rabi Forest in Gabon as part of the AfriSAR campaign in 2016. These data were produced using synthetic aperture radar tomography (TomoSAR), a method that reveals three-dimensional forest structures by extending the conventional two-dimensional imaging capabilities of radars using multiple images acquired from slightly different antenna positions. AfriSAR was an airborne campaign that collected radar, lidar, and field measurements of forests in Gabon, West Africa, as part of a collaborative mission between NASA, the European Space Agency, and the Gabonese Space Agency. These data will help prepare for and calibrate current and upcoming spaceborne missions that aim to gauge the role of forests in Earth's carbon cycle, such as the Global Ecosystem Dynamics Investigation (GEDI).

PolInSAR_Canopy_Height_1589

This dataset provides estimates of forest canopy height and canopy height uncertainty for study areas in the Pongara National Park and the Lope National Park, Gabon. Two canopy height products are included: 1) Canopy height was derived from multi-baseline Polarimetric Interferometric Synthetic Aperture Radar (PolInSAR) data using an inversion of the random volume over ground (RVoG) model and Kapok, an open source Python library. 2) Canopy height was derived from a fusion of PolInSAR and Land, Vegetation, and Ice Sensor (LVIS) Lidar data. This dataset also includes various intermediate parameters of the PolInSAR data (including radar backscatter, coherence, and viewing and terrain geometry) which provide additional insight into the input data used to invert the RVoG model and accuracy of the canopy height estimates. The AfriSAR campaign was flown from 2016-02-27 to 2016-03-08. AfriSAR data were collected by NASA, in collaboration with the European Space Agency (ESA) and the Gabonese Space Agency.

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NASA AirMOSS Project

This dataset provides in situ measurements of soil temperature, moisture, conductivity, measured diameter of tree at breast height (DBH) and total height collected at the Harvard Forest, Petersham, Massachusetts, USA, during October 2012 and July - August 2013. These measurements were collected in support of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project to validate root-zone soil measurements and carbon flux model estimates.

AirMOSS_L1_Sigma0_BERMS_1406

This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the BERMS (Boreal Ecosystem Research and Monitoring Sites), in Saskatchewan, Canada. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.

AirMOSS_L1_Sigma0_Chamel_1407

This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Chamela Biological Station, in Jalisco, Mexico. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.

AirMOSS_L1_Sigma0_DukeFr_1408

This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Duke Forest site in North Carolina. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.

AirMOSS_L1_Sigma0_Harvrd_1409

This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Harvard Forest site in Massachusetts. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.

AirMOSS_L1_Sigma0_Howlnd_1410

This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Howland Forest site in Maine. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.

AirMOSS_L1_Sigma0_LaSelv_1411

This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the La Selva Biological Station in Costa Rica. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.

AirMOSS_L1_Sigma0_Metoli_1412

This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Metolius site in Oregon. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.

AirMOSS_L1_Sigma0_Moisst_1413

This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the MOISST site in Oklahoma. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.

AirMOSS_L1_Sigma0_TonziR_1414

This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Tonzi Ranch site in California. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.

AirMOSS_L1_Sigma0_Walnut_1415

This data set provides level 1 (L1) polarimetric radar backscattering coefficient (sigma-0), multilook complex, polarimetrically calibrated, and georeferenced data products from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over the Walnut Gulch site in Arizona. The AirMOSS radar is a P-band (UHF) fully polarimetric synthetic aperture radar (SAR) currently operating in the 420-440 MHz band designed to measure root-zone soil moisture (RZSM) and is flown on a NASA Gulfstream-III aircraft. Flight campaigns took place at least biannually from 2012 to 2015 at 10 study sites across North America. The acquired L1 P-band radar backscatter data will be used to retrieve the RZSM at the study sites. Subsequent analyses will investigate both seasonal and inter-annual variability in soil moisture and the relationships to carbon fluxes and their associated uncertainties on a continental scale.

AirMOSS_L2_Carbon_Flux_1420

This data set contains carbon flux measurements recorded by an aircraft at the Duke, Harvard, and Howland Forest sites during the summers of 2012-2014 as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Frequent measurements of CO2 and H2O were obtained using a cavity ring down spectrometer on board the Airborne Laboratory for Atmospheric Research, operated by Purdue University. Estimates of surface CO2 flux, sensible and latent heat fluxes, their corresponding uncertainties, and average wind speed and direction are provided for each of the 26 flights.

AirMOSS_L2_Inground_Soil_Moist_1416

This data set provides level 2 (L2) hourly volumetric (cm3/cm3) soil moisture profiles from in-ground sensors at seven North American sites as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Three profiles were installed at each site, sampling at seven different depths per profile (2 cm to 80 cm). Initial sampling began at three sites in September 2011 and additional sites were added during 2012 and 2013. All sampling concluded in December 2015. The AirMOSS project used an airborne radar instrument to estimate root-zone soil moisture at 10 study sites across North America. These in-ground soil moisture data were collected to calibrate and validate the AirMOSS data.

AirMOSS_L2_Precipitation_1417

This data set provides level 2 (L2) calibrated hourly precipitation (cm/hr) from rain gauges at seven North American sites as part of the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) project. Three gauges were installed at each site. Initial sampling began at three sites in September 2011 and additional sites were added during 2012 and 2013. All sampling concluded in December 2015. The AirMOSS project used an airborne radar instrument to estimate root-zone soil moisture at 10 study sites across North America. These precipitation data were collected in conjunction with in-ground soil moisture data in order to calibrate and validate the AirMOSS data.

AirMOSS_L2_3_RZ_Soil_Moisture_1418

This data set provides level 2/3 root zone soil moisture (RZSM) estimates at multiple depths at 90-m spatial resolution from the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) radar instrument collected over ten sites across North America. AirMOSS produces estimates of RZSM with data from a P-band synthetic aperture radar (SAR) flown on a NASA Gulfstream-III aircraft. The resulting soil moisture estimates capture the effects of gradients of soil, topography, and vegetation heterogeneity over an area of approximately 100km x 25km at each of the study sites. AirMOSS flight campaigns took place at least biannually from 2012 to 2015 at each site.

AirMOSS_L4_Daily_NEE_1422

This data set provides Level 4 daily estimates of Net Ecosystem Exchange (NEE) of CO2 at a spatial resolution of 30 arc-seconds (~1 km) for seven of the sites covered by the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) flights, each site spanning ~2500 km2. The daily NEE estimates are generally available from October 2012 through October 2014, although the exact time ranges vary by site. The AirMOSS L4 daily NEE were produced by the Ecosystem Demography Biosphere Model (ED2) augmented by the AirMOSS-derived L2/3 root zone soil moisture data as an additional input. The AirMOSS soil moisture data were used to estimate the sensitivity of carbon fluxes to soil moisture and to diagnose and improve estimation and prediction of NEE by constraining the model's predictions of soil moisture and its impact on above- and below-ground fluxes.

AirMOSS_L4_Regional_NEE_1423

This data set provides Level 4 estimates of Net Ecosystem Exchange (NEE) of CO2 across the conterminous USA at a spatial resolution of 50 km. Modeled estimates are provided at hourly and monthly temporal resolutions, from January 2012 through October 2014. The AirMOSS L4 Regional NEE data were produced by the Ecosystem Demography Biosphere Model (ED2) augmented by the AirMOSS-derived L2/3 root zone soil moisture data as an additional input. The AirMOSS soil moisture data were used to estimate the sensitivity of carbon fluxes to soil moisture and to diagnose and improve estimation and prediction of NEE by constraining the model's predictions of soil moisture and its impact on above- and below-ground fluxes.

AirMOSS_L4_RZ_Soil_Moisture_1421

This data set provides hourly gridded soil moisture estimates derived from hydrologic modeling at nine AirMOSS sites across North America. The AirMOSS L4 RZSM product represents a temporal interpolation of intermittent AirMOSS L2/3 RZSM retrievals into a temporally-continuous, multi-layer, hourly soil moisture product. The L4 RZSM data have the same spatial resolution (3-arcsecs or ~100 m), and the same temporal coverage (generally Fall 2012 through Fall 2015), as the underlying L2/3 RZSM data. The L4 RZSM data were produced by the integration of the Level 2/3 product and other ancillary information into the Penn State Integrated Hydrologic Model (PIHM). Many key applications for AirMOSS data products, including the calculation of net ecosystem exchange (NEE), require temporally continuous RZSM estimates such as those provided here.

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NASA Applications Technology Satellite Project

GVHRRATS6IMIR is the Geosynchronous Very High Resolution Radiometer (GVHRR) Black and White Infrared Images on 70mm Film data product from the sixth Applications Technology Satellite (ATS-6). This set of IR imagery (10.5 to 12.5 micrometer, with an 11 km footprint at the sub-satellite point) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title at the bottom of the image and a gray scale on the right boundary that represents brightness temperatures. The title contains the satellite identification, receiving station, spectral band, picture number, picture type, pixel scale, sector number, and date. The ATS-6 satellite was in a geosynchronous orbit parked at 95W viewing the hemisphere below the satellite. The GVHRR experiment returned data from launch until August 15, 1974 when it became inoperable, The PI was William E. Shenk from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00092 (old ID 74-039A-08B).

GVHRRATS6IMVIS

GVHRRATS6IMVIS is the Geosynchronous Very High Resolution Radiometer (GVHRR) Black and White Visible Images on Film data product from the sixth Applications Technology Satellite (ATS-6). This set of visible imagery (0.55 to 0.75 micrometer, with a 5.5 km footprint at the sub-satellite point) was originally produced on commercial image-generation equipment from digital tapes and was made available on 70-mm film, from which they were later scanned to digital TIFF image files. Each TIFF scan contains 2 or 3 pictures, and there are several hundred scans from an original 70 mm film roll which are combined into a ZIP file. Each picture contains a title at the bottom of the image and a gray scale on the right boundary that represents brightness temperatures. The title contains the satellite identification, receiving station, spectral band, picture number, picture type, pixel scale, sector number, and date. The ATS-6 satellite was in a geosynchronous orbit parked at 95W viewing the hemisphere below the satellite. The GVHRR experiment returned data from launch until August 15, 1974 when it became inoperable, The PI was William E. Shenk from NASA Goddard Space Flight Center. This product was previously available from the NSSDC with the identifier ESAD-00047 (old ID 74-039A-08A).

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NASA Aqua Project

AIRS is a facility instrument whose goal is to support climate research and improve weather forecasting Launched into Earth-orbit on May 4, 2002, the Atmospheric Infrared Sounder, AIRS, moves climate research and weather prediction into the 21st century.

AIRXAMAP

The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. The AIRS Granule Map Product consists of images of granule coverage in PDF and JPG format. The images are daily ones but updated every 6 minutes to capture any new available granule. Granules are assembled by ascending, descending, in north and south hemisphere, and the maps are in global cylindrical projection and satellite projection for better view.

AIRIBRAD

WARNING: On 2021/09/23 the EOS Aqua executed a Deep Space Maneuver (DSM). In the DSM, the spacecraft is turned such that the normal Earth field of regard is deep space. The thermal impact of the DSM caused a shift of the centroids of spectral response functions (SRF) of about 1% of the width of the SRF, equivalent to a frequency shift of 9 parts per million. This shift is reflected in the “spectral_freq” parameter (observed frequencies) in the L1b v5 files for each 6 minute granule. The magnitude of the effect on brightness temperatures (BT) depends on the spectral gradient of each channel. Maximum BT shifts are approximately +- 0.5 K, although many channels experience far smaller BT shifts. Approximately 1803 channels have BT shifts of less than 0.1 K and 575 channels are now shifted in BT by more than 0.1 K, while 231 of these channels have BT shifts greater than 0.2 K. Users of the L1b v5 product who are concerned that these shifts may impact their science investigations and applications are encouraged to switch to the AIRS L1c v6.7.4 product, which, among many other improvements, converts the spectra to a fixed frequency grid. END OF WARNING. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AIRS Infrared (IR) level 1B data set contains AIRS calibrated and geolocated radiances in milliWatts/m^2/cm^-1/steradian for 2378 infrared channels in the 3.74 to 15.4 micron region of t he spectrum. The AIRS instrument is co-aligned with AMSU-A so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. The AIRIBRAD_005 products are stored in files (often referred to as "granules") that contain 6 minutes of data, 90 footprints across track by 135 lines along track.

AIRABRAD_NRT

The AMSU-A Level 1B Near Real Time (NRT) product (AIRABRAD_NRT_005) differs from the routine product (AIRABRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. The AMSU-A instrument is co-aligned with AIRS so that successive blocks of 3 x 3 AIRS footprints are contained within one AMSU-A footprint. AMSU-A is primarily a temperature sounder that provides atmospheric information in the presence of clouds, which can be used to correct the AIRS infrared measurements for the effects of clouds. This is possible because non-precipitating clouds are for the most part transparent to microwave radiation, in contrast to visible and infrared radiation which are strongly scattered and absorbed by clouds. AMSU-A1 has 13 channels from 50 - 90 GHz and AMSU-A2 has 2 channels from 23 - 32 GHz. The AIRABRAD_NRT_005 products are stored in files (often referred to as "granules") that contain 6 minutes of data, 30 footprints across track by 45 lines along track.

AIRIBQAP_NRT

The AIRS Level 1B Near Real Time (NRT) product (AIRIBQAP_NRT_005) differs from the routine product (AIRIBQAP_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a facility instrument aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. AIRS data will be generated continuously. Global coverage will be obtained twice daily (day and night) on a 1:30pm sun synchronous orbit from a 705-km altitude. The AIRS IR Level 1B QA Subset contains Quality Assurance (QA) parameters that a user of may use to filter AIRS IR Level 1B radiance data to create a subset of analysis. QA parameters indicate quality of granule-per-channel, scan-per-channel, field of view, and channel and should be accessed before any data of analysis. It also contains "glintlat", "glintlon", and "sun_glint_distant" that users can use to check for possibility of solar glint contamination.

AIRVBRAD_NRT

The AIRS Visible/Near Infrared (VIS/NIR) Level 1B Near Real Time (NRT) product (AIRVBRAD_NRT_005) differs from the routine product (AIRVBRAD_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The AIRS VIS/NIR level 1B data set contains visible and near-infrared calibrated and geolocated radiances in W/m^2/micron/steradian for 4 channels in the 0.4 to 1.0 um region of the spectrum. The spectral range of the VIS/NIR channels are as follows: Channel 1 0.41 um - 0.44 um, Channel 2 0.58 um - 0.68 um, Channel 3 0.71 um - 0.92 um, Channel 4 0.49 um - 0.94 um. The AIRVBRAD_NRT_005 products are stored in files (often referred to as "granules") that contain 6 minutes of data, 90 footprints across track by 135 lines along track. The VIS/NIR granules are only produced in the daytime so there will always be fewer VIS/NIR granules than Infrared or microwave granules.

AIRVBQAP_NRT

The AIRS Level 1B Near Real Time (NRT) product (AIRVBQAP_NRT_005) differs from the routine product (AIRVBQAP_005) in 2 ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) Visible/Near Infrared (VIS/NIR) instrument in combination with the AIRS Infrared Spectrometer, the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB) constitute an innovative atmospheric sounding group aboard the Earth Observing System (EOS) Aqua platform in a near-polar Sun-synchronous orbit with a 1:30 AM/PM equator crossing time and an ~705 km altitude. The AIRS Visible/Near Infrared (VIS/NIR) Level 1B QA Subset contains Quality Assurance (QA) parameters that a may use of filter AIRS VIS/NIR Level 1B radiance data to create a subset of analysis. It includes "state" that user should check before using any VIS/NIR Level 1B data radiance and "glintlat", "glintlon", and "sun_glint_distant" that users can use to check for possibility of solar glint contamination. AIRS VIS/NIR Level 1B radiance data can be found in AIRVBRAD.

AIRS2CCF_NRT

The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) product (AIRS2CCF_NRT_006) differs from the routine product (AIRS2CCF_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file. The AIRS2CCF_NRT_006 products are stored in files (often referred to as "granules") that contain 6 minutes of data, 30 footprints across track by 45 lines along track.

AIRS2RET_NRT

The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) product (AIRS2RET_NRT_006) differs from the routine product (AIRS2RET_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.

AIRS2SUP_NRT

The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Support Retrieval (AIRS-only) product (AIRS2SUP_NRT_006) differs from the routine product (AIRS2SUP_006) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is product produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 scanlines. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.

AIRG2SSD_IRonly

The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This precipitation estimate from AIRS IR only is using a TOVS-like algorithm, and is intended for merging into the precipitation product of the Global Precipitation Climatology Project (GPCP). The precipitation estimate from AIRS Level 2 Support product, which are 6-min swath granules (240 per day) are combined here into one daily "Level 2G" global grid with dimensions (24x1440x720). Thus every hour is a "layer", and the resulting grid cell size is 0.25 degree (~25 km). Thus the grid size is made to fit TRMM products. Since AIRS precipitation is retrieved at AMSU footprint resolution, which is about 45 km at nadir, many grid cells in this 0.25-deg grid are "empty". The data are stored such that the first line is the South Pole. The geolocation information for every hour-layer is also provided in the file.

AIRX3C28

Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is the AIRS mid-tropospheric Carbon Dioxide (CO2) Level 3 8-day Gridded Retrieval, from the AIRS and AMSU instruments on board of Aqua satellite. It is 8-day gridded data, at 2.5x2 deg (lon)x(lat) grid cell size. The data is in mole fraction units (data x 10^6 =ppm in volume). This is a total tropospheric column property. The file format is HDF-EOS 2.12 corresponding to HDF4. This AIRS mid-tropospheric CO2 Level 3, 8-day, Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts as well as the latitude and longitude arrays giving the centers of the grid boxes. Each file covers an 8-day period. The mean values are simply the arithmetic means of the individual CO2 retrievals which fall within that grid box over the 8-day period. The mid-tropospheric CO2 retrievals have been averaged and binned into 2.5 x 2 deg grid cells, from -180.0 to +180.0 deg longitude and from -60.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean.

LPRM_AMSRE_SOILM2

AMSR-E/Aqua surface soil moisture (LPRM) L2B V002 is a Level 2 (swath) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), using the Land Parameter Retrieval Model (LPRM). Each swath is packaged with associated geolocation fields. The data set covers the period from June 2002 to October 2011 (when the AMSR-E on the NASA EOS Aqua satellite stopped producing data due to a problem with the rotation of its antenna), at the spatial resolution (nominally 56 and 38 km, respectively) of AMSR-E's C and X bands (6.9 and 10.7 GHz, respectively). The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR-E's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the AMSR-E resampled brightness temperatures (AE_L2A) product, archived at the National Snow and Ice Data Center (NSIDC).

LPRM_AMSRE_A_SOILM3

AMSR-E/Aqua surface soil moisture (LPRM) L3 1 day 25 km x 25 km ascending V002 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the daytime product. The data set covers the period from June 2002 to October 2011 (when the AMSR-E on the NASA EOS Aqua satellite stopped producing data due to a problem with the rotation of its antenna). The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR-E's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the AMSR-E resampled brightness temperatures (AE_L2A) product, daytime passes, as processed using LPRM (i.e., LPRM/AMSR-E/Aqua L2B product, LPRM_AMSRE_SOILM2_V002).

LPRM_AMSRE_D_SOILM3

AMSR-E/Aqua surface soil moisture (LPRM) L3 1 day 25 km x 25 km descending V002 is a Level 3 (gridded) data set. Its land surface parameters, surface soil moisture, land surface (skin) temperature, and vegetation water content, are derived from passive microwave remote sensing data from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), using the Land Parameter Retrieval Model (LPRM). There are two files per day, one ascending (daytime) and one descending (nighttime), archived as two different products. This document is for the nighttime product. The data set covers the period from June 2002 to October 2011 (when the AMSR-E on the NASA EOS Aqua satellite stopped producing data due to a problem with the rotation of its antenna). The LPRM is based on a forward radiative transfer model to retrieve surface soil moisture and vegetation optical depth. The land surface temperature is derived separately from the AMSR-E's Ka-band (36.5 GHz). A unique feature of this method is that it can be applied at any microwave frequency, making it very suitable to exploit all the available passive microwave data from various satellites. Input data are from the AMSR-E resampled brightness temperatures (AE_L2A) product, nighttime passes, as processed using LPRM (i.e., LPRM/AMSR-E/Aqua L2B product, LPRM_AMSRE_SOILM2_V002).

SNDRAQIL2JSFRET

The Joint Single Footprint Retrieval Algorithm (JoSFRA) Level-2 geophysical parameters include estimates of atmospheric temperature and water vapor profiles, cloud properties, and surface temperature. These are retrieved from infrared spectra observed by the Atmospheric Infrared Sounder (AIRS) instrument on the Aqua satellite. AIRS is a grating spectrometer aboard Aqua, the second Earth Observing System (EOS) polar-orbiting platform. AIRS is co-boresited with the Advanced Microwave Sounding Unit (AMSU) also on Aqua. Horizontal resolutions are 50 km for AMSU and 13.5 km for AIRS. The JoSFRA algorithm uses an optimal-estimation scheme and retrieves geophysical quantities from AIRS thermal infrared spectra at their native horizontal resolution. Cloud observations from MODIS are used in the forward model without recourse to a cloud-cleared state. JOSFRA retrievals provide improved spatial resolution (13.5 km vs 50 km for cloud-cleared retrievals) and information content quantification, making them well-suited for process studies. JoSFRA retrievals are particularly useful in cases where high resolution (finer than 45 km) is needed or is beneficial, such as regions of strong horizontal gradients in water vapor. Use of JoSFRA retrievals is recommended under medium to low cloud cover. AIRS observations provide near-global coverage twice daily (around 1:30 am and pm local time) since August 30, 2002. An AIRS granule includes 6 minutes of data, 90 AIRS (30 AMSU) footprints across the orbit track by 135 AIRS (45 AMSU) footprints along track. Each day includes 240 granules, with an orbit repeat cycle of approximately 16 days. For the initial release of Version 2 JoSFRA, a limited test data set is provided. Future releases will expand the dataset. The initial dataset includes full global coverage data for two 5-day periods: January 13-17, 2011 and July 13-17, 2011, the Marine ARM GPCI Investigation of Clouds (MAGIC) (Lewis and Teixeira, EOS, 2015) test campaign in the Pacific Ocean with all 6-minute granules that overlap the box bounded by 20-35 degrees North latitude and 120-160 West longitude, June 1, 2012 – September 30, 2013, select granules from the years 2002-2007 where correlative data were available. The locations include Dept. of Energy (DOE) Atmospheric Radiation Measurement (ARM) sites at the North Slope of Alaska (NSA), Southern Great Plains (SGP), and Tropical Western Pacific (TWP), as well as scientific field campaigns.

AIRSIL3MSOLR

This L3 Spectral Outgoing Longwave Radiation (OLR) is derived using the AIRS radiances to compute spectral fluxes based on an algorithm developed by Xianglei Huang at the University of Michigan. It uses data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft. The Aqua AIRS Huang Level-3 Spectral OLR product contains OLR parameters derived from the AIRS version 6 data: all-sky and clear-sky OLR both spectrally resolved at 10 cm-1 bandwidth and integrated to a single value per grid square. This is monthly product on a 2x2 degree latitude/longitude grid.

AER_DBDT_D10KM_L3_MODIS_AQUA

This High-Resolution (0.1 x 0.1 degree) Level 3 daily Aerosol Optical Depth (AOD) product is generated by combining two Moderate Resolution Imaging Spectroradiometer (MODIS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the AQUA satellite. This dataset is provided in daily files ranging from 2002-07-04 to the present. The spatial coverage is global and the dataset is gridded at 0.1 x 0.1 degree spatial resolution. The data are generated using Level 2 AOD retrieved using DT and DB algorithms. The product provides multiple options for using data either from DT or DB or combined. Depending on user need and application, they can choose one or more relevant parameter. The pixels with highest quality as recommended by science teams are only considered in these averaging. In addition to averaged AOD at 0.1 x 0.1 degree resolution, standard deviation and number of pixels averaged from each algorithm are also provided. Average sensor zenith angle is also provided for additional filtering of the data. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).

AER_DBDT_M10KM_L3_MODIS_AQUA

This High-Resolution (0.1 x 0.1 degree) Level 3 monthly Aerosol Optical Depth (AOD) product is generated by combining two Moderate Resolution Imaging Spectroradiometer (MODIS) operational algorithms, namely Deep Blue (DB) and Dark Target (DT), on board the AQUA satellite. This dataset is provided in monthly files ranging from July 2002 to the present. The spatial coverage is global and the dataset is gridded at 0.1 x 0.1 degree spatial resolution. The data are generated using Level 2 AOD retrieved using DT and DB algorithms. The product provides multiple options for using data either from DT or DB or combined. Depending on user need and application, they can choose one or more relevant parameter. The pixels with highest quality as recommended by science teams are only considered in these averaging. In addition to averaged AOD at 0.1 x 0.1 degree resolution, standard deviation and number of pixels averaged from each algorithm are also provided. Average sensor zenith angle is also provided for additional filtering of the data. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).

AIRX2RET

The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS combination with the Advanced Microwave Sounding Unit (AMSU) constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities are also be part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.

AIRX2SUP

AIRS2CCF_NRT

The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Cloud-Cleared Infrared Radiances (AIRS-only) product (AIRS2CCF_NRT_7.0) differs from the routine product (AIRS2CCF_7.0) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in AMSU channel 4 started to increase significantly (since June 2007). Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file. The AIRS2CCF_NRT_7.0 products are stored in files (often referred to as "granules") that contain 6 minutes of data, 30 footprints across track by 45 lines along track.

AIRS2RET_NRT

The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Standard Physical Retrieval (AIRS-only) product (AIRS2RET_NRT_7.0) differs from the routine product (AIRS2RET_7.0) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is produced using AIRS IR only because the radiometric noise in several AMSU channels started to increase significantly (since June 2007). The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. Estimates of the errors associated with these quantities is also part of the Standard Product. The temperature profile vertical resolution is 28 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at 14 atmospheric layers between 1100 mb and 50 mb. The horizontal resolution is 50 km. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.

AIRS2SUP_NRT

The Atmospheric Infrared Sounder (AIRS) Level 2 Near Real Time (NRT) Support Retrieval (AIRS-only) product (AIRS2SUP_NRT_7.0) differs from the routine product (AIRS2SUP_7.0) in four ways to meet the three hour latency requirements of the Land Atmosphere NRT Capability Earth Observing System (LANCE): (1) The NRT granules are produced without previous or subsequent granules if those granules are not available within 5 minutes, (2) the predictive ephemeris/attitude data are used rather than the definitive ephemeris/attitude, (3) if the forecast surface pressure is unavailable, a surface climatology is used, and (4) no ice cloud properties retrievals are performed. The consequences of these differences are described in the AIRS Near Real Time (NRT) data products document. The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is product produced using AIRS IR only because the radiometric noise in several AMSU channels started to increase significantly (since June 2007). The Support Product includes higher vertical resolution profiles of the quantities found in the Standard Product, plus intermediate outputs (e.g., microwave-only retrieval), research products such as the abundance of trace gases, and detailed quality assessment information. The Support Product profiles contain 100 levels between 1100 and .016 mb; this higher resolution simplifies the generation of radiances using forward models, though the vertical information content is no greater than that in the Standard Product profiles. The intended users of the Support Product are researchers interested in generating forward radiance or in examining research products, and the AIRS algorithm development team. The Support Product is generated at all locations as Standard Products. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 scanlines. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.

AIRX3STD

The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) AIRS constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Level 3 Daily Gridded Product contains standard retrieval means, standard deviations and input counts. Each file covers a temporal period of 24 hours for either the descending (equatorial crossing North to South at 1:30 AM local time) or ascending (equatorial crossing South to North at 1:30 PM local time) orbit. The data starts at the international dateline and progresses westward (as do the subsequent orbits of the satellite) so that neighboring gridded cells of data are no more than a swath of time apart (about 90 minutes). The two parts of a scan line crossing the dateline are included in separate L3 files, according to the date, so that data points in a grid box are always coincident in time. The edge of the AIRS Level 3 gridded cells is at the date line (the 180E/W longitude boundary). When plotted, this produces a map with 0 degrees longitude in the center of the image unless the bins are reordered. This method is preferred because the left (West) side of the image and the right (East) side of the image contain data farthest apart in time. The gridding scheme used by AIRS is the same as used by TOVS Pathfinder to create Level 3 products. The daily Level 3 products have gores between satellite paths where there is no coverage for that day. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. The value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the box. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.

AIRS3STD

The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS Level 3 Daily Gridded Product contains standard retrieval means, standard deviations and input counts. Each file covers a temporal period of 24 hours for either the descending (equatorial crossing North to South at 1:30 AM local time) or ascending (equatorial crossing South to North at 1:30 PM local time) orbit. The data starts at the international dateline and progresses westward (as do the subsequent orbits of the satellite) so that neighboring gridded cells of data are no more than a swath of time apart (about 90 minutes). The two parts of a scan line crossing the dateline are included in separate L3 files, according to the date, so that data points in a grid box are always coincident in time. The edge of the AIRS Level 3 gridded cells is at the date line (the 180E/W longitude boundary). When plotted, this produces a map with 0 degrees longitude in the center of the image unless the bins are reordered. This method is preferred because the left (West) side of the image and the right (East) side of the image contain data farthest apart in time. The gridding scheme used by AIRS is the same as used by TOVS Pathfinder to create Level 3 products. The daily Level 3 products have gores between satellite paths where there is no coverage for that day. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document. The value for each grid box is the sum of the values that fall within the 1x1 area divided by the number of points in the box.

AIRS3STM

The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS Only Level 3 Monthly Gridded Retrieval Product contains standard retrieval means, standard deviations and input counts. Each file covers a calendar month. The mean values are simply the arithmetic means of the daily products, weighted by the number of input counts for each day in that grid box. The geophysical parameters have been averaged and binned into 1 x 1 deg grid cells, from -180.0 to +180.0 deg longitude and from -90.0 to +90.0 deg latitude. For each grid map of 4-byte floating-point mean values there is a corresponding 4-byte floating-point map of standard deviation and a 2-byte integer grid map of counts. The counts map provides the user with the number of points per bin that were included in the mean and can be used to generate custom multi-day maps from the daily gridded products. The thermodynamic parameters are: Skin Temperature (land and sea surface), Air Temperature at the surface, Profiles of Air Temperature and Water Vapor, Tropopause Characteristics, Column Precipitable Water, Cloud Amount/Frequency, Cloud Height, Cloud Top Pressure, Cloud Top Temperature, Reflectance, Emissivity, Surface Pressure, Cloud Vertical Distribution. The trace gases parameters are: Total Amounts and Vertical Profiles of Carbon Monoxide, Methane, and Ozone. The actual names of the variables in the data files should be inferred from the Processing File Description document.

AIRSAC3MNH3

The mass concentration ammonia in the atmosphere, consists of products generated for the study of atmospheric ammonia. Atmospheric ammonia is an important component of the global nitrogen cycle. In the troposphere, ammonia reacts rapidly with acids such as sulfuric and nitric to form fine particulate matter. These ammonium containing aerosols affect Earth's radiative balance, both directly by scattering incoming radiation and indirectly as cloud condensation nuclei. Major sources of atmospheric ammonia involve agricultural activities including animal husbandry, especially concentrated animal feeding operations and fertilizer use. Major sinks of atmospheric ammonia involve dry deposition and wet removal by precipitation, as well as conversion to particulate ammonium by reaction with acids. Measurements of ambient NH3 are sparse, but satellites provide a means to monitor atmospheric composition globally. Using the AIRS/AMSU satellite this algorithm provides monthly measurements of derived atmospheric NH3 for September 2002 through August 2016.

MYDFDS_CLM_GLB_L3

Version 1 is the current version of the dataset. This collection MYDFDS_CLM_GLB_L3 provides level 3 climatological monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚. It is derived from Level 2, the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products Collection 6.1 from Aqua (MYD04_L2). The dataset is the climatological monthly mean for each month averaged over 2003 to 2022. The FDS is calculated as the number of days per month when the daily dust optical depth is greater than a threshold optical depth (e.g., 0.025) with two quality flags: the lowest (1) and highest (3). It is advised to use flag 1, which is of lower quality, over dust source regions, and flag 3 over remote areas or polluted regions. Eight thresholds (0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 1, 2) are saved separately in eight files. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).

MYD04_L2

The MODIS/Aqua Aerosol 5-Min L2 Swath 10km product (MYD04_L2) provides full global coverage of aerosol properties from the Dark Target (DT) and Deep Blue (DB) algorithms. The DT algorithm is applied over ocean and dark land (e.g., vegetation), while the DB algorithm now covers the entire land areas including both dark and bright surfaces. Both results are provided on a 10x10 pixel scale (10 km at nadir). Each MYD04_L2 product file covers a five-minute time interval. The output grid is 135 pixels in width by 203 pixels in length. Every tenth file has an output grid size of 135 by 204 pixels. MYD04_L2 product files are stored in Hierarchical Data Format (HDF-EOS). The new Collection 6.1 (C61) MYD04_L2 product is an improved version based on algorithm changes in Dark Target (DT) Aerosol retrieval over urban areas and uncertainty estimates for Deep Blue (DB) Aerosol retrievals. The MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties, quality assurance, and other parameters, globally over ocean and land. In Collection 5 and in earlier collections, there was only one aerosol product (MYD04_L2) at 10km (at nadir) spatial resolution. Starting from C6, the Dark Target (DT) Aerosol algorithm team provided a new 3 km spatial resolution product (MYD04_3k) intended for the air quality community. For more information visit the MODIS Atmosphere website at: https://modis-atmos.gsfc.nasa.gov/products/aerosol And, for C6.1 changes and updates, visit: https://modis-atmosphere.gsfc.nasa.gov/documentation/collection-61

MYD04_3K

The new Collection 6.1 (C61) MODIS/Aqua Aerosol 5 Min L2 Swath 3km (MYD04_3K) product is an improved version based on algorithm changes in Dark Target (DT) Aerosol retrieval over urban areas and uncertainty estimates for Deep Blue (DB) Aerosol retrievals. The MODIS level-2 atmospheric aerosol product provides retrieved ambient aerosol optical properties, quality assurance, and other parameters, globally over ocean and land. In Collection 5, and earlier collections, there was only one aerosol product (MYD04_L2) at 10km (at nadir) spatial resolution. Starting from C6, the Dark Target (DT) Aerosol algorithm team provided a new 3 km spatial resolution product (MYD04_3k) intended for the air quality community. The MYD04_3K product is based on the same algorithm and Look up Tables as the standard Dark Target aerosol product. Because of finer resolution, subtle differences are made in selecting pixels for retrieval and in determining QA. The only differences between the existing 10km algorithm and the new 3km algorithm are: 1) the size of the pixel-arrays defining each retrieval box ( 6x6 retrieval boxes of 36 pixels at 0.5km resolution for 3km algorithm as oppose to 20x20 retrieval boxes of 400 pixels at 0.5km resolution for 10km product); 2) the minimum percentage of "good" pixels required for a retrieval (a minimum of 5 pixels over ocean and 6 pixels over land instead of a minimum of 10 pixels over ocean or 12 pixels over land for 10km product retrieval); 3) the 10km algorithm attempts a "poor quality" retrieval while 3km algorithm does not. Everything else is the same between two products. For more information on C6.1 changes and updates, visit the MODIS Atmosphere website at: https://modis-atmosphere.gsfc.nasa.gov/documentation/collection-61

MYD08_E3

The MODIS/Aqua Aerosol Cloud Water Vapor Ozone 8-Day L3 Global 1Deg CMG product (MYD08_E3) contains 8-Day 1 degree x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter. The MYD08_E3 contains nearly 1000 statistical datasets (SDS's) that are derived from the Level-3 MODIS Atmosphere Daily Global Product. Statistics are computed over a 1 degree equal-angle lat-lon grid that spans an 8-Day interval. Since the grid cells are 1 degree by 1 degree, the output grid is always 360 pixels in width and 180 pixels in length. MYD08_E3 product files are stored in Hierarchical Data Format (HDF-EOS). Each gridded global parameter is stored as Scientific Data Sets (SDS) within the file. The MODIS 8-Day Product will be used in the simultaneously study of clouds, water vapor, aerosol , trace gases, land surface and oceanic properties, as well as the interaction between them and their effect on the Earth's energy budget and climate. This product will also be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial resolution. For more information about the MYD08_E3 product, please visit the MODIS-Atmosphere site at: https://modis-atmos.gsfc.nasa.gov/products/eight-day

MYD08_D3

The MODIS/Aqua Aerosol Cloud Water Vapor Ozone Daily L3 Global 1Deg CMG product (MYD08_D3) contains daily 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter. The MYD08_D3 contains roughly 600 statistical datasets that are derived from approximately 80 scientific parameters from four Level-2 MODIS Atmosphere Products: MOD04_L2, MOD05_L2, MOD06_L2, and MOD07_L2. Statistics are computed over a 1 degree equal-angle lat-lon grid that spans a 24-hour (0000 to 2400 Greenwich Mean Time) interval. Since the grid cells are 1 degree by 1 degree, the output grid is always 360 pixels in width and 180 pixels in length. MYD08_D3 product files are stored in Hierarchical Data Format (HDF-EOS). Each gridded global parameter is stored as Scientific Data Sets (SDS) within the file. The MODIS Daily Product will be used in the simultaneously study of clouds, water vapor, aerosol , trace gases, land surface and oceanic properties, as well as the interaction between them and their effect on the Earth's energy budget and climate. This product will also be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial resolution. For more information about the MYD08_D3 product, please visit the MODIS-Atmosphere site at: https://modis-atmos.gsfc.nasa.gov/products/daily

MYD08_M3

The MODIS/Aqua Aerosol Cloud Water Vapor Ozone Monthly L3 Global 1Deg CMG product (MYD08_M3) contains monthly 1 x 1 degree grid average values of atmospheric parameters related to atmospheric aerosol particle properties, total ozone burden, atmospheric water vapor, cloud optical and physical properties, and atmospheric stability indices. This product also provides standard deviations, quality assurance weighted means and other statistically derived quantities for each parameter. The MYD08_M3 contains roughly 800 statistical datasets that are derived from the Level-3 MODIS Atmosphere Daily Global Product. Statistics are sorted into 1x1 degree cells on an equal-angle grid that spans a (calendar) monthly interval and then summarized over the globe. MYD08_M3 product files are stored in Hierarchical Data Format (HDF-EOS). Each gridded global parameter is stored as Scientific Data Sets (SDS) within the file. The MODIS monthly Product will be used in the simultaneously study of clouds, water vapor, aerosol , trace gases, land surface and oceanic properties, as well as the interaction between them and their effect on the Earth's energy budget and climate. This product will also be used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial resolution. For more information about the MYD08_M3 product, please visit the MODIS-Atmosphere site at: https://modis-atmos.gsfc.nasa.gov/products/monthly

MYDATML2

The MODIS/Aqua Aerosol, Cloud and Water Vapor Subset 5-Min L2 Swath 5km and 10km (MYDATML2) product contains a combination of key high interest science parameters. The ATML2 product provides a subset of datasets from the suite of atmosphere team products on both a 10 km scale (aerosols) and 5km scale (native 5 km cloud properties and a 5x5 pixel sample of the 1km cloud datasets). The ATML2 product employs the same 5x5 pixel sampling scheme for the 1km native resolution Level 2 products as is used in the MOD08 Level 3 global aggregated product, an approach that has been shown to retain statistical integrity for multi-day aggregations. The C6 significantly increases the number of datasets included in the ATML2 product, including the full suite of QA datasets. Since the ATML2 data granule file size is significantly smaller than the combined size of the individual L2 products, and because the 1 km pixel sampling is consistent with the L3 algorithm, the ATML2 product is a more practical means for the user community to develop research L3 algorithms for their own specific purposes. For more information, visit the MODIS Atmosphere website at: https://modis-atmos.gsfc.nasa.gov/products/joint-atm

MYD09

The MODIS/Aqua Atmospherically Corrected Surface Reflectance 5-Min L2 Swath 250m, 500m, 1km (MYD09) product is computed from the MODIS Level 1B land bands 1, 2, 3, 4, 5, 6, and 7 (centered at 648 nm, 858 nm, 470 nm, 555 nm, 1240 nm, 1640 nm, and 2130 nm, respectively). The product is an estimate of the surface spectral reflectance for each band as it would have been measured at ground level if there were no atmospheric scattering or absorption. The surface-reflectance product is the input for product generation for several land products: vegetation Indices (VIs), Bidirectional Reflectance Distribution Function (BRDF), thermal anomaly, snow/ice, and Fraction of Photosynthetically Active Radiation/Leaf Area Index (FPAR/LAI).

MYD021KM

The MODIS/Aqua Calibrated Radiances 5Min L1B Swath 1km data set contains calibrated and geolocated at-aperture radiances for 36 discrete bands located in the 0.4 to 14.4 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance which during processing are converted to geophysical units of W / (m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. Visible, shortwave infrared, and near infrared measurements are only made during the daytime (except band 26), while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously. The shortname for this product is MYD021KM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical file size is approximately 115 MB. Environmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world. See the MODIS Characterization Support Team webpage for more C6 product information at: https://mcst.gsfc.nasa.gov/l1b/product-information or visit Science Team homepage at: https://modis.gsfc.nasa.gov/data/dataprod/

MYD02QKM

The MODIS/Terra Calibrated Radiances 5-Min L1B Swath 250m data set contains calibrated and geolocated at-aperture radiances for 2 discrete bands located in the 0.62 to 0.88 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance which during processing are converted to geophysical units of W / (m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. Separate L1B products are available for the five 500m resolution channels (MYD02HKM) and the twenty-nine 1km resolution channels (MYD021KM). For the 500m product, there are actually seven channels available since the data from the two 250 m channels have been aggregated to 500m resolution. Similarly, for the 1km product, all 36 MODIS channels are available since the data from the two 250m and five 500m channels have been aggregated into equivalent 1km pixel values. Spatial resolution for pixels at nadir is 250 m, degrading to 1.2 km in the along-scan direction and 0.5 km in the along-track direction for pixels located at the scan extremes. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 250 m granule will contain a scene built from 203 scans sampled 5416 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 40 along-track spatial elements for the 250 m channels, the scene will be composed of (5416 x 8120) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 17 degrees scan angle. The shortname for this product is MYD02QKM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical file size will be approximately 140 MB and the total daily volume is around 22GB. Environmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world. See the MODIS Characterization Support Team webpage for more C6.1 product information at: http://mcst.gsfc.nasa.gov/l1b/product-information or visit Science Team homepage at: http://modis.gsfc.nasa.gov/data/dataprod/

MYD02HKM

The MODIS/Aqua Calibrated Radiances 5Min L1B Swath 500m data set contains calibrated and geolocated at-aperture radiances for 7 discrete bands located in the 0.45 to 2.20 micron region of the electromagnetic spectrum. These data are generated from the MODIS Level 1A scans of raw radiance and in the process converted to geophysical units of W/(m^2 um sr). Additional data are provided including quality flags, error estimates and calibration data. Visible, shortwave infrared, and near infrared measurements are only made during the daytime (except band 26), while radiances for the thermal infrared region (bands 20-25, 27-36) are measured continuously. Channels 1 and 2 have 250 m resolution, channels 3 through 7 have 500 m resolution. However, for the MODIS L1B 500 m product, the 250 m band radiance data and their associated uncertainties have been aggregated to 500m resolution. Thus the entire channel data set has been co-registered to the same spatial scale in the 500 m product. Separate L1B products are available for the 250 m resolution channels (MYD02QKM) and 1 km resolution channels (MYD021KM). For the latter product, the 250 m and 500 m channel data (bands 1 through 7) have been aggregated into equivalent 1 km pixel values. Spatial resolution for pixels at nadir is 500 km, degrading to 2.4 km in the along-scan direction at the scan extremes. However, thanks to the overlapping of consecutive swaths and respectively pixels there, the resulting resolution at the scan extremes is about 1 km. A 55 degree scanning pattern at the EOS orbit of 705 km results in a 2330 km orbital swath width and provides global coverage every one to two days. A single MODIS Level 1B 500 m granule will contain a scene built from 203 scans sampled 2708 times in the cross-track direction, corresponding to approximately 5 minutes worth of data; thus 288 granules will be produced per day. Since an individual MODIS scan will contain 20 along-track spatial elements for the 500 m channels, the scene will be composed of (2708 x 4060) pixels, resulting in a spatial coverage of (2330 km x 2040 km). Due to the MODIS scan geometry, there will be increasing scan overlap beyond about 20 degrees scan angle. To summarize, the MODIS L1B 500 m data product consists of: 1. Calibrated radiances, uncertainties and number of samples for (2) 250 m reflected solar bands aggregated to 500 m resolution 2. Calibrated radiances and uncertainties for (5) 500 m reflected solar bands 3. Geolocation for 1km pixels, that must be interpolated to get 500 m pixel locations. For the relationship of 1km pixels to 500m pixels, see the Geolocation ATBD https://modis.gsfc.nasa.gov/data/atbd/atbd_mod28_v3.pdf. 4. Calibration data for all channels (scale and offset) 5. Comprehensive set of file-level metadata summarizing the spatial, temporal and parameter attributes of the data, as well as auxiliary information pertaining to instrument status and data quality characterization users requiring all geolocation and solar/satellite geometry fields at 1km resolution can obtain the separate MODIS Level 1 Geolocation product (MYD03) from LAADS https://ladsweb.modaps.eosdis.nasa.gov/ . The shortname for this product is MYD02HKM and is stored in the Earth Observing System Hierarchical Data Format (HDF-EOS). A typical MYD02HKM file size is approximately 135 MB. Environmental information derived from MODIS L1B measurements will offer a comprehensive and unprecedented look at terrestrial, atmospheric, and ocean phenomenology for a wide and diverse community of users throughout the world. See the MODIS Characterization Support Team webpage for more C6 product information at: https://mcst.gsfc.nasa.gov/l1b/product-information or visit Science Team homepage at: https://modis.gsfc.nasa.gov/data/dataprod/

CLDMSK_L2_MODIS_Aqua

The MODIS-VIIRS Cloud Mask (MVCM) is designed to facilitate continuity in cloud detection between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Suomi NPP spacecraft. To establish continuity, this MODIS MVCM product does not use an algorithm identical to that used in the standard MODIS product (MOD35/MYD35). The MVCM-MODIS Cloud Mask product is Aqua MOIDS Level-2, 5-Min Swath product generated at 1000 m (at nadir) spatial resolution. The algorithm employs a series of visible through infrared threshold and consistency tests to specify confidence that an unobstructed view of the Earth's surface has been observed. Radiometrically-accurate radiances are required, thus holes in the cloud mask will appear wherever the input radiances are incomplete or of poor quality. For more information consult Product Page at: https://cimss.ssec.wisc.edu/MVCM/

MYD35_L2

The MODIS/Aqua Cloud Mask and Spectral Test Results 5-Min L2 Swath 250m and 1km product consists of global cloud mask quality assurance and other ancillary parameters. The algorithm employs a series of visible and infrared threshold and consistency tests to specify confidence levels that an unobstructed view of the Earth's surface is observed. An indication of shadows affecting the scene is also provided. The 250-m cloud mask flags are based on the visible channel data only. Radiometrically accurate radiances are required, so holes in the cloud mask will appear wherever the input radiances are incomplete or of poor quality. The shortname for this Level-2 MODIS cloud mask product is MYD35_L2. The MYD35_L2 product files are stored in Hierarchical Data Format (HDF-EOS). This product consists of 9 parameters and each of these parameters are stored as a Scientific Data Set (SDS) within the HDF-EOS file. The Cloud Mask and Quality Assurance SDS's are stored at 1 kilometer pixel resolution. All other SDS's (those relating to time, geolocation, and viewing geometry) are stored at 5 kilometer pixel resolution. For more information about the MYD35_L2 product, visit the MODIS-Atmosphere site at: https://modis-atmos.gsfc.nasa.gov/products/cloud-mask

CLDPROP_L2_MODIS_Aqua

The MODIS/Aqua Cloud Properties 5-min L2 Swath 1km product is designed to facilitate continuity in cloud properties between the MODIS (Moderate Resolution Imaging Spectroradiometer) on the Aqua and Terra platforms and the series of VIIRS (Visible Infrared Imaging Radiometer Suite) instruments, beginning with the Suomi NPP spacecraft. To establish continuity, this MODIS Cloud Properties product does not use algorithms identical to those used in the standard MODIS product (MOD06/MYD06). The product consists of cloud optical and physical parameters derived using observations in visible through infrared spectral channels. MODIS infrared channels that are common with VIIRS are primarily used to derive cloud-top temperature, cloud-top height, effective emissivity, an infrared cloud phase product (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. The MODIS solar reflectances channels are primarily used to derive cloud optical thickness, particle effective radius, water path, and to inform the phase used in the optical retrievals. The MODIS Cloud Properties product is a Level-2 product generated at 1 km (at nadir) spatial resolution. The current version-1.1 of the Level-2 CLDPROP product collection is corrected to address an issue with the cloud optical properties’ thermodynamic phase that caused erroneous liquid water cloud phase results.

CLDPROPCOSP_D3_MODIS_Aqua

The MODIS/Aqua Cloud Properties COSP Level 3 daily, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_D3_MODIS_Aqua. It contains MODIS Aqua cloud mask, cloud top, and cloud optical retrieval data over daily timeframe. It provides a set of custom cloud-related parameters for better comparison with climate model output. The “COSP” acronym in the short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. Provided in netCDF4 format, it contains 32 aggregated science data sets (SDS/parameters). Consult the CLDPROPCOSP User Guide for details regarding how the L3 daily statistics are computed, and to learn more about the gridding and sampling protocols specific to this product and a number of other topics germane to the user community. The collection of this product starts from July 4, 2002 and includes 365 granules each calendar year.

CLDPROPCOSP_M3_MODIS_Aqua

The MODIS/Aqua Cloud Properties COSP Level 3 monthly, 1x1 degree grid product is a new L3 CLDPROP COSP Cloud product with short-name CLDPROPCOSP_M3_MODIS_Aqua. It contains MODIS Aqua cloud mask, cloud top, and cloud optical retrieval data over monthly timeframe. It provides a set of custom cloud-related parameters for better comparison with climate model output. The “COSP” acronym in the short-name stands for Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package. Provided in netCDF4 format, it contains 32 aggregated science data sets (SDS/parameters). Consult the CLDPROPCOSP User Guide for details regarding how the L3 daily statistics are computed, and to learn more about the gridding and sampling protocols specific to this product and a number of other topics germane to the user community. The collection of this product starts from August 1, 2002 and includes 12 granules each calendar year.

CLDPROP_D3_MODIS_Aqua

The Cloud Properties Level-3 gridded product is designed to facilitate continuity in cloud property statistics between the MODIS on the Aqua and Terra platforms and the common continuity products generated for the VIIRS (Visible Infrared Imaging Radiometer Suite) and the MODIS Aqua instruments. CLDPROP Level-3 statistical routines include scalar and histograms (1-D and 2-D) that are calculated identically to statistical datasets in the MODIS standard Level-3 product (MOD08 and MYD08 for MODIS Terra and Aqua, respectively). In addition, the same dataset names are used for all common datasets provided in both the continuity and standard Level-3 files.

MYD06_L2

The MODIS/Aqua Clouds 5-Min L2 Swath 1km and 5km product consists of cloud optical and physical parameters. The cloud optical parameters are generated at 1km and cloud top (physical) parameters are generated at 5km resolution. These parameters are derived using remotely sensed infrared, visible and near infrared solar reflected radiances. MODIS infrared channel radiances are used to derive cloud top temperature, cloud top height, effective emissivity, cloud phase (ice vs. water, opaque vs. non-opaque), and cloud fraction under both daytime and nighttime conditions. MODIS visible radiances are used to derive cloud optical thickness and effective particle radius and cloud shadow effects. Near infrared solar reflected radiance provides additional information in the retrieval of cloud particle phase (ice vs. water, clouds vs. snow). The shortname for this level-2 MODIS cloud product is MYD06_L2. The MYD06_L2 consists of parameters at a spatial resolution of either 1- km or 5-km (at nadir). Each MYD06_L2 product file covers a five-minute time interval. This means that for 5-km resolution parameters, the output grid is 270 pixels in width by 406 pixels in length. C6.1 changes for the cloud optical property retrievals are low-impact, and are limited primarily to ancillary product usage, the Quality Assurance (QA), and handling of cloud top (CT) properties fill values; no updates to retrieval science are implemented. The MODIS Cloud Product is used to investigate seasonal and inter-annual changes in cirrus (semi-transparent) global cloud cover and cloud phase with multispectral observations at high spatial (1 kilometer) resolution. For more information about the MYD06_L2 product, visit the MODIS-Atmosphere site at: https://modis-atmos.gsfc.nasa.gov/products/cloud

MYD03

The MODIS/Aqua Geolocation Fields 5-Min L1A Swath 1km are calculated for each 1 km MODIS Instantaneous Field of Views (IFOV) for all orbits daily (in Collection 6 and later, information is provided to calculate 500m geolocation fields). The locations and ancillary information corresponds to the intersection of the centers of each IFOV from 10 detectors in an ideal 1 km band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit, the instrument telemetry and the digital elevation model. The geolocation fields include geodetic Latitude, Longitude, surface height above geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/sea mask for each 1 km sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors of any of the 36 MODIS bands. This product is used as input by a large number of subsequent MODIS products, particularly the products produced by the Land team. The short name for this product is MYD03. Each file is roughly 30 MB in size, and the total data volume is approximately 8 GB/day. See the MODIS Science Team homepage for more data set information: https://modis.gsfc.nasa.gov/data/dataprod/

MYD17A2H

The MYD17A2H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD17A2H Version 6.1 data product. The MYD17A2H Version 6 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP minus the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD17A2H

The MYD17A2H Version 6.1 Gross Primary Productivity (GPP) product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP minus the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD17A2HGF

The MYD17A2HGF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD17A2HGF Version 6.1 data product. The MYD17A2HGF Version 6 Gross Primary Productivity (GPP) Gap-Filled product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. The MYD17A2HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H is available. Hence, the gap-filled MYD17A2HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A2HGF in near-real time because it will be generated only at the end of a given year. Known Issues: Operational and uncertainty issues are provided under Section 2 in the User Guide. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD17A2HGF

The MYD17A2HGF Version 6.1 Gross Primary Productivity (GPP) Gap-Filled product is a cumulative 8-day composite of values with 500 meter (m) pixel size based on the radiation use efficiency concept that can be potentially used as inputs to data models to calculate terrestrial energy, carbon, water cycle processes, and biogeochemistry of vegetation. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) data product includes information about GPP and Net Photosynthesis (PSN). The PSN band values are the GPP less the Maintenance Respiration (MR). The data product also contains a PSN Quality Control (QC) layer. The quality layer contains quality information for both the GPP and the PSN. The MYD17A2HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H is available. Hence, the gap-filled MYD17A2HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A2HGF in near-real time because it will be generated only at the end of a given year. Known Issues: Operational and uncertainty issues are provided under Section 2 in the User Guide. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD21

The MYD21 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD21 Version 6.1 data product. The MYD21 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MYD21 Land Surface Temperature (LST) algorithm differs from the MYD11 algorithm in that the MYD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. MYD21 products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination example documentation. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD21

The MYD21 Version 6.1 Land Surface Temperature and Emissivity (LST&E) swath data product is produced daily in five minute temporal increments of satellite acquisition. The swath is approximately 2,030 pixels along track and 1,354 pixels per line, at a nadir resolution of 1,000 meters. The MYD21 Land Surface Temperature (LST) algorithm differs from the MYD11 algorithm in that the MYD21 LST algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. Additional details regarding the method used to create this Level 2 (L2) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination example documentation. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD21C2

A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21C2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MYD21A1D and MYD21A1N daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination example documentation. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD21A2

The MYD21A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD21A2 Version 6.1 data product. A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21A2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MYD21A1D and MYD21A1N daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. MYD21A2 products are available two months after acquisition due to latency of data inputs. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination example documentation. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD21A2

A suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21A2 dataset is an 8-day composite LST product that uses an algorithm based on a simple averaging method. The algorithm calculates the average from all the cloud free MYD21A1D and MYD21A1N daily acquisitions from the 8-day period. Unlike the MOD21A1 data sets where the daytime and nighttime acquisitions are separate products, the MYD21A2 contains both daytime and nighttime acquisitions as separate Science Dataset (SDS) layers within a single Hierarchical Data Format (HDF) file. The LST, Quality Control (QC), view zenith angle, and viewing time have separate day and night SDS layers, while the values for the MODIS emissivity bands 29, 31, and 32 are the average of both the nighttime and daytime acquisitions. Additional details regarding the method used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination example documentation. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD21C1

A new suite of MODIS Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 LST algorithm differs from the algorithm of the MYD11 LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to retrieve dynamically both the LST and spectral emissivity simultaneously from the three MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21C1 Version 6.1 dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MYD21 swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21C1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The MYD21C1 product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination example documentation. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD21A1D

The MYD21A1D Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD21A1D Version 6.1 data product. A new suite of MODIS Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6. The MYD21 LST algorithm differs from the algorithm of the MYD11 LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to retrieve dynamically both the LST and spectral emissivity simultaneously from the three MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21A1D dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MYD21 swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The MYD21A1D product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. MYD21A1D products are available two months after acquisition due to latency of data inputs. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document [ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination example documentation. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD21A1D

A suite of MODIS Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6.1. The MYD21 LST algorithm differs from the algorithm of the MYD11 LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to retrieve dynamically both the LST and spectral emissivity simultaneously from the three MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21A1D dataset is produced daily from daytime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MYD21 swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The daytime average is weighted by the observation coverage for that cell. Only observations having observation coverage more than a certain threshold (15%) are considered for this averaging. The MYD21A1D product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination example documentation. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD21A1N

The MYD21A1N Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD21A1N Version 6.1 data product. A new suite of Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature and Emissivity (LST&E) products are available in Collection 6. The MYD21 Land Surface Temperature (LST) algorithm differs from the algorithm of the MYD11 LST products, in that the MYD21 algorithm is based on the ASTER Temperature/Emissivity Separation (TES) technique, whereas the MYD11 uses the split-window technique. The MYD21 TES algorithm uses a physics-based algorithm to dynamically retrieve both the LST and spectral emissivity simultaneously from the MODIS thermal infrared bands 29, 31, and 32. The TES algorithm is combined with an improved Water Vapor Scaling (WVS) atmospheric correction scheme to stabilize the retrieval during very warm and humid conditions. The MYD21A1N dataset is produced daily from nighttime Level 2 Gridded (L2G) intermediate LST products. The L2G process maps the daily MYD21 swath granules onto a sinusoidal MODIS grid and stores all observations falling over a gridded cell for a given day. The MOD21A1 algorithm sorts through all these observations for each cell and estimates the final LST value as an average from all observations that are cloud free and have good LST&E accuracies. The nighttime average is weighted by the observation coverage for that cell. Only observations having an observation coverage greater than a 15% threshold are considered. The MYD21A1N product contains seven Science Datasets (SDS), which include the calculated LST as well as quality control, the three emissivity bands, view zenith angle, and time of observation. MYD21A1N products are available two months after acquisition due to latency of data inputs. Additional details regarding the methodology used to create this Level 3 (L3) product are available in the Algorithm Theoretical Basis Document (ATBD). Known Issues: Users of MODIS LST products may notice an increase in occurrences of extreme high temperature outliers in the unfiltered MxD21 Version 6 and 6.1 products compared to the heritage MxD11 LST products. This can occur especially over desert regions like the Sahara where undetected cloud and dust can negatively impact both the MxD21 and MxD11 retrieval algorithms. * In the MxD11 LST products, these contaminated pixels are flagged in the algorithm and set to fill values in the output products based on differences in the band 32 and band 31 radiances used in the generalized split window algorithm. In the MxD21 LST products, values for the contaminated pixels are retained in the output products (and may result in overestimated temperatures), and users need to apply Quality Control (QC) filtering and other error analyses for filtering out bad values. High temperature outlier thresholds are not employed in MxD21 since it would potentially remove naturally occurring hot surface targets such as fires and lava flows. * High atmospheric aerosol optical depth (AOD) caused by vast dust outbreaks in the Sahara and other deserts highlighted in the example documentation are the primary reason for high outlier surface temperature values (and corresponding low emissivity values) in the MxD21 LST products. Future versions of the MxD21 product will include a dust flag from the MODIS aerosol product and/or brightness temperature look up tables to filter out contaminated dust pixels. It should be noted that in the MxD11B day/night algorithm products, more advanced cloud filtering is employed in the multi-day products based on a temporal analysis of historical LST over cloudy areas. This may result in more stringent filtering of dust contaminated pixels in these products. * In order to mitigate the impact of dust in the MxD21 V6 and 6.1 products, the science team recommends using a combination of the existing QC bits, emissivity values, and estimated product errors, to confidently remove bad pixels from analysis. For more details, refer to this dust and cloud contamination example documentation. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11_L2

The MYD11_L2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD11_L2 Version 6.1 data product. The MYD11_L2 Version 6 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm. MYD11_L2 is a Level 2 product which provides the input for the Level 3 products. Provided in each MYD11_L2 file are LST, quality control assessment, error estimates, bands 31 and 32 emissivities, zenith angle of the pixel view, observation time, and the geographic coordinates for every five scan lines and samples. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11_L2

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 5-Minute (MYD11_L2) Version 6.1 swath product provides per-pixel Land Surface Temperature and Emissivity (LST&E) with a pixel size of 1,000 meters (m). The product is produced daily in 5-minute temporal increments of satellite acquisition using the generalized split-window algorithm. MYD11_L2 is a Level 2 product which provides the input for the Level 3 products. Provided in each MYD11_L2 file are LST, quality control assessment, error estimates, bands 31 and 32 emissivities, zenith angle of the pixel view, observation time, and the geographic coordinates for every five scan lines and samples. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11C2

The MYD11C2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD11C2 Version 6.1 data product. The MYD11C2 Version 6 product provides Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule follows a geographic grid with 7,200 columns and 3,600 rows, representing the entire globe. The LST&E values in the MYD11C2 product are derived by compositing and averaging the values from the corresponding eight MYD11C1 daily files. The MYD11C2 granule consists of 17 layers. Each MYD11C2 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11C2

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MYD11C2) Version 6.1 product provides Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule follows a geographic grid with 7,200 columns and 3,600 rows, representing the entire globe. The LST&E values in the MYD11C2 product are derived by compositing and averaging the values from the corresponding eight MYD11C1 daily files. The MYD11C2 granule consists of 17 layers. Each MYD11C2 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11A2

The MYD11A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD11A2 Version 6.1 data product. The MYD11A2 Version 6 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MYD11A2 is a simple average of all the corresponding MYD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types. Known Issues For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11A2

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MYD11A2) Version 6.1 product provides an average 8-day per-pixel Land Surface Temperature and Emissivity (LST&E) with a 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. Each pixel value in the MYD11A2 is a simple average of all the corresponding MYD11A1 LST pixels collected within that 8-day period. The 8-day compositing period was chosen because twice that period is the exact ground track repeat period of the Terra and Aqua platforms. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11B2

The MYD11B2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD11B2 Version 6.1 data product. The MYD11B2 Version 6 product provides an average 8-day per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each temperature and emissivity pixel value in the MYD11B2 is a simple average of all the corresponding values from the LST&E values from the MYD11B1 product collected during that 8-day period. Each MYD11B2 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MYD11_L2 swath product aggregated to the 6 km grid. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11B2

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity 8-Day (MYD11B2) Version 6.1 product provides an average 8-day per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each temperature and emissivity pixel value in the MYD11B2 is a simple average of all the corresponding values from the LST&E values from the MYD11B1 product collected during that 8-day period. Each MYD11B2 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MOD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MYD11_L2 swath product aggregated to the 6 km grid. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11C1

The MYD11C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD11C1 Version 6.1 data product. The MYD11C1 Version 6 product provides daily Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). The MYD11C1 product is directly derived from the MYD11B1 product. A CMG granule follows a Geographic grid, having 7,200 columns and 3,600 rows, which represent the entire globe. Each MYD11C1 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the grid. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11C1

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MYD11C1) Version 6.1 product provides daily Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). The MYD11C1 product is directly derived from the MYD11B1 product. A CMG granule follows a Geographic grid, having 7,200 columns and 3,600 rows, which represent the entire globe. Each MYD11C1 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the grid. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11A1

The MYD11A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD11A1 Version 6.1 data product. The MYD11A1 Version 6 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MYD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11A1

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MYD11A1) Version 6.1 product provides daily per-pixel Land Surface Temperature and Emissivity (LST&E) with 1 kilometer (km) spatial resolution in a 1,200 by 1,200 km grid. The pixel temperature value is derived from the MYD11_L2 swath product. Above 30 degrees latitude, some pixels may have multiple observations where the criteria for clear-sky are met. When this occurs, the pixel value is a result of the average of all qualifying observations. Provided along with the daytime and nighttime surface temperature bands are associated quality control assessments, observation times, view zenith angles, and clear-sky coverages along with bands 31 and 32 emissivities from land cover types. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11B1

The MYD11B1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD11B1 Version 6.1 data product. The MYD11B1 Version 6 product provides daily per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each MOD11B1 granule consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the tile. Unique to the MYD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MYD11_L2 swath product aggregated to the 6 km grid. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11B1

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Daily (MYD11B1) Version 6.1 product provides daily per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each MOD11B1 granule consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, number of clear-sky observations, and emissivities from bands 20, 22, 23, 29, 31, and 32 (bands 31 and 32 are daytime only) along with the percentage of land in the tile. Unique to the MYD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MYD11_L2 swath product aggregated to the 6 km grid. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11C3

The MYD11C3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD11C3 Version 6.1 data product. The MYD11C3 Version 6 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7,200 columns and 3,600 rows representing the entire globe. The LST&E values in the MYD11C3 product are derived by compositing and averaging the values from the corresponding month of MYD11C1 daily files. Each MYD11C3 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11C3

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Monthly (MYD11C3) Version 6.1 product provides monthly Land Surface Temperature and Emissivity (LST&E) values in a 0.05 degree (5,600 meters at the equator) latitude/longitude Climate Modeling Grid (CMG). A CMG granule is a geographic grid with 7,200 columns and 3,600 rows representing the entire globe. The LST&E values in the MYD11C3 product are derived by compositing and averaging the values from the corresponding month of MYD11C1 daily files. Each MYD11C3 product consists of the following layers for daytime and nighttime observations: LSTs, quality control assessments, observation times, view zenith angles, and number of clear-sky observations along with percentage of land in the grid and emissivities from bands 20, 22, 23, 29, 31, and 32. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11B3

The MYD11B3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD11B3 Version 6.1 data product. The MYD11B3 Version 6 product provides average monthly per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each LST&E pixel value in the MYD11B3 is a simple average of all the corresponding values from the MYD11B1 collected during the month period. Each MYD11B3 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MYD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MYD11_L2 swath product aggregated to the 6 km grid. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD11B3

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature/Emissivity Monthly (MYD11B3) Version 6.1 product provides average monthly per pixel Land Surface Temperature and Emissivity (LST&E) in a 1,200 by 1,200 kilometer (km) tile with a pixel size of 5,600 meters (m). Each LST&E pixel value in the MYD11B3 is a simple average of all the corresponding values from the MYD11B1 collected during the month period. Each MYD11B3 granule consists of 19 layers including daytime and nighttime layers for LSTs, quality control assessments, observation times, view zenith angles, and number of clear sky observations along with percentage of land in the tile and emissivities from bands 20, 22, 23, 29, 31, and 32. Unique to the MYD11B products are additional day and night LST layers generated from band 31 of the corresponding 1 km MYD11_L2 swath product aggregated to the 6 km grid. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD15A2H

The MYD15A2H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD15A2H Version 6.1 data product. The MYD15A2H Version 6 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the “best” pixel available from all the acquisitions of the Aqua sensor from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nanometers (nm)) absorbed by the green elements of a vegetation canopy. Science Datasets (SDS) in the Level 4 (L4) MYD15A2H product include LAI, FPAR, two quality layers, and standard deviation for LAI and FPAR. Two low resolution browse images, LAI and FPAR, are also available for each MYD15A2H granule. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD15A2H

The MYD15A2H Version 6.1 Moderate Resolution Imaging Spectroradiometer (MODIS) combined Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) product is an 8-day composite dataset with 500 meter (m) pixel size. The algorithm chooses the "best" pixel available from all the acquisitions of the Aqua sensor from within the 8-day period. LAI is defined as the one-sided green leaf area per unit ground area in broadleaf canopies and as one-half the total needle surface area per unit ground area in coniferous canopies. FPAR is defined as the fraction of incident photosynthetically active radiation (400-700 nanometers (nm)) absorbed by the green elements of a vegetation canopy. Science Datasets (SDS) in the Level 4 (L4) MYD15A2H product include LAI, FPAR, two quality layers, and standard deviation for LAI and FPAR. Two low resolution browse images, LAI and FPAR, are also available for each MYD15A2H granule. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD16A2

The MYD16A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD16A2 Version 6.1 data product. The MYD16A2 Version 6 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. Provided in the MYD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A2 granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period depending on the year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD16A2

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) MYD16A2 Version 6.1 Evapotranspiration/Latent Heat Flux product is an 8-day composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. Provided in the MYD16A2 product are layers for composited Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A2 granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. Note that the last acquisition period of each year is a 5 or 6-day composite period depending on the year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD16A2GF

The MYD16A2GF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD16A2GF Version 6.1 data product. The MYD16A2GF Version 6 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The MYD16A2GF will be generated at the end of each year when the entire yearly 8-day MYD15A2H is available. Hence, the gap-filled MYD16A2GF is the improved MYD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD16A2GF in near-real time because it will be generated only at the end of a given year. Provided in the MYD16A2GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A2GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. The last acquisition period of each year is a 5- or 6-day composite period, depending on the year. Known Issues: Operational and uncertainty issues are provided on page 13 in the User Guide. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD16A2GF

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) MYD16A2GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled 8-day composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The MYD16A2GF will be generated at the end of each year when the entire yearly 8-day MYD15A2H is available. Hence, the gap-filled MYD16A2GF is the improved MYD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD16A2GF in near-real time because it will be generated only at the end of a given year. Provided in the MYD16A2GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A2GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum of all eight days within the composite period, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all eight days within the composite period. The last acquisition period of each year is a 5 or 6-day composite period, depending on the year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD16A3GF

The MYD16A3GF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD16A3GF Version 6.1 data product. The MYD16A3GF Version 6 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The MYD16A3GF will be generated at the end of each year when the entire yearly 8-day MYD15A2H is available. Hence, the gap-filled MYD16A3GF is the improved MYD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD16A3GF in near-real time because it will be generated only at the end of a given year. Provided in the MYD16A3GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A3GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD16A3GF

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) MYD16A3GF Version 6.1 Evapotranspiration/Latent Heat Flux (ET/LE) product is a year-end gap-filled yearly composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with MODIS remotely sensed data products such as vegetation property dynamics, albedo, and land cover. The MYD16A3GF will be generated at the end of each year when the entire yearly 8-day MYD15A2H is available. Hence, the gap-filled MYD16A3GF is the improved MYD16, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction Photosynthetically Active Radiation (LAI/FPAR) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD16A3GF in near-real time because it will be generated only at the end of a given year. Provided in the MYD16A3GF product are layers for composited ET, LE, Potential ET (PET), and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A3GF granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD16A3

The MYD16A3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD16A3GF Version 6.1 data product. The MYD16A3 Version 6 Evapotranspiration/Latent Heat Flux product is a yearly composite dataset produced at 500 meter (m) pixel resolution. The improved algorithm is based on the logic of the Penman-Monteith equation, which includes inputs of daily meteorological reanalysis data along with Moderate Resolution Imaging Spectroradiometer (MODIS) remotely sensed data products such as vegetation property dynamics, albedo, and land cover. Provided in the MYD16A3 product are layers for yearly Evapotranspiration (ET), Latent Heat Flux (LE), Potential ET (PET) and Potential LE (PLE) along with a quality control layer. Two low resolution browse images, ET and LE, are also available for each MYD16A3 granule. The pixel values for the two Evapotranspiration layers (ET and PET) are the sum for all days within the defined year, and the pixel values for the two Latent Heat layers (LE and PLE) are the average of all days within the defined year. Known Issues: Operational and uncertainty issues are provided under Section 3 in the User Guide. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website. Improvements/Changes from Previous Versions * Spatial resolution of Version 6 products has increased to nominal 500 m from nominal 1,000 m in Version 5. * Version 5 data products were previously distributed by the Numerical Terradynamic Simulation Group at the University of Montana. The Version 6 products are a continuation of this project.

MYD17A3HGF

The MYD17A3HGF Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD17A3HGF Version 6.1 data product. The MYD17A3HGF Version 6 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products (MYD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). The MYD17A3HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H is available. Hence, the gap-filled MYD17A3HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A3HGF in near-real time because it will be generated only at the end of a given year. Known Issues: Operational and uncertainty issues are provided under Section 2 in the User Guide. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD17A3HGF

The MYD17A3HGF Version 6.1 product provides information about annual Gross and Net Primary Production (GPP and NPP) at 500 meter (m) pixel resolution. Annual Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) GPP and NPP is derived from the sum of all 8-day GPP and Net Photosynthesis (PSN) products (MYD17A2H) from the given year. The PSN value is the difference of the GPP and the Maintenance Respiration (MR). The MYD17A3HGF will be generated at the end of each year when the entire yearly 8-day MYD15A2H is available. Hence, the gap-filled MYD17A3HGF is the improved MYD17, which has cleaned the poor-quality inputs from 8-day Leaf Area Index and Fraction of Photosynthetically Active Radiation (FPAR/LAI) based on the Quality Control (QC) label for every pixel. If any LAI/FPAR pixel did not meet the quality screening criteria, its value is determined through linear interpolation. However, users cannot get MYD17A3HGF in near-real time because it will be generated only at the end of a given year. Known Issues: Operational and uncertainty issues are provided under Section 2 in the User Guide. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD17A3H

The MYD17A3H Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD17A3HGF Version 6.1 data product. The MYD17A3H Version 6 product provides information about annual Net Primary Production (NPP) at 500 meter (m) pixel resolution. Annual NPP is derived from the sum of all 8-day Net Photosynthesis (PSN) products (MYD17A2H) from the given year. The PSN value is the difference of the Gross Primary Productivity (GPP) and the Maintenance Respiration (MR). Knonw Issues * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website. * Forward processing of Aqua MODIS Net Primary Production Yearly L4 Global 500 m SIN Grid products was discontinued on January 3, 2004, due to unexpected errors in the input data. Users are encouraged to use the MYD17A3HGF Version 6.1 data product.

MYDOCGA

The MYDOCGA Version 6 data product was decommissioned on July 31, 2023. The MYDOCGA Version 6 Level 2 Gridded Lite (L2G-lite) Ocean Reflectance product provides an estimate of the surface spectral reflectance data from Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) bands 8 through 16. Data have been corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. MYDOCGA is a daily land product with a pixel size of 1 kilometer (km). The product is referred to as ocean reflectance because bands 8 through 16 are used primarily to produce ocean products. The MYDOCGA, as with other L2G data sets, stores the “best available pixel” from all the qualifying observations in the first layer and any subsequent observations are stored in either a full or compact format layer within the Hierarchical Data Format (HDF) file. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website. Improvements/Changes from Previous Versions * New product for MODIS Version 6.

MYD09Q1

The MYD09Q1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD09Q1 Version 6.1 data product. The MYD09Q1 Version 6 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. Known Issues: The Collection 6 MODIS Land Surface Reflectance product (MYD09) may incorrectly flag retrievals as ‘High Aerosol’ over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values. * Corrections were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD09Q1

The MYD09Q1 Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 250 meter (m) surface reflectance bands are two quality layers. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. Known Issues: Prior to the Aqua MODIS launch, Band 6 exhibited several anomalous detectors. Band 6 performance degraded seriously after launch and presently a majority of the Band 6 detectors are non-functional. Science users should read and use the non-functional detector flags and decide for themselves the optimum manner to handle non-functional detector "gaps" for their products. For complete information please refer to the MODIS Characterization Support Team (MCST) website. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD09A1

The MYD09A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD09A1 Version 6.1 data product. The Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua MYD09A1 Version 6 product provides an estimate of the surface spectral reflectance of Aqua MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are a quality variable and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. Known Issues: The Collection 6 MODIS Land Surface Reflectance product (MYD09) may incorrectly flag retrievals as ‘High Aerosol’ over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values. * Corrections were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD09A1

The Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua MYD09A1 Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua MODIS Bands 1 through 7 corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the seven 500 meter (m) reflectance bands are a quality layer and four observation bands. For each pixel, a value is selected from all the acquisitions within the 8-day composite period. The criteria for the pixel choice include cloud and solar zenith. When several acquisitions meet the criteria the pixel with the minimum channel 3 (blue) value is used. Known Issues: Prior to the Aqua MODIS launch, Band 6 exhibited several anomalous detectors. Band 6 performance degraded seriously after launch and presently a majority of the Band 6 detectors are non-functional. Science users should read and use the non-functional detector flags and decide for themselves the optimum manner to handle non-functional detector "gaps" for their products. For complete information please refer to the MODIS Characterization Support Team (MCST) website. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD09GA

The MYD09GA Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD09GA Version 6.1 data product. The MYD09GA Version 6 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 km observation bands and geolocation flags. The reflectance layers from the MYD09GA are used as the source data for many of the MODIS land products. Known Issues: The Collection 6 MODIS Land Surface Reflectance product (MYD09) may incorrectly flag retrievals as ‘High Aerosol’ over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values. * Corrections were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD09GA

The MYD09GA Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Provided along with the 500 meter (m) surface reflectance, observation, and quality bands are a set of ten 1 kilometer observation bands and geolocation flags. The reflectance layers from the MYD09GA are used as the source data for many of the MODIS land products. Known Issues: Prior to the Aqua MODIS launch, Band 6 exhibited several anomalous detectors. Band 6 performance degraded seriously after launch and presently a majority of the Band 6 detectors are non-functional. Science users should read and use the non-functional detector flags and decide for themselves the optimum manner to handle non-functional detector "gaps" for their products. For complete information please refer to the MODIS Characterization Support Team (MCST) website. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD09GQ

The MYD09GQ Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD09GQ Version 6.1 data product. The MYD09GQ Version 6 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m bands are the Quality Assurance (QA) layer and five observation layers. This product is intended to be used in conjunction with the quality and viewing geometry information of the 500 m product (MYD09GA). Known Issues: The Collection 6 MODIS Land Surface Reflectance product (MYD09) may incorrectly flag retrievals as ‘High Aerosol’ over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values. * Corrections were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD09GQ

The MYD09GQ Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) 250 meter (m) bands 1 and 2, corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. Along with the 250 m bands are the Quality Assurance (QA) layer and five observation layers. This product is intended to be used in conjunction with the quality and viewing geometry information of the 500 m product (MYD09GA). Known Issues: Prior to the Aqua MODIS launch, Band 6 exhibited several anomalous detectors. Band 6 performance degraded seriously after launch and presently a majority of the Band 6 detectors are non-functional. Science users should read and use the non-functional detector flags and decide for themselves the optimum manner to handle non-functional detector "gaps" for their products. For complete information please refer to the MODIS Characterization Support Team (MCST) website. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD09CMG

The MYD09CMG Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD09CMG Version 6.1 data product. The MYD09CMG Version 6 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, resampled to 5600 meter (m) pixel resolution and corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. The MOD09CMG data product provides 25 layers including MODIS bands 1 through 7; Brightness Temperature data from thermal bands 20, 21, 31, and 32; along with Quality Assurance (QA) and observation bands. This product is based on a Climate Modeling Grid (CMG) for use in climate simulation models. Known Issues: The Collection 6 MODIS Land Surface Reflectance product (MYD09) may incorrectly flag retrievals as ‘High Aerosol’ over brighter surfaces and at higher view angles. This will impact the downstream MODIS BRDF/Albedo (MCD43) and Vegetation Index (MOD13 and MYD13) data products which use the aerosol quantity flag to screen out high aerosol values. * Corrections were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD09CMG

The MYD09CMG Version 6.1 product provides an estimate of the surface spectral reflectance of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Bands 1 through 7, resampled to 5600 meter (m) pixel resolution and corrected for atmospheric conditions such as gasses, aerosols, and Rayleigh scattering. The MOD09CMG data product provides 25 layers including MODIS bands 1 through 7; Brightness Temperature data from thermal bands 20, 21, 31, and 32; along with Quality Assurance (QA) and observation bands. This product is based on a Climate Modeling Grid (CMG) for use in climate simulation models. Known Issues: A striping anomaly in Band 5 of the MYD09CMG V61 product has been identified, caused by a dead detector in the AQUA Band 5, affecting data accuracy throughout the Aqua MODIS mission. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD07_L2

The MODIS/Aqua Temperature and Water Vapor Profiles 5-Min L2 Swath 5km (MYD07_L2) product consists of a numbers of parameters related to atmospheric stability, atmospheric temperature and moisture profiles, total atmospheric water vapor, and total ozone. All of these parameters are produced for both daytime and nighttime conditions at 5-km pixel resolution when at least 9 Field Of View (FOV) are cloud free. The MODIS total-ozone burden is an estimate of the total-column tropospheric and stratospheric ozone content. The MODIS atmospheric stability consists of three daily Level 2 atmospheric stability indices. The Total Totals (TT), the Lifted Index (LI), and the K index (K) are each computed using the infrared temperature- and moisture-profile data, also derived as part of MYD07. The MODIS temperature and moisture profiles are produced at 20 vertical levels. The MODIS atmospheric water-vapor product is an estimate of the total tropospheric column water vapor made from integrated MODIS infrared retrievals of atmospheric moisture profiles in clear scenes. Additional information is available at: https://modis-atmos.gsfc.nasa.gov/products/atm-profile.

MYD14

The MYD14 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD14 Version 6.1 data product. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire MYD14 Version 6 product is produced daily in 5-minute temporal satellite increments (swaths). The MYD14 product is used to generate all of the higher level fire products, but can also be used to identify fires and other thermal anomalies, such as volcanoes. Each swath of data is approximately 2,030 kilometers along track (long), and 2,300 kilometers across track (wide). Known Issues: Known issues are described on the MODIS/VIIRS Land Quality Assessment website and in Section 7.2 of the User Guide which covers Pre-November 2000 Data Quality, Detection Confidence, Flagging of Static Sources, and the August 2020 MODIS Aqua Outage.

MYD14

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire (MYD14) Version 6.1 product is produced daily in 5-minute temporal satellite increments (swaths). The MYD14 product is used to generate all of the higher level fire products, but can also be used to identify fires and other thermal anomalies, such as volcanoes. Each swath of data is approximately 2,030 kilometers along track (long), and 2,300 kilometers across track (wide). Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD14A2

The MYD14A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD14A2 Version 6.1 data product. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day (MYD14A2) Version 6 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MYD14A2 gridded composite contains maximum value of individual fire pixel classes detected during the eight days of acquisition. The Science Dataset (SDS) layers include the fire mask and pixel quality indicators. Known Issues: Known issues are described on the MODIS/VIIRS Land Quality Assessment website and in Section 7.2 of the User Guide which covers Pre-November 2000 Data Quality, Detection Confidence, Flagging of Static Sources, and the August 2020 MODIS Aqua Outage.

MYD14A2

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire 8-Day (MYD14A2) Version 6.1 data are generated at 1 kilometer (km) spatial resolution as a Level 3 product. The MYD14A2 gridded composite contains maximum value of individual fire pixel classes detected during the eight days of acquisition. The Science Dataset (SDS) layers include the fire mask and pixel quality indicators. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD14A1

The MYD14A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD14A1 Version 6.1 data product. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily (MYD14A1) Version 6 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MYD14A1 contains eight consecutive days of fire data conveniently packaged into a single file. The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire-radiative-power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition. Known Issues: Known issues are described on the MODIS/VIIRS Land Quality Assessment website and in Section 7.2 of the User Guide which covers Pre-November 2000 Data Quality, Detection Confidence, Flagging of Static Sources, and the August 2020 MODIS Aqua Outage.

MYD14A1

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Thermal Anomalies and Fire Daily (MYD14A1) Version 6.1 data are generated every eight days at 1 kilometer (km) spatial resolution as a Level 3 product. MYD14A1 contains eight consecutive days of fire data conveniently packaged into a single file. The Science Dataset (SDS) layers include the fire mask, pixel quality indicators, maximum fire-radiative-power (MaxFRP), and the position of the fire pixel within the scan. Each layer consists of daily per pixel information for each of the eight days of data acquisition. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYDTBGA

The MYDTBGA Version 6 data product was decommissioned on July 31, 2023. The MYDTBGA Version 6 Moderate Resolution Imaging Spectroradiometer (MODIS) Level 2 Gridded Lite (L2G-lite) Thermal Band product consists of brightness temperature data from Aqua MODIS bands 20, 31, and 32 and albedo data from band 20 along with the orbit and granule pointer fields. MYDTBGA is a daily product with a pixel size of 1 kilometer (km). The MYDTBGA, as with other L2G data sets, stores the “best available pixel” from all the qualifying observations in the first layer and any subsequent observations are stored in either full or a compact layers within the Hierarchical Data Format (HDF) file. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website. Improvements/Changes from Previous Versions * New product for MODIS Version 6.

MYD05_L2

The MODIS/Aqua Total Precipitable Water Vapor 5-Min L2 Swath 1km and 5km (MYD05_L2) product consists of atmospheric column water-vapor amounts. This product is derived from data collected by the Moderate-Resolution Imaging Spectroradiometer (MODIS) on board the Aqua satellite. There are two different algorithms used to derive total precipitable water vapor in this data product: a near-infrared algorithm and an infrared algorithm. The near-infrared algorithm relies on observations of reflected solar radiation in MODIS's near-infrared channels, thus, the near-infrared retrievals are only produced during the daytime over surfaces that reflect near-infrared energy. As a result, the near-infrared algorithm is only applied over clear, cloud free land areas of the globe and above clouds over both the land and ocean. Over clear ocean areas, water-vapor estimates are provided over extended sun glint areas. Data produced by the near-infrared algorithm are generated at a 1-km spatial resolution. The other algorithm is the infrared algorithm which can be used to derive atmospheric precipitable water vapor profiles during both day and night. Data from the infrared algorithm are generated at a 5-km spatial resolution when at least nine field of views (FOVs) are cloud free. The infrared-derived precipitable water vapor is generated as a component of product MYD07, and is simply added to product MYD05 for convenience. There are two MODIS Precipitable Water Vapor products: MOD05_L2, containing data collected from the Terra platform; and MYD05_L2, containing data collected from the Aqua platform. This dataset has a short name of MYD05_L2 and provides data from the Aqua platform only. The MODIS Adaptive Processing System (MODAPS) is currently generating an improved version 6.1 (061) for all MODIS Level-1 (L1) and higher-level Level-2 (L2) & Level-3 (L3) Atmosphere Team products. The decision to create a new improved Collection 6.1 (061) was driven by the need to address a number of issues in the current Collection 6 (006) Level-1B (L1B) data, which have a negative impact in varying degrees in downstream products. It should be noted that the near-infrared algorithm refinement for this product is no longer being supported by NASA and as such there has been no update to this algorithm for Collection 6.1. For more information, visit the MODIS Atmosphere website at: https://modis-atmos.gsfc.nasa.gov/products/water-vapor

MYD13C1

The MYD13C1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD13C1 Version 6.1 data product. The MYD13C1 Version 6 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The Climate Modeling Grid (CMG) consists 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. Global MYD13C1 data are cloud-free spatial composites of the gridded 16-day 1 kilometer MYD13A2 data, and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MYD13C1 has data fields for NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. Known Issues: The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MYD09 surface reflectance products may have impacted MYD13 Vegetation Index data products particularly over arid bright surfaces. * Corrections were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD13C1

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MYD13C1) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The Climate Modeling Grid (CMG) consists 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. Global MYD13C1 data are cloud-free spatial composites of the gridded 16-day 1 kilometer MYD13A2 data, and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MYD13C1 has data fields for NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD13A2

The MYD13A2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD13A2 Version 6.1 data product. The MYD13A2 Version 6 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value. Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: The following issues have been detected: * Unexpected missing data in the last cycles of each year. * Incorrect instances of "NoData" and spikes in NDVI values. * VI Usefulness Bits are not correctly assigned. * For instances where the VI Quality (bits 0-1) is flagged as good and the VI Usefulness (bits 2-5) indicates the same pixels have the lowest usefulness score, users are advised to disregard the usefulness score. * The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MYD09 surface reflectance products may have impacted MYD13 Vegetation Index data products particularly over arid bright surfaces. * Corrections were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD13A2

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MYD13A2) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 1 kilometer (km) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle and the highest NDVI/EVI value. Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD13Q1

The MYD13Q1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD13Q1 Version 6.1 data product. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13Q1) Version 6 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MYD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: The following issues have been detected: * Unexpected missing data in the last cycles of each year. * Incorrect instances of "NoData" and spikes in NDVI values. * VI Usefulness Bits are not correctly assigned. * For instances where the VI Quality (bits 0-1) is flagged as good and the VI Usefulness (bits 2-5) indicates the same pixels have the lowest usefulness score, users are advised to disregard the usefulness score. * The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MYD09 surface reflectance products may have impacted MYD13 Vegetation Index data products particularly over arid bright surfaces. * Corrections were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD13Q1

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13Q1) Version 6.1 data are generated every 16 days at 250 meter (m) spatial resolution as a Level 3 product. The MYD13Q1 product provides two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Along with the vegetation layers and the two quality layers, the HDF file will have MODIS reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD13A1

The MYD13A1 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD13A1 Version 6.1 data product. The MYD13A1 Version 6 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: The following issues have been detected: * Unexpected missing data in the last cycles of each year. * Incorrect instances of "NoData" and spikes in NDVI values. * VI Usefulness Bits are not correctly assigned. * For instances where the VI Quality (bits 0-1) is flagged as good and the VI Usefulness (bits 2-5) indicates the same pixels have the lowest usefulness score, users are advised to disregard the usefulness score. * The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MYD09 surface reflectance products may have impacted MYD13 Vegetation Index data products particularly over arid bright surfaces. * Corrections were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD13A1

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices 16-Day (MYD13A1) Version 6.1 product provides Vegetation Index (VI) values at a per pixel basis at 500 meter (m) spatial resolution. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI), which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The algorithm for this product chooses the best available pixel value from all the acquisitions from the 16 day period. The criteria used is low clouds, low view angle, and the highest NDVI/EVI value. Provided along with the vegetation layers and two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as four observation layers. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD13C2

The MYD13C2 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD13C2 Version 6.1 data product. The MYD13C2 Version 6 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. In generating this monthly product, the algorithm ingests all the MYD13A2 products that overlap the month and employs a weighted temporal average. Global MYD13C1 data are cloud-free spatial composites and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MYD13C2 has data fields for the NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. Known Issues: The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MYD09 surface reflectance products may have impacted MYD13 Vegetation Index data products particularly over arid bright surfaces. * Corrections were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD13C2

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices Monthly (MYD13C2) Version 6.1 product provides a Vegetation Index (VI) value at a per pixel basis. There are two primary vegetation layers. The first is the Normalized Difference Vegetation Index (NDVI) which is referred to as the continuity index to the existing National Oceanic and Atmospheric Administration-Advanced Very High Resolution Radiometer (NOAA-AVHRR) derived NDVI. The second vegetation layer is the Enhanced Vegetation Index (EVI), which has improved sensitivity over high biomass regions. The Climate Modeling Grid (CMG) consists of 3,600 rows and 7,200 columns of 5,600 meter (m) pixels. In generating this monthly product, the algorithm ingests all the MYD13A2 products that overlap the month and employs a weighted temporal average. Global MYD13C1 data are cloud-free spatial composites and are provided as a Level 3 product projected on a 0.05 degree (5,600 m) geographic CMG. The MYD13C2 has data fields for the NDVI, EVI, VI QA, reflectance data, angular information, and spatial statistics such as mean, standard deviation, and number of used input pixels at the 0.05 degree CMG resolution. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD13A3

The MYD13A3 Version 6 data product was decommissioned on July 31, 2023. Users are encouraged to use the MYD13A3 Version 6.1 data product. The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13A3) Version 6 data are provided monthly at 1 kilometer (km) spatial resolution as a gridded Level 3 product in the sinusoidal projection. In generating this monthly product, the algorithm ingests all the MYD13A2 products that overlap the month and employs a weighted temporal average. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes an Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin clouds. The MODIS NDVI and EVI products are computed from surface reflectances corrected for molecular scattering, ozone absorption, and aerosols. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes as well as global and regional climate. Additional applications include characterizing land surface biophysical properties and processes, such as primary production and land cover conversion. Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as three observation layers. Known Issues: The incorrect representation of the aerosol quantities (low, average, high) in the Collection 6 MYD09 surface reflectance products may have impacted MYD13 Vegetation Index data products particularly over arid bright surfaces. * Corrections were implemented in Collection 6.1 reprocessing. * For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD13A3

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Vegetation Indices (MYD13A3) Version 6.1 data are provided monthly at 1 kilometer (km) spatial resolution as a gridded Level 3 product in the sinusoidal projection. In generating this monthly product, the algorithm ingests all the MYD13A2 products that overlap the month and employs a weighted temporal average. The MODIS Normalized Difference Vegetation Index (NDVI) complements NOAA's Advanced Very High Resolution Radiometer (AVHRR) NDVI products and provides continuity for time series historical applications. MODIS also includes an Enhanced Vegetation Index (EVI) that minimizes canopy background variations and maintains sensitivity over dense vegetation conditions. The EVI uses the blue band to remove residual atmosphere contamination caused by smoke and sub-pixel thin clouds. The MODIS NDVI and EVI products are computed from surface reflectances corrected for molecular scattering, ozone absorption, and aerosols. Vegetation indices are used for global monitoring of vegetation conditions and are used in products displaying land cover and land cover changes. These data may be used as input for modeling global biogeochemical and hydrologic processes as well as global and regional climate. Additional applications include characterizing land surface biophysical properties and processes, such as primary production and land cover conversion. Provided along with the vegetation layers and the two quality assurance (QA) layers are reflectance bands 1 (red), 2 (near-infrared), 3 (blue), and 7 (mid-infrared), as well as three observation layers. Known Issues: For complete information about known issues please refer to the MODIS/VIIRS Land Quality Assessment website.

MYD28C2

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir 8-Day Level 3 (L3) Global (MYD28C2) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The MYD28C2 Version 6.1 data product provides an 8-day time series of surface area, elevation, and water storage. Datasets are combined with pre-established Area-Elevation (A-E) curves and image classifications of near-infrared (NIR) reflectance from the surface reflectance product acquired by the Aqua satellite (MYD09Q1). The MYD28C2 data product contains a single layer with information about the reservoir identifier, dam location (longitude and latitude), reservoir area, elevation, and storage capacity. Known Issues: Water occurrence images generally show smaller surface area dynamics in high latitude regions, creating pixels with low occurrence values that have relatively large uncertainties. In addition, the quality of raw water area classification can be affected by lake ice coverage typically creating an overestimation of surface area in the enhancement algorithm. This issue will be addressed in a future release of the enhancement algorithm. For additional information about known issues, refer to Section 4 in the User Guide and MODIS/VIIRS Land Quality Assessment website.

MYD28C3

The Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) Water Reservoir Monthly Level 3 (L3) Global (MYD28C3) Version 6.1 product provides current data for 151 man-made reservoirs and 13 regulated natural lakes for a total of 164 reservoirs. The MYD28C3 Version 6.1 data product is a composite of the 8-day area classifications from MYD28C2, which is converted to provide monthly elevation and water storage. Lake Temperature and Evaporation Model (LTEM) via MODIS Land Surface Temperature (LST) (MYD21) and meteorological data from Global Land Data Assimilation System (GLDAS) are used to produce monthly evaporation rates and volume losses. The MYD28C3 data product contains a single layer with information about the reservoir identifier, dam location (longitude and latitude), monthly reservoir area, elevation, storage capacity, evaporation rate, and evaporation volume. Known Issues: Water occurrence images generally show smaller surface area dynamics in high latitude regions, creating pixels with low occurrence values that have relatively large uncertainties. In addition, the quality of raw water area classification can be affected by lake ice coverage typically creating an overestimation of surface area in the enhancement algorithm. This issue will be addressed in a future release of the enhancement algorithm. For additional information about known issues, refer to Section 4 in the User Guide and MODIS/VIIRS Land Quality Assessment website.

MYDFDS_MON_GLB_L3

Version 1 is the current version of the dataset. This collection MYDFDS_MON_GLB_L3 provides level 3 monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚. It is derived from Level 2, the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products Collection 6.1 from Aqua (MYD04_L2). The dataset covers the monthly mean from 2003 to 2022. The FDS is calculated as the number of days per month when the daily dust optical depth is greater than a threshold optical depth (e.g., 0.025) with two quality flags: the lowest (1) and highest (3). It is advised to use flag 1, which is of lower quality, over dust source regions, and flag 3 over remote areas or polluted regions. Eight thresholds (0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 1, 2) are saved separately in eight files. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).

SNDRAQIML2CCPRET

WARNING: To users of the derived product “co_mmr_midtrop” (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply “co_mmr_midtrop” by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (“mol_lay/co_mol_lay”) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) and AMSU (Advanced Microwave Sounding Unit). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS in combination with the AMSU constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km. The CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.

SNDRAQIML2CCPCCR

The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) and AMSU (Advanced Microwave Sounding Unit). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS in combination with the AMSU constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km. Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV. An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.

SNDRAQIML2PLEVCPS

The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder)/AMSU (Advanced Microwave Sounding Unit) instruments aboard the Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using data. They include including gas mixing ratio profiles, column totals, surface values, tropopause properties, and relative humidity, together with per-field quality flagging. The profiles are specified at the surface and layer boundaries and are estimated from layer amounts using the L2 algorithm An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.

SNDRAQIML3SDCCP

WARNING: Users of the derived product “co_mmr_midtrop” (carbon monoxide mass mixing ratio to dry air [kg/kg] at ~500 hPa). This variable has a significant bias due to a conversion error: the molecular weight of carbon dioxide (CO2, 44.01 g/mol) was used instead of carbon monoxide (CO, 28.01 g/mol). To correct, simply multiply “co_mmr_midtrop” by 28.01/44.01. Alternatively, derive a profile of mass mixing ratio from scratch using the retrieved column density values (“mol_lay/co_mol_lay”) in the Level 2 files. For further questions or concerns please contact the Sounder SIPS at: sounder.sips@jpl.nasa.gov The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) and AMSU (Advanced Microwave Sounding Unit). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS in combination with the AMSU constitutes an innovative atmospheric sounding group of infrared and microwave sensors. The AIRS Standard Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km. The CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid. This daily one degree latitude by one degree longitude level-3 product starts with level-2 retrieval products applying the specific quality control (QC) methodology. Specific QC accepts profile level from the top of the atmosphere down to the level where the QC determines that it is still good. Below this level, the data is rejected.

SNDRAQIL2CPS

The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder). The AIRS instrument is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. The AIRS CLIMCAPS Retrieval Product consists of retrieved estimates of cloud and surface properties, plus profiles of retrieved temperature, water vapor, ozone, carbon monoxide and methane. The temperature profile vertical resolution is 100 levels total between 1100 mb and 0.1 mb, while moisture profile is reported at atmospheric layers between 1100 mb and 300 mb. The horizontal resolution is 50 km. The CLIMCAPS algorithm uses an Optimal Estimation methodology and uses an a-priori first guess to start the process. A CLIMCAPS sounding is comprised of a set of parameters that characterizes the full atmospheric state and includes a variety of geophysical parameters derived from the CrIMSS data. These include surface temperature and infrared emissivity; full atmosphere profiles of temperature, water vapor and ozone; infrared effective cloud top characteristics; carbon monoxide, methane, carbon dioxide, sulfur dioxide, nitrous oxide, and nitric acid. An AIRS level 2 granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.

SNDRAQIL2PLEVCPS

The CLIMCAPS (Community Long-term Infrared Microwave Coupled Product System) algorithm is used to analyze data from the AIRS (Atmospheric Infrared Sounder) instrument aboard the Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. This file contains the fixed Pressure Level product (PLEV) variables derived from the CLIMCAPS algorithm using data. They include including gas mixing ratio profiles, column totals, surface values, tropopause properties, and relative humidity, together with per-field quality flagging. The profiles are specified at the surface and layer boundaries and are estimated from layer amounts using the L2 algorithm An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day. The CLIMCAPS algorithm uses data from the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) as a first-guess for the atmospheric state. Because MERRA-2 products typically have a latency from 3 to 7 weeks, so too do the CLIMCAPS products.

SNDRAQIML1BCALSUBSUM

The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. AIRS/Aqua Level-1C calibration subset including clear cases, special calibration sites, random nadir spots, and high clouds. Infrared temperature sounders generate a large amount of Level-1B spectral data. For example, the AIRS instrument with 2378 channels, its visible light component and AMSU with 15 channels create 3x240 files each day, for a total of over 500 MB of data. The purpose of the Calibration Data Subsets is extract key information from these data into a few daily files to: 1. Facilitate a quick evaluation of the absolute calibration of the instruments. 2. Facilitate an assessment of the instrument performance under clear, cloudy, and extreme hot and cold conditions. 3. Facilitate the evaluation of instrument trends and their significance relative to climate trends. 4. Facilitate the comparison of AIRS with CrIS using their equivalent data subsets. The output files are constructed from Level-1B or Level-1C IR and MW brightness or antenna temperatures. Each file contains selected observations taken from a nominal 24-hour period. The “summary” product includes a large set of cases of interest, including all identified spectra that match selection criteria detailed below for clear, special cloud classes, etc. These amount to about 10% of all spectra. But for each selected case only brightness temperatures (BTs) for selected key channels are saved.

SNDR13CHRP1

The Climate Hyperspectral Infrared Radiance Product (CHIRP) is a Level 1 radiance product derived from Atmospheric Infrared Sounder (AIRS) on EOS-AQUA and the Cross-Track Infrared Sounders (CrIS) on the SNPP and JPSS-1+ platforms. (JPSS-1 is also called NOAA-20). CHIRP provides a consistent spectral response function (SRF) across all instruments. Inter-instrument radiometric offsets are removed with SNPP-CrIS chosen as the "standard". CHIRP follows the original instrument storage, i.e., granule in, granule out, and contains all information needed for retrievals (including cross-track, along-track, fov id, etc.). This version of CHIRP, SNDR13CHRP1, is the primary CHIRP product which provides a full radiance record from 2002 onwards starting with AIRS from September 1, 2002 to August 30, 2016, switching to the SNPP CrIS instrument on September 1, 2016. The SNDR13CHRP1 product then switches to using JPSS-1 CrIS radiances starting September 1, 2018. This selection of time periods provides the best match to times when the microwave sounders on each of these platforms exhibited good performance and avoids long outages (such as SNPP CrIS in early Spring 2019). CHIRP is available for AIRS, CrIS-SNPP, and CrIS-JPSS-1 for time periods not used in the product distributed here, and are named SNDR13CHRP1AQCal, SNDR13CHRP1SNCal, and SNDR13CHRP1J1Cal respectively.

SNDRAQIL3SSDFCNSAT

This data set provides an estimate of the surface air temperature. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.

SNDRAQIL3SSDFCVPD

This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.

SNDR13IML3SSDFCVPD

The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS). The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.

MYDFDS_SDV_GLB_L3

Version 1 is the current version of the dataset. This collection MYDFDS_SDV_GLB_L3 provides level 3 standard deviation of climatological monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚. It is derived from Level 2, the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products Collection 6.1 from Aqua (MYD04_L2). The dataset is the standard deviation of climatological monthly mean for each month over 2003 to 2022. The FDS is calculated as the number of days per month when the daily dust optical depth is greater than a threshold optical depth (e.g., 0.025) with two quality flags: the lowest (1) and highest (3). It is advised to use flag 1, which is of lower quality, over dust source regions, and flag 3 over remote areas or polluted regions. Eight thresholds (0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 1, 2) are saved separately in eight files. If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).

Data Discovery

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Data Access

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NASA Aura Project

Aura (Latin for breeze) obtains measurements of ozone, aerosols and key gases throughout the atmosphere.

OMUFPMET

The GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUFPMET) product provides selected meteorlogical fields from the GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include layer pressure thickness, surface pressure, vertical temperature profiles, surface potential, and mid-layer pressure along with geolocation info. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPMET. The OMI ancillary products were developed to provide supplementary information for use with the OMI collection 4 L1B data sets. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. The OMUFPMET files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.

OMVFPMET

The GEOS-5 FP-IT 3D Time-Averaged Model-Layer Assimilated Data Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km (OMVFPMET) product provides selected meteorlogical fields from the GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include layer pressure thickness, surface pressure, vertical temperature profiles, surface potential, and mid-layer pressure along with geolocation info. The OMI team also provides a corresponding product for the OMI UV2 swath, OMUFPMET. The OMI ancillary products were developed to provide supplementary information for use with the OMI collection 4 L1B data sets. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. The OMVFPMET files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.

OMUFPSLV

The GEOS-5 FP-IT 3D Time-Averaged Single-Level Diagnostics Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUFPSLV) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include boundary layer top pressure, tropopause pressure, surface pressure, surface skin temperature, and vertical wind profiles at 10m. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPSLV. The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. The OMUFPSLV files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.

OMVFPSLV

The GEOS-5 FP-IT 3D Time-Averaged Single-Level Diagnostics Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km (OMVFPSLV) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include boundary layer top pressure, tropopause pressure, surface pressure, surface skin temperature, and vertical wind profiles at 10m. The OMI team also provides a corresponding product for the OMI UV2 swath, OMUFPSLV. The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. The OMVFPSLV files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.

OMUFPITMET

The GEOS-5 FP-IT Assimilation Geo-colocated to OMI/Aura UV-2 1-Orbit L2 Support Swath 13x24km (OMUFPITMET) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include surface pressure, vertical temperature profiles, surface and vertical wind profiles, tropopause pressure, boundary layer top pressure, and surface geopotenial. The OMI team also provides a corresponding product for the OMI VIS swath, OMVFPITMET. The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. To reduce the size of each orbital file, FP-IT data fields with a vertical dimension of 72 layers have been reduced to 47 layers in OMUFPITMET by combining layers above the troposphere. The OMUFPITMET files are in netCDF4 format which is compatible with most HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.

OMVFPITMET

The GEOS-5 FP-IT Assimilation Geo-colocated to OMI/Aura VIS 1-Orbit L2 Support Swath 13x24km (OMVFPITMET) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI VIS swath. The fields in this product include surface pressure, vertical temperature profiles, surface and vertical wind profiles, tropopause pressure, boundary layer top pressure, and surface geopotenial. The OMI team also provides a corresponding product for the OMI UV-2 swath, OMUFPITMET. The product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI VIS spatial resolution is 13km x 24km at nadir. To reduce the size of each orbital file, FP-IT data fields with a vertical dimension of 72 layers have been reduced to 47 layers in OMVFPITMET by combining layers above the troposphere. The OMVFPITMET files are in netCDF4 format which is compatible with most HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.

HIRMLS3IWC

HIRMLS3IWC is the Joint EOS High Resolution Dynamics Limb Sounder (HIRDLS) and Microwave Limb Sounder (MLS) monthly 10 degreee lat x 20 degreee lon gridded product for ice water content (IWC) data. This is version 2 released to the public, with the original input coming from v3.3 MLS and v7 HIRDLS. The grid spatial coverage is near-global (-80 to +90 degrees latitude). The product contains HIRDLS and MLS IWC data for the time of the HIRDLS mission from February 1, 2005 through December 31, 2007. The useful vertical range of the data is from 215 to 82 hPa for both HIRDLS and MLS, and the vertical resolution is about 1.5 km for HIRDLS and 3 km for MLS. Users of the HIRMLS3IWC data product should read the Version 2 HIRDLS-MLS Level 3 IWC Data Description and Quality document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF5. The data file contains two grid objects (one with HIRDLS data, the other with MLS data), each with a set of data fields, attributes, and metadata. Each grid contains data fields with IWC values, and the HIRDLS grid includes data fields with volume density, cloud top pressure and frequency of clouds. Time, latitude and vertical pressure information are also included in each grid.

HIRDLS2

The "HIRDLS/Aura Level 2 Geophysical Parameters" data product (HIRDLS2) contains an entire day's worth of Level-2 vertical profiles of O3, HNO3, H2O, CFC-11, CFC-12, N2O, NO2, N2O5, ClONO2, temperature, geopotential height, and aerosol extinction at 12.1 and 8.3 microns, as well as cloud top pressure. HIRDLS measured infrared emissions in 21 channels ranging from 6.12 to 17.76 microns in the upper troposphere, stratosphere and mesosphere. Data are available for the ~3 year mission from January 29, 2005 until March 17, 2008. Observations of the Earth's atmosphere were only made from the far azimuth scan (away from sun side) resulting in limited data coverage from +80 to -64 degrees latitude. The useful vertical range of the data depends on the measured species, and are provided on 24 levels per decade of pressure corresponding to about 1 km vertical resolution. The current and final version of this product is 7. Of the original targeted species, only CH4 was not retrieved in this version. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a single swath object with one day of data (measured species and species precision), geolocation fields (e.g. time, latitude, longitude, pressure), and swath attributes, along with file level metadata. Each file contains approximately 5600 profile scans.

H3ZFCCLONO2

The "HIRDLS/Aura Level 3 Chlorine Nitrate (ClONO2) Zonal Fourier Coefficients" version 7 data product (H3ZFCCLONO2) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 100 to 1.0 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.

H3ZFCCFC11

The "HIRDLS/Aura Level 3 Chlorofluorocarbon-11 (CFC-11) Zonal Fourier Coefficients" version 7 data product (H3ZFCCFC11) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the CFC-11 data is 316 to 17.8 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.

H3ZFCCFC12

The "HIRDLS/Aura Level 3 Chlorofluorocarbon-12 (CFC-12) Zonal Fourier Coefficients" version 7 data product (H3ZFCCFC12) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the CFC-12 data is 316 to 8.3 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.

HIR3SCOL

HIR3SCOL is the EOS High Resolution Dynamics Limb Sounder (HIRDLS/Aura) level 3 daily gridded 1 x 1 deg. stratospheric columns of NO2 (nitrogen dioxide) data product. The data are gridded at 1 x 1 degree resolution from +80 to -64 degrees latitude. The stratospheric column is computed from data at 57 to 1.0 hPa. The product consists of one file spanning the entire ~3 year HIRDLS mission from January 22, 2005 through March 17, 2008. Users of the HIR3SCOL data product should read the Version 7 HIRDLS Data Description and Quality document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF5. The data file contains one grid object with data fields, attributes, and metadata.

H3ZFCN2O5

The "HIRDLS/Aura Level 3 Dinitrogen Pentoxide (N2O5) Zonal Fourier Coefficients" version 7 data product (H3ZFCN2O) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 82.5 to 1.0 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.

H3ZFC12MEXT

The "HIRDLS/Aura Level 3 Extinction at 12.1 Microns Zonal Fourier Coefficients" version 7 data product (H3ZFC12MEXT) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 215 to 20 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.

H3ZFC8MEXT

The "HIRDLS/Aura Level 3 Extinction at 8.3 Microns Zonal Fourier Coefficients" version 7 data product (H3ZFC8MEXT) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 215 to 20 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.

H3ZFCGPH

The "HIRDLS/Aura Level 3 Geopotential Height Zonal Fourier Coefficients" version 7 data product (H3ZFCGPH) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 1000 to 0.01 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.

H3ZFCHNO3

The "HIRDLS/Aura Level 3 Nitric Acid (HNO3) Zonal Fourier Coefficients" version 7 data product (H3ZFCHNO3) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 215 to 5.1 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.

H3ZFCNO2

The "HIRDLS/Aura Level 3 Nitrogen Dioxide (NO2) Zonal Fourier Coefficients" version 7 data product (H3ZFCNO2) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 100 to 5.1 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.

H3ZFCN2O

The "HIRDLS/Aura Level 3 Nitrous Oxide (N2O) Zonal Fourier Coefficients" version 7 data product (H3ZFCN2O) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 100 to 5.1 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.

H3ZFCO3

The "HIRDLS/Aura Level 3 Ozone (O3) Zonal Fourier Coefficients" version 7 data product (H3ZFCO3) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 422 to 0.1 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.

H3ZFCT

The "HIRDLS/Aura Level 3 Temperature Zonal Fourier Coefficients" version 7 data product (H3ZFCT) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 1000 to 0.0042 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.

H3ZFCH2O

The "HIRDLS/Aura Level 3 Water Vapor (H2O) Zonal Fourier Coefficients" version 7 data product (H3ZFCH2O) contains the entire mission (~3 years) of HIRDLS data expressed as zonal Fourier coefficients in 1 degree latitude bands from -64 to 80 degrees at 121 pressure levels. The coefficients are computed from the HIRDLS Level 2 profiles with a Kalman filter approach using both forward and backward passes in time. Expressed as the mean and up to 7 sine and cosine coefficients (4 waves for ascending and descending, 7 waves for combined), these coefficients may be used to compute values at any longitude. The data are provided on a pressure grid with 24 levels per decade, corresponding to about 1 km vertical resolution. The useful vertical range of the data is 200 to 10 hPa. The precision values are given by the root-mean square of the differences between the estimated fields and the input data. The data are stored in the version 5 Hierarchical Data Format for the Earth Observing System (HDF-EOS5), which is an extension of the HDF5 format. Each file contains a zonal object with data for the entire mission with separate data fields for ascending (daytime), descending (nighttime), and combined orbit node.

ML3DZMBRO

ML3DZMBRO is the EOS Aura Microwave Limb Sounder (MLS) daily zonal mean product for bromine monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-85 degrees to 85 degrees latitude) spaced every 10 degrees in latitude. The recommended useful vertical range is between 10 and 4.64 hPa, and the vertical resolution is about 5 km. Users of the ML3DZMBRO data product should read the MLS Radiance Average Retrievals (RAR) BrO Product Guideline document, as well as section 3.2 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 4 network Common Data Form (netCDF4), which is built on the version 5 Hierarchical Data Format, or HDF5. The netCDF4 files follow the Climate and Forecast (CF) metadata conventions. Each file contains two zonal means objects or groups, one with data from the ascending part of the MLS orbit, the other with the descending data. Each zonal means object contains the average, error (precision), solar zenith angle, and local solar time for each latitude band and pressure level. Files also contain metadata attributes describing the data and product.

ML3DZMHO2

ML3DZMHO2 is the EOS Aura Microwave Limb Sounder (MLS) daily zonal mean product for hydroperoxy derived from radiances measured in two bands from the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-85 degrees to 85 degrees latitude) spaced every 10 degrees in latitude. The recommended useful vertical range is between 21.5 to 0.0464 hPa, and the vertical resolution is about 5 km. Users of the ML3DZMHO2 data product should read the MLS Radiance Average Retrievals (RAR) Product Guideline document, as well as section 3.2 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 4 network Common Data Form (netCDF4), which is built on the version 5 Hierarchical Data Format, or HDF5. The netCDF4 files follow the Climate and Forecast (CF) metadata conventions. Each file contains two zonal means objects or groups, one with data from the daytime part of the MLS orbit, the other with the nighttime data. Each zonal means object contains the average, error (precision), solar zenith angle, and local solar time for each latitude band and pressure level. Files also contain metadata attributes describing the data and product.

ML1OA

ML1OA is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 orbit attitude and tangent point geolocation data. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1OA data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains orbital and attitude information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.

ML1OA

ML1OA is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 orbit attitude and tangent point geolocation data. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1OA data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains orbital and attitude information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.

ML1RADD

ML1RADD is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the digital autocorrelators. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADD data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.

ML1RADD

ML1RADD is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the digital autocorrelators. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADD data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.

ML1RADG

ML1RADG is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the filter banks for the GHz radiometers. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADG data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.

ML1RADG

ML1RADG is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the filter banks for the GHz radiometers. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADG data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.

ML1RADT

ML1RADT is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the filter banks for the GHz radiometers. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADG data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.

ML1RADT

ML1RADT is the EOS Aura Microwave Limb Sounder (MLS) product containing the level 1 radiances from the filter banks for the GHz radiometers. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), and files contain a full days worth of data (15 orbits). Users of the ML1RADG data product should read the 'A Short Guide to the Use and Interpretation of v4.2x Level 1 Data' document for additional information. The data are stored in the version 5 Hierarchical Data Format, or HDF-5. Each file contains radiances and ancillary information written as HDF-5 dataset objects (n-dimensional arrays), along with file attributes and metadata.

ML2BRO

ML2BRO is the EOS Aura Microwave Limb Sounder (MLS) standard product for bromine monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 10 and 3.16 hPa, and the vertical resolution is about 5.5 km (6 km at 3.16 hPa). Users of the ML2BRO data product should read section 3.2 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2BRO

ML2BRO is the EOS Aura Microwave Limb Sounder (MLS) standard product for bromine monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 10 and 3.16 hPa, and the vertical resolution is about 5.5 km (6 km at 3.16 hPa). Users of the ML2BRO data product should read section 3.2 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2CO

ML2CO is the EOS Aura Microwave Limb Sounder (MLS) standard product for carbon monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML2CO data product should read section 3.7 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2CO

ML2CO is the EOS Aura Microwave Limb Sounder (MLS) standard product for carbon monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 215 and 0.00564 hPa, and the vertical resolution is about 6 km. Users of the ML2CO data product should read section 3.7 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2CLO

ML2CLO is the EOS Aura Microwave Limb Sounder (MLS) standard product for chlorine monoxide derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML2CLO data product should read section 3.6 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2CLO

ML2CLO is the EOS Aura Microwave Limb Sounder (MLS) standard product for chlorine monoxide derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML2CLO data product should read section 3.6 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2IWC

ML2IWC is the EOS Aura Microwave Limb Sounder (MLS) standard product for cloud ice water content derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML2IWC data product should read sections 3.15 and 3.16 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2IWC

ML2IWC is the EOS Aura Microwave Limb Sounder (MLS) standard product for cloud ice water content derived from radiances measured by the 240 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML2IWC data product should read sections 3.15 and 3.16 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2DGG

ML2DGG is the EOS Aura Microwave Limb Sounder (MLS) product containing geophysical diagnostic quantities pertaining directly to the standard geophysical data products, generally on a similar (or identical) grid, and at different spectral ranges. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). Vertical resolution varies between species and typically ranges from 3 - 6 km. Users of the ML2DGG data product should read the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contain swaths objects for each diagnostics measurement. Each swath has a set of data and geolocation fields, swath attributes, and metadata.

ML2DGG

ML2DGG is the EOS Aura Microwave Limb Sounder (MLS) product containing geophysical diagnostic quantities pertaining directly to the standard geophysical data products, generally on a similar (or identical) grid, and at different spectral ranges. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). Vertical resolution varies between species and typically ranges from 3 - 6 km. Users of the ML2DGG data product should read the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contain swaths objects for each diagnostics measurement. Each swath has a set of data and geolocation fields, swath attributes, and metadata.

ML2DGM

ML2DGM is the EOS Aura Microwave Limb Sounder (MLS) product containing the minor frame diagnostic quantities on a miscellaneous grid. These include items such as tangent pressure, chi-square describing various fits to the measured radiances, number of radiances used in various retrieval phases, etc. This product contains a second auxiliary file which includes cloud-induced radiances inferred for selected spectral channels. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). Vertical resolution varies between species and typically ranges from 3 - 6 km. Users of the ML2DGM data product should read the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 Hierarchical Data Format, or HDF5. Each file contains sets of HDF5 dataset objects (n-dimensional arrays) for each diagnostics measurement. The dataset objects represent data and geolocation fields; included in the file are file attributes and metadata. There are two files per day (MLS-Aura_L2AUX-DGM* and MLS-Aura_L2AUX-Cloud*).

ML2GPH

ML2GPH is the EOS Aura Microwave Limb Sounder (MLS) standard product for geopotential height derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML2GPH data product should read section 3.8 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2GPH

ML2GPH is the EOS Aura Microwave Limb Sounder (MLS) standard product for geopotential height derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML2GPH data product should read section 3.8 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2HCL

ML2HCL is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydrogen chloride derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2HCL data product should read section 3.10 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2HCL

ML2HCL is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydrogen chloride derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2HCL data product should read section 3.10 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2HCN

ML2HCN is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydrogen cyanide derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML2HCN data product should read section 3.11 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2HCN

ML2HCN is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydrogen cyanide derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML2HCN data product should read section 3.11 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2HO2

ML2HO2 is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroperoxy derived from radiances measured in two bands from the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 21.5 to 0.0464 hPa, and the vertical resolution is about 5 km. Users of the ML2HO2 data product should read section 3.13 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2HO2

ML2HO2 is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroperoxy derived from radiances measured in two bands from the 640 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 21.5 to 0.0464 hPa, and the vertical resolution is about 5 km. Users of the ML2HO2 data product should read section 3.13 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2OH

ML2OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroxyl derived from radiances measured by the THz radiometer. The data version is 4.2. Data coverage is continuous from August 8, 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML2OH data product should read section 3.19 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2OH

ML2OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for hydroxyl derived from radiances measured by the THz radiometer. The data version is 5.0. Data coverage is continuous from August 8, 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML2OH data product should read section 3.19 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2HOCL

ML2HOCL is the EOS Aura Microwave Limb Sounder (MLS) standard product for hypochlorous acid derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML2OHCL data product should read section 3.14 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2HOCL

ML2HOCL is the EOS Aura Microwave Limb Sounder (MLS) standard product for hypochlorous acid derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML2OHCL data product should read section 3.14 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2CH3OH

At this time it is recommended that these data not be used pending further validation.ML2CH3OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for methanol derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 100 hPa, and the vertical resolution range is about 4-5 km. Users of the ML2CH3OH data product should read section 3.5 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.

ML2CH3OH

At this time it is recommended that these data not be used pending further validation.ML2CH3OH is the EOS Aura Microwave Limb Sounder (MLS) standard product for methanol derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 100 hPa, and the vertical resolution range is about 4-5 km. Users of the ML2CH3OH data product should read section 3.5 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.

ML2CH3CL

ML2CH3CL is the EOS Aura Microwave Limb Sounder (MLS) standard product for methyl chloride derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML2CH3CL data product should read section 3.3 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.

ML2CH3CL

ML2CH3CL is the EOS Aura Microwave Limb Sounder (MLS) standard product for methyl chloride derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML2CH3CL data product should read section 3.3 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.

ML2CH3CN

ML2CH3CN is the EOS Aura Microwave Limb Sounder (MLS) standard product for methyl cyanide derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 46.4 and 1.0 hPa, and the vertical resolution ranges between ~5 km in the lower stratosphere and ~10 km in the upper stratosphere. Users of the ML2CH3CN data product should read section 3.4 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.

ML2CH3CN

ML2CH3CN is the EOS Aura Microwave Limb Sounder (MLS) standard product for methyl cyanide derived from radiances measured by the 640 GHz radiometer. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 46.4 and 1.0 hPa, and the vertical resolution ranges between ~5 km in the lower stratosphere and ~10 km in the upper stratosphere. Users of the ML2CH3CN data product should read section 3.4 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains one swath object (profile data), with a set of data and geolocation fields, swath attributes, and metadata.

ML2HNO3

ML2HNO3 is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitric acid derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 4.2. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML2HNO3 data product should read section 3.12 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2HNO3

ML2HNO3 is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitric acid derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 5.0. Data coverage is from August 8, 2004 to current. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML2HNO3 data product should read section 3.12 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2N2O

ML2N2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitrous oxide derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML2N2O data product should read section 3.17 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2N2O

ML2N2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for nitrous oxide derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML2N2O data product should read section 3.17 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2O3

ML2O3 is the EOS Aura Microwave Limb Sounder (MLS) standard product for ozone derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML2O3 data product should read section 3.18 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2O3

ML2O3 is the EOS Aura Microwave Limb Sounder (MLS) standard product for ozone derived from radiances measured by the 240 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML2O3 data product should read section 3.18 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2RHI

ML2RHI is the EOS Aura Microwave Limb Sounder (MLS) standard product for relative humidity with respect to ice derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2RHI data product should read section 3.20 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2RHI

ML2RHI is the EOS Aura Microwave Limb Sounder (MLS) standard product for relative humidity with respect to ice derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2RHI data product should read section 3.20 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2SO2

ML2SO2 is the EOS Aura Microwave Limb Sounder (MLS) standard product for sulfur dioxide derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML2SO2 data product should read section 3.21 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2SO2

ML2SO2 is the EOS Aura Microwave Limb Sounder (MLS) standard product for sulfur dioxide derived from radiances measured by the 240 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML2SO2 data product should read section 3.21 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2T

ML2T is the EOS Aura Microwave Limb Sounder (MLS) standard product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2T data product should read section 3.22 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2T

ML2T is the EOS Aura Microwave Limb Sounder (MLS) standard product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML2T data product should read section 3.22 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2H2O

ML2H2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for water vapor derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML2H2O data product should read section 3.9 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML2H2O

ML2H2O is the EOS Aura Microwave Limb Sounder (MLS) standard product for water vapor derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.0. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML2H2O data product should read section 3.9 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5), which is based on the version 5 Hierarchical Data Format, or HDF-5. Each file contains two swath objects (profile and column data), each with a set of data and geolocation fields, swath attributes, and metadata.

ML3DZMBRO

ML3DZMBRO is the EOS Aura Microwave Limb Sounder (MLS) daily zonal mean product for bromine monoxide derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-85 degrees to 85 degrees latitude) spaced every 10 degrees in latitude. The recommended useful vertical range is between 10 and 4.64 hPa, and the vertical resolution is about 5 km. Users of the ML3DZMBRO data product should read the MLS Radiance Average Retrievals (RAR) BrO Product Guideline document, as well as section 3.2 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 4 network Common Data Form (netCDF4), which is built on the version 5 Hierarchical Data Format, or HDF5. The netCDF4 files follow the Climate and Forecast (CF) metadata conventions. Each file contains two zonal means objects or groups, one with data from the ascending part of the MLS orbit, the other with the descending data. Each zonal means object contains the average, error (precision), solar zenith angle, and local solar time for each latitude band and pressure level. Files also contain metadata attributes describing the data and product.

ML3DBCO

ML3DBCO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3DBCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBCO

ML3DBCO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3DBCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZCO

ML3DZCO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3DZCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZCO

ML3DZCO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3DZCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBCLO

ML3DBCLO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3DBCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBCLO

ML3DBCLO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3DBCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZCLO

ML3DZCLO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3DZCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZCLO

ML3DZCLO is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3DZCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBIWC

ML3DBIWC is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3DBIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBIWC

ML3DBIWC is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3DBIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZIWC

ML3DZIWC is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3DZIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZIWC

ML3DZIWC is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3DZIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBGPH

ML3DBGPH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3DBGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3DBGPH

ML3DBGPH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3DBGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3DZGPH

ML3DZGPH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3DZGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZGPH

ML3DZGPH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3DZGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBHCL

ML3DBHCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3DBHCL

ML3DBHCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3DZHCL

ML3DZHCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZHCL

ML3DZHCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBHCN

ML3DBHCN is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3DBHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBHCN

ML3DBHCN is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3DBHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZHCN

ML3DZHCN is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3DZHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZHCN

ML3DZHCN is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3DZHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBOH

ML3DBOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 4.2. Data coverage is continuous from August 2, 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3DBOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3DBOH

ML3DBOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 5.1. Data coverage is continuous from August 2, 2005 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3DBOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3DZOH

ML3DZOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 4.2. Data coverage is continuous from August 2, 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3DZOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZOH

ML3DZOH is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 5.1. Data coverage is continuous from August 2, 2005 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3DZOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBHOCL

ML3DBHOCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3DBOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBHOCL

ML3DBHOCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3DBOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZHOCL

ML3DZHOCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3DZOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZHOCL

ML3DZHOCL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3DZOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBCH3CL

ML3DBCH3CL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3DBCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBCH3CL

ML3DBCH3CL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3DBCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZCH3CL

ML3DZCH3CL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3DZCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZCH3CL

ML3DZCH3CL is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3DZCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBHNO3

ML3DBHNO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3DBHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBHNO3

ML3DBHNO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3DBHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZHNO3

ML3DZHNO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3DZHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZHNO3

ML3DZHNO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 5.1. Data coverage is from August 2, 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3DZHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBN2O

ML3DBN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3DBN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBN2O

ML3DBN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3DBN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZN2O

ML3DZN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3DZN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZN2O

ML3DZN2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3DZN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBO3

ML3DBO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3DBO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBO3

ML3DBO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3DBO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZO3

ML3DZO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3DZO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZO3

ML3DZO3 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3DZO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBRHI

ML3DBRHI is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBRHI

ML3DBRHI is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZRHI

ML3DZRHI is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZRHI

ML3DZRHI is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBSO2

ML3DBSO2 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3DBSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBSO2

ML3DBSO2 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3DBSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZSO2

ML3DZSO2 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3DZSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZSO2

ML3DZSO2 is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3DZSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBT

ML3DBT is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBT

ML3DBT is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DBT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZT

ML3DZT is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZT

ML3DZT is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3DZT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DBH2O

ML3DBH2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3DBH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3DBH2O

ML3DBH2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on various vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3DBH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3DZH2O

ML3DZH2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3DZH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZH2O

ML3DZH2O is the EOS Aura Microwave Limb Sounder (MLS) daily binned on zonal and assorted vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at 4 degree latitude zonal increments. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3DZH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files contain one year of data and are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains four group objects: lat vs pressure zonal mean, lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3DZMHO2

ML3DZMHO2 is the EOS Aura Microwave Limb Sounder (MLS) daily zonal mean product for hydroperoxy derived from radiances measured in two bands from the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2, 2004 to current. Spatial coverage is near-global (-85 degrees to 85 degrees latitude) spaced every 10 degrees in latitude. The recommended useful vertical range is between 21.5 to 0.0464 hPa, and the vertical resolution is about 5 km. Users of the ML3DZMHO2 data product should read the MLS Radiance Average Retrievals (RAR) Product Guideline document, as well as section 3.2 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data are stored in the version 4 network Common Data Form (netCDF4), which is built on the version 5 Hierarchical Data Format, or HDF5. The netCDF4 files follow the Climate and Forecast (CF) metadata conventions. Each file contains two zonal means objects or groups, one with data from the daytime part of the MLS orbit, the other with the nighttime data. Each zonal means object contains the average, error (precision), solar zenith angle, and local solar time for each latitude band and pressure level. Files also contain metadata attributes describing the data and product.

ML3MBCO

ML3MBCO is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3MBCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3MBCO

ML3MBCO is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for carbon monoxide (CO) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 215 and 0.00464 hPa, and the vertical resolution is about 6 km. Users of the ML3MBCO data product should read chapter 4 and section 3.7 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3MBCLO

ML3MBCLO is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3MBCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3MBCLO

ML3MBCLO is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for chlorine monoxide (ClO) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 1.0 hPa, and the vertical resolution varies between 3 and 4.5 km. Users of the ML3MBCLO data product should read chapter 4 and section 3.6 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3MBIWC

ML3MBIWC is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3MBIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBIWC

ML3MBIWC is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for cloud ice water content (IWC) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 82.5 hPa, and the vertical resolution is about 3 km. Users of the ML3MBIWC data product should read chapter 4 and sections 3.15 and 3.16 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBGPH

ML3MBGPH is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Data coverage is from August 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3MBGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBGPH

ML3MBGPH is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for geopotential height (GPH) derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Data coverage is from August 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 261 and 0.001 hPa, and the vertical resolution varies between ~3.6 and 6 km. Users of the ML3MBGPH data product should read chapter 4 and section 3.8 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBHCL

ML3MBHCL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3MBHCL

ML3MBHCL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydrogen chloride (HCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The recommended useful vertical range is from 100 to 0.316 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBHCL data product should read chapter 4 and section 3.10 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3MBHCN

ML3MBHCN is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3MBHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3MBHCN

ML3MBHCN is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydrogen cyanide (HCN) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 21.5 to 0.1 hPa, and the vertical resolution is between 8 and 12 km. Users of the ML3MBHCN data product should read chapter 4 and section 3.11 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3MBOH

ML3MBOH is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 4.2. Data coverage is continuous from August 2004 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3MBOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3MBOH

ML3MBOH is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hydroxyl (OH) derived from radiances measured by the THz radiometer. The data version is 5.1. Data coverage is continuous from August 2005 to December 12, 2009 when the THz radiometer was placed in standby mode. After this date OH data were collected for about 30 days in August/September of 2011, 2012, 2013 and 2014. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 31.6 to 0.00316 hPa, and the vertical resolution is about 3. Users of the ML3MBOH data product should read chapter 4 and section 3.19 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3MBHOCL

ML3MBHOCL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3MBOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBHOCL

ML3MBHOCL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for hypochlorous acid (HOCl) derived from radiances measured primarily by the 640 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 10 to 2.15 hPa, and the vertical resolution is about 6 km. Users of the ML3MBOHCL data product should read chapter 4 and section 3.14 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". These are further subdivided into groups with all valid, ascending orbit, descending orbit, daytime (SZA < 90), and nighttime (SZA > 110) profiles. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBCH3CL

ML3MBCH3CL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 4.2. Data coverage is from August 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3MBCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBCH3CL

ML3MBCH3CL is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for methyl chloride (CH3Cl) derived from radiances measured by the 640 GHz radiometer. The data version is 5.1. Data coverage is from August 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 147 and 4.64 hPa, and the vertical resolution ranges between 4-6 km in the lower stratosphere and 8-10 km above 14 hPa. Users of the ML3MBCH3CL data product should read chapter 4 and section 3.3 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBHNO3

ML3MBHNO3 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 4.2. Data coverage is from August 2004 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3MBHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3MBHNO3

ML3MBHNO3 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitric acid (HNO3) derived from radiances measured by the 240 GHz radiometer at and below 10 hPa, and from the 190 GHz radiometer above 10 hPa. The data version is 5.1. Data coverage is from August 2005 to current. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 1.47 hPa (1.0 hPa under enhanced conditions), and the vertical resolution is between about 3 and 5 km. Users of the ML3MBHNO3 data product should read chapter 4 and section 3.12 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3MBN2O

ML3MBN2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3MBN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBN2O

ML3MBN2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for nitrous oxide (N2O) derived from radiances measured primarily by the 640 GHz radiometer (Band 12) until August 6, 2013, after this date using the 190 GHz radiometer (Band 3). The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 68.1 to 0.464 hPa, and the vertical resolution is between 4 and 6 km. Users of the ML3MBN2O data product should read chapter 4 and section 3.17 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs "potential temperature", lat vs "potential temperature" zonal mean, "equivalent latitude" vs "potential temperature" zonal mean, and vortex average vs "potential temperature". Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBO3

ML3MBO3 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3MBO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3MBO3

ML3MBO3 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for ozone (O3) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude), with a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.0215 hPa, and the vertical resolution is between 2.5 and 6 km. Users of the ML3MBO3 data product should read chapter 4 and section 3.18 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains two grid objects (profile and column data), each with a set of data and geolocation fields, grid attributes, and metadata.

ML3MBRHI

ML3MBRHI is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBRHI

ML3MBRHI is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for relative humidity with respect to ice (RHI) derived from radiances measured by the 118 and 190 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 316 to 0.0215 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBRHI data product should read chapter 4 and section 3.20 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBSO2

ML3MBSO2 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3MBSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBSO2

ML3MBSO2 is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for sulfur dioxide (SO2) derived from radiances measured by the 240 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 215 to 10 hPa, and the vertical resolution is about 3 km. Users of the ML3MBSO2 data product should read chapter 4 and section 3.21 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBT

ML3MBT is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBT

ML3MBT is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for temperature derived from radiances measured by the 118 and 240 GHz radiometers. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is from 261 to 0.001 hPa, and the vertical resolution is between 3 and 6 km. Users of the ML3MBT data product should read chapter 4 and section 3.22 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBH2O

ML3MBH2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 4.2. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3MBH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 4 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML3MBH2O

ML3MBH2O is the EOS Aura Microwave Limb Sounder (MLS) monthly binned on various vertical grids product for water vapor (H2O) derived from radiances measured primarily by the 190 GHz radiometer. The data version is 5.1. Spatial coverage is near-global (-82 to +82 degrees latitude) at a spatial resolution of 4 degrees latitude by 5 degrees longitude. The recommended useful vertical range is between 316 and 0.00215 hPa, and the vertical resolution is about 1.5 km at 316 hPa increasing to 3.5 km to 4.64 hPa, and degrades to 15 km above 0.1 hPa. Users of the ML3MBH2O data product should read chapter 4 and section 3.9 of the EOS MLS Level 2 Version 5 Quality Document for more information. The data files are archived in the netCDF4 format, which is also compatible with HDF5 readers and tools. Each file contains six group objects: lat-lon map vs pressure, lat vs pressure zonal mean, lat-lon map vs theta, lat vs theta zonal mean, equivalent lat vs theta zonal mean, and vortex average vs theta. Each group has a set of data (average, min, max, std dev, rms) and geolocation fields, grid attributes, and metadata.

ML2CO_NRT

ML2CO_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for carbon monoxide (CO). This product contains CO profiles derived from the 240 GHz region. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 215 to 0.1 hPa. The MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.

ML2HNO3_NRT

ML2HNO3_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for nitric acid (HNO3). This product contains HNO3 profiles derived from the 190 and 240 GHz regions. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 100 to 1.47 hPa. The MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.

ML2N2O_NRT

ML2N2O_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for nitrous oxide (N2O). This product contains N2O profiles derived from the 190 GHz region. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 100 to 1 hPa. The MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.

ML2O3_NRT

ML2O3_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for ozone (O3). This product contains O3 profiles derived from the 240 GHz region. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 261 to 0.1 hPa. The MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.

ML2SO2_NRT

ML2SO2_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for sulfur dioxide (SO2). This product contains SO2 profiles derived from the 190 and 240 GHz regions. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 215 to 10 hPa. The MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.

ML2T_NRT

ML2T_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for temperature. This product contains temperature profiles derived from the 118 GHz region. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 215 to 0.001 hPa. The MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.

ML2H2O_NRT

ML2H2O_NRT is the EOS Aura Microwave Limb Sounder (MLS) Near-Real-Time (NRT) product for water vapor (H2O). This product contains H2O profiles derived from the 190 GHz region. The NRT data are typically available within 3 hours of observation and are broken into files containing about 15 minutes of data. The most recent 7 days of data are available online. Spatial coverage is near-global (-82 degrees to +82 degrees latitude), with each profile spaced 1.5 degrees or ~165 km along the orbit track (roughly 15 orbits per day). The vertical coverage is from 147 to 1 hPa. The MLS NRT algorithm uses a simplified fast forward model to meet Near Real Time data latency requirements and are therefore not as accurate as the retrievals that constitute the standard MLS products. Nevertheless the results are scientifically useful in selected regions of the Earth's atmosphere provided that the data are screened according to the recommendations in the MLS NRT User Guide and the MLS L2 Data Quality Document for Standard Products.

OMAEROZ

The reprocessed OMI/Aura Level-2 Zoomed Aerosol data product OMAEROZ at 13x12 km resolution have been made available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access in March 2012. There are two Level-2 Aura OMI aerosol products OMAERO and OMAERUV. The OMAERUV product uses the near-UV algorithm. The OMAERO (13x24 km resolution) and OMAEROZ (13x12 km resolution) is based on the multi-wavelength algorithm that uses up to 20 wavelength bands between 331 nm and 500 nm. The multi-wavelength retrieval algorithm is developed by the KNMI OMI Team Scientists. Drs. Deborah Stein-Zweers, Martin Sneep and Pepijn Veefkind are now the key investigators of this product. The OMAEROZ products contain Aerosol Optical Depths, Single Scattering Albedo, Aerosol Type, Aerosol Layer Height, and other intermediate and ancillary parameters and geolocation information. The OMAEROZ files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). OMAEROZ data files are based on Zoomed Level 1B radiance observations which are made once a month. Thus there is one day of zoomed data (approximately 14 orbits) per month. The maximum file size for the OMAEROZ data is about 11 Mbytes. A Readme document containing brief algorithm description and known data quality related issues and file specifications are provided by the OMAERO Algorithm lead.

OMMYDAGEO

The OMI/Aura and MODIS/Aqua Aerosol Geo-colocation Product 1-Orbit L2 Swath 13x24 km (OMMYDAGEO) is a Level-2 orbital data product that links the MODIS/Aqua aerosol geo-coordinates at 3 and 10 km with the OMI indices along the OMI orbital track. This product allows users to match up MODIS granules with the OMI orbit for analysis and validation. It co-locates MODIS and OMI cloud and radiance information onto the OMI pixel. The OMMYDAGEO data files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5) using the swath model, and follows the same conventions used by the other OMI Level-2 data products. Each file contains data for 5 minutes, corresponding to the MODIS granule from the daylit side of the orbit. There are on the order of about 140 swath files per day. The file size for the OMMYDAGEO data product is about 12 Megabytes.

OMMYDCLD

The OMI/Aura and MODIS/Aqua Merged Cloud Product 1-Orbit L2 Swath 13x24 km (OMMYDCLD) is a Level-2 orbital product that combines cloud parameters retrieved by the Ozone Mapping Instrument (OMI) on the Aura satellite with collocated statistical information for cloud parameters retrieved by the Moderate Resolution Imaging Spectrometer (MODIS) on the Aqua spacecraft. This product is designed to take advantage of the synergy between OMI and MODIS, which both fly on satellites in the NASA A-Train constellation of Earth-observing satellites that follow similar orbital tracks and collect near-simultaneous observations. This product can be used for cloud-clearing, detection of multi-layered clouds, and other applications that may exploit these multi-spectral measurements. The algorithm for the OMMYDCLD product co-locates daytime cloud parameters from MODIS onto the OMI visible (VIS) pixel for a given OMI orbit and generates statistical information from the collocated MODIS pixels. For each OMI granule, the orbit start and end times are used to select the corresponding 5-minute MODIS granules for processing. A contiguous list of MODIS granules spanning the full duration of the OMI orbit are selected based on the relative time lag between Aqua and Aura. The algorithm lead for this product is NASA OMI scientist Dr. Joanna Joiner. The OMMYDCLD data files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5) using the swath model, and follows the same conventions used by the other OMI Level-2 data products. Each file contains data from the day lit portion of an orbit (about 53 minutes). There are approximately 14 orbits per day. The file size for the OMMYDCLD data product is about 8 Mbytes.

OMBRO

The Aura Ozone Monitoring Instrument (OMI) collection-3 Bromine Monoxide Product OMBRO from the Aura-OMI, is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The shortname for this Level-2 OMI total column BrO product is OMBRO. The algorithm leads for this product are the US OMI scientists Dr. Kelly Chance and Dr. Thomas Kurosu from the Harvard-Smithsonian Center, Cambridge, MA. The OMBRO product contains total vertical column BrO, standard errors (rms and sigma), quality flags, geolocation and other ancillary information. The OMBRO files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The average file size for the OMBRO data product is about 5 Mbytes.

OMOCLO

The Aura Ozone Monitoring Instrument (OMI) collection-3 Chlorine Dioxide Product OMOCLO is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The shortname for this Level-2 OMI total column OClO product is OMOCLO. The algorithm leads for this product are the US OMI scientists Dr. Kelly Chance and Dr. Thomas Kurosu from the Harvard-Smithsonian Center, Cambridge, MA. The OMOCLO product contains slant column OClO, standard errors (rms and sigma), quality flags, geolocation and other ancillary information. The OMOCLO files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMOCLO data product is about 20 MB.

OMCLDO2

The reprocessed OMI/Aura Level-2 cloud data product OMCLDO2 is now available from the NASA GoddardEarth Sciences Data and Information Services Center (GES DISC) for the public access. It is the second release of Version 003 and was reprocessed in late 2011. OMI provides two cloud products based on two different algorithms, the Rotational Raman Scattering method, and O2-O2 absorption method using the DOAS technique. This level-2 global cloud product, with a pixel resolution of 13x24 km2at nadir, is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. This product contains cloud pressure, cloud fraction, slant column O2-O2, ozone, ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The lead scientist for this product is Dr. Pepijn Veefkind. The OMCLDO2 product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes) and is roughly 15.096 MB in size. There are approximately 14 orbits per day thus the total data volume is approximately 200 GB/day.

OMCLDO2G

This Level-2G daily global gridded product OMCLDO2G is based on the pixel level OMI Level-2 CLDO2 product OMCLDO2. This level-2G global cloud product (OMCLDO2G) is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. This product contains cloud pressure, cloud fraction, slant column O2-O2 and Ozone, Ring coefficients, uncertainties in derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The short name for this Level-2 OMI cloud product is OMCLDO2G and the lead scientist for this product and for OMCLDO2 (the data source of OMCLDO2G) is KNMI scientist Dr. Pepijn Veefkind. OMCLDO2G data product is a special Level-2 Global Gridded Product where pixel level data (OMCLDO2) are binned into 0.25x0.25 degree global grids. It contains the OMCLDO2 data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid are saved 'Without Averaging'. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMCLDO2G product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each daily data file contains data from the day lit portion of the orbits (~14 orbits) and is roughly 85 MB in size.

OMCLDO2Z

The reprocessed Aura Ozone Monitoring Instrument (OMI) Level-2 zoomed cloud data product OMCLDO2Z at 13x12 km resolution is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. It is the second release of Version 003 and was reprocessed late 2011. OMI provides two cloud products based on two different algorithms, the Rotational Raman Scattering method and O2-O2 absorption method using the DOAS technique. This level-2 zoomed cloud product at the pixel resolution (13x12 km2 at nadir) is based on the spectral fitting of O2-O2 absorption band at 477 nm using DOAS technique. This product contains effective cloud pressure, effective cloud fraction, slant column O2-O2; uncertainties in derived, parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Zoomed cloud product is OMCLDO2Z. The lead scientist for this product is Dr. Pepijn Veefkind. The OMCLDO2Z files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes) and is roughly 20 MB in size. OMCLDO2Z data files are based on Zoomed Level 1B radiance observations which are made once a month. Thus there is one zoomed cloud product per month.

OMDOAO3

The second release of Aura Ozone Monitoring Instrument (OMI) Version 003 OMI/Aura Level-2 Total Column Ozone Data Product OMDOAO3 is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The data were processed in late 2011 using Algorithm or PGE version 1.2.3 and released in March 2012. OMI provides two total column ozone products based on two different algorithms. This level-2 global total column ozone product at the pixel resolution (13x24 km at nadir), is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI UV radiance values between 331.1 and 336.1 nm. In addition to the total ozone column this product also contains auxiliary , derived and ancillary input parameters e.g. ozone slant column density, ozone ghost column density, air mass factor, scene reflectivity, radiance over the DOAS fit window, root mean square of DAOS fit, cloud fraction, cloud radiance, cloud pressure, terrain height, geolocation, viewing angles and quality flags. The shortname for this Level-2 OMI total column ozone product is OMDOAO3. The lead scientist for this product is Dr. J. Pepijn Veefkind. The OMDOAO3 product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (approximately 53 minutes) and is approximately 11 MB in size. There are approximately 14 orbits per day.

OMDOAO3Z

The reprocessed Aura Ozone Monitoring Instrument (OMI) Level-2 Zoomed Ozone data product OMDOAO3Z at 13x12 km resolution is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. It is the second release of Version 003 and was reprocessed late 2011. OMI provides two sets of total column ozone products OMTO3 and OMDOAO3 which are based on two different algorithms. OMTO3 product is based on TOMS like ozone retrieval algorithm whereas OMDOAO3 total column ozone product is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI visible radiance values between 331.1 and 336.1 nm. The DOAS retrieval algorithm is developed by the KNMI OMI Scientist, Dr Pepijn Veefkind. Based on spatial resolutions, there are two DOAS algorithm based ozone products, OMDOAO3 (at 13x24 km resolution) and OMDOAO3Z (13x12 km resolution). In addition to the total ozone column values these DOAS based ozone products also contain some auxiliary derived and ancillary input parameters e.g. ozone slant column density, ozone ghost column density, air mass factor, scene reflectivity, radiance over the DOAS fit window, root mean square of DAOS fit, cloud fraction, cloud radiance, cloud pressure, terrain height, geolocation, viewing angles and quality flags. The shortname for this Level-2 OMI Zoomed Ozone product is OMDOAO3Z. The OMDOAO3Z files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). OMDOAO3Z data files are based on Zoomed Level 1B radiance observations which are made once a month. Thus there is one day of zoomed data (approximately 14 orbits) per month. The maximum file size for the OMDOAO3Z data is approximately 30 MB.

OMCLDRR

The reprocessed Aura Ozone Monitoring Instrument (OMI) Version 003 Level 2 Cloud Data Product OMCLDRR is available to the public from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). Aura OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes.

OMCLDRR

This is the Aura Ozone Monitoring Instrument (OMI) Version 004 Level 2 Cloud Data Product OMCLDRR. OMI provides two Level-2 Cloud products (OMCLDRR and OMCLDO2) at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms, the Rotational Raman Scattering method and the O2-O2 absorption method. This level-2 global cloud product, OMCLDRR, provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI Cloud Pressure and Fraction product is OMCLDRR and the algorithm lead for this product is NASA OMI scientist Dr. Joanna Joinner. The OMCLDRR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMCLDRR data product is about 9 Mbytes.

OMCLDRRG

This Level-2G daily global gridded product OMCLDRRG is based on the pixel level OMI Level-2 CLDRR product OMCLDRR. This level-2G global cloud product (OMCLDRRG) provides effective cloud pressure and effective cloud fraction that is based on the least square fitting of the Ring spectrum (filling-in of Fraunhofer lines in the range 392 to 398 nm due to rotational Raman scattering). This product also contains many ancillary and derived parameters, terrain and geolocation information, solar and satellite viewing angles, and quality flags. The algorithm lead for the products OMCLDRR and OMCLDRRG is NASA OMI scientist Dr. Joanna Joinner. OMCLDRRG data product is a special Level-2G Gridded Global Product where pixel level data (OMCLDRR)are binned into 0.25x0.25 degree global grids. It contains the OMCLDRR data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without Averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMCLDRRG data products are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (~14 orbits). The average file size for the OMCLDRRG data product is about 75 Mbytes.

OMHCHO

The Aura Ozone Monitoring Instrument (OMI) Version-3 Formaldehyde Product OMHCHO is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The shortname for this Level-2 OMI total column Formaldehyde product is OMHCHO. The algorithm leads for this product are the US OMI scientists Dr. Kelly Chance and Dr. Thomas Kurosu from the Harvard-Smithsonian Center, Cambridge, MA. The OMHCHO product contains total vertical column HCHO, standard errors (rms and sigma), quality flags, geolocation and other ancillary information. The OMHCHO files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The average file size for the OMHCHO data product is about 5 MB.

OMHCHOG

This Level-2G daily global gridded product OMHCHOG is based on the pixel level OMI Level-2 HCHO product OMHCHO. OMHCHOG data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree grids. It contains data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without Averaging (third dimension provides indexing for the data points in each small grid). Scientists can apply a data filtering scheme of their choice and create Level-3 global gridded products. The OMHCHOG data product contains almost all parameters (e.g. total vertical column HCHO, standard errors, quality flags, geolocation and ancillary information) that are contained in the OMHCHO product. The OMHCHOG data are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portions of 14 to 15 orbits that cover the globe in a day. The average file size for the OMGCHOG data product is about 55 Mbytes.

OMHCHOd

The OMI/Aura Formaldehyde (HCHO) Total Column Daily L3 Weighted Mean Global 0.1deg Lat/Lon Grid (OMHCHOd). The formaldehyde values in each file are the average for 0.1 x 0.1 degree grid cell of cloud-screened total HCHO columns for a single day. Other variables included in the files are the weight of each grid cell, the standard error of column averages, mean albedo, mean cloud fraction, mean cloud pressure, and surface height. The weight information is useful for combining data from several files and reducing the noise of the retrievals by co-adding in the temporal or spatial dimensions. The OMHCHOd files are in the netCDF4 format which is compatible with most HDF5 readers and tools. Each file contains daily data from approximately 15 orbits. The maximum file size for the OMHCHOd data product is about 80 Mbytes.

OMGLER

The OMI/Aura Global Geometry-Dependent Surface LER 1-Orbit L2 Swath 13x24km product, or OMGLER, provides GLER, and the computed top-of-atmosphere (TOA) radiance from which GLER is derived, for the OMI field of view. The OMGLER data also contain a number of ancillary/input parameters for each OMI pixel used to compute TOA radiance. The primary intended use of the product is to provide surface reflectance information for OMI cloud, aerosol and trace gas algorithms. GLER is designed to easily replace commonly used LER climatologies within existing OMI algorithms. The product lead is Joanna J. Joiner (OMI US science team leader). The algorithm developer is Wenhan Qin. The OMGLER product file is produced in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains GLER data for the daylit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. Files are roughly 9 MB in size.

OMPIXCOR

The Version-3 Aura Ozone Monitoring Instrument (OMI) Pixel Corner Product, OMPIXCOR, is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The shortname for this Level-2 OMI product is OMPIXCOR. The algorithm lead for this product is the US OMI scientists Dr. Thomas Kurosu from the Harvard-Smithsonian Center, Cambridge, MA. The OMPIXCOR product contains ground locations of the OMI pixel corners in the global scanning mode. The motivation for the development of the OMI ground pixel corner products was the common need for: the visualization of derived OMI data products, the provision of ground pixel area for computations of trace gas emissions per area, the facilitation of the development of cross-platform pixel mapping applications (e.g., between OMI and MODIS), and to generally aid validation studies, to name just a few. The OMPIXCOR files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes) . There are approximately 14 orbits per day. The average file size for the OMPIXCOR data product is about 5 Mbytes.

OML1BIRR

The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) UV Averaged Solar Irradiances product (shortname OML1BIRR) contains the averaged radiometrically calibrated irradiance measurements from the UV and VIS detectors. The OMI UV Band 1 (264-311 nm) has 159 wavelength bins, the UV Band 2 (307-383 nm) has 559 wavelength bins, and the VIS Band 3 (349-504 nm) has 751 wavelength bins. The data files are written in netCDF version 4 format and are usually made once per day when the Sun is within the solar port field-of-view just before the spacecraft moves into the night shadow at the north end of an orbit. The lead algorithm scientist for this product is Quintus Kleipool from the Royal Netherlands Meteorological Institude (KNMI).

OML1BIRR

The OMI Level 1B solar irradiance product is the radiometrically calibrated and geolocated measurements of the UV and Visible channels of the spectral solar irradiance. It is the averaged measurements of the solar irradiances over a single solar observation in the wavelength ranges of UV1 (264-311 nm, 159 channels), UV2 (307-383 nm, 557 channels) and VIS (349-504 nm, 751 channels). The data contain solar measurement products for both the global and the spatial zoom-in mode. This product only contains measurements obtained with the quartz volume diffuser and provides average of the individual measurements made along track to average out the solar elevation dependent bidirectional reflectance distribution function (BRDF) features of the diffuser. The shortname for this OMI Level-1B Product is OML1BIRR. The lead algorithm scientists for this product is Dr. Marcel Dobber from the Roayl Netherlands Meteorological Institude (KNMI). OMI calibrated and geolocated radiances for the UV and Visible channels, spectral irradiances, calibration measurements, and all derived geophysical atmospheric products are archived at the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). OML1BIRR files are stored in the HDF4 based EOS Hierarchical Data Format. The radiances for the earth measurements (also referred as signal) and its precision are stored as a 16 bit mantissa and an 8-bit exponent. The signal can be computed using the equation: signal = mantissa x 10^exponent. For the precision, the same exponent is used as for the signal.

OML1BRUG

The Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View UV Radiance, Global-Mode (OML1BRUG) Version-3 product contains geo-located Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm conducted in the global measurement mode. In the standard global measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath for each of the 557 channels of UV2 (307-383 nm) and 30 ground pixels (13 km x 48 km at nadir) for the 159 channels of UV1 (264-311 nm). Each file contains data from the day lit portion of an orbit (~53 minutes) and is roughly 180 MB in size. There are approximately 14 orbits per day. Once a month, in one orbit, OMI performs dark measurements, it does not perform radiance measurements. In addition, OMI performs spatial zoom measurements one day per month. For that day, this product also contains UV2 measurements that are rebinned from the spatial zoom-in measurements. In original spatial zoom mode the nadir ground pixel size is 13 x 12 km and measurements are available only for the UV2 and VIS wavelengths (306 to 432 nm). The shortname for this OMI Level-1B Product is OML1BRUG. The lead algorithm scientist for this product is Dr. Marcel Dobber from the Royal Netherlands Meteorological Institude (KNMI). The OML1BRUG files are stored in the HDF4 based EOS Hierarchical Data Format (HDF-EOS). The radiances for the earth measurements (also referred as signal) and its precision are stored as a 16 bit mantissa and an 8-bit exponent. The signal can be computed using the equation: signal = mantissa x 10^exponent. For the precision, the same exponent is used as for the signal.

OML1BRUG

The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) Geolocated Earthshine UV Radiance, Global-mode (shortname OML1BRUG) Version 4 product contains geolocated Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm taken in the global measurement mode. In the global mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath (~2600 km) for each of the 557 channels of Band 2 (307-383 nm) and 30 ground pixels (13 km x 48 km at nadir) for the 159 channels of Band 1 (264-311 nm). There are approximately 14 files of orbital data per day. Each file contains data from the daylit portion of an orbit and is roughly 210 MB in size. This OML1BRUG global-mode product is occasionally unavailable when the instrument is collecting data in the zoom-mode or is making special calibration measurements. The data in the OML1BRUG files are stored in the Network Common Data Form (netCDF) format. The lead algorithm scientist for the OMI Level 1 products is Dr. Quintus Kleipool of the Royal Netherlands Meteorological Institude (KNMI).

OML1BRUZ

The Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View UV Radiance, Zoom-in-Mode (OML1BRUZ) Version-3 product contains geo-located Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm using spectral and spatial zoom-in measurement modes. In zoom-in measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath. Each file contains data from the day lit portion of an orbit (~60 minutes) and is roughly 215 MB in size. There are approximately 14 orbits per day. OMI performs spatial zoom-in measurements one day per month. For that day, this product also contains UV2 measurements that are rebinned from the spatial zoom-in measurements. The shortname for this OMI Level-1B Product is OML1BRUZ. The lead algorithm scientist for this product is Dr. Marcel Dobber from the Royal Netherlands Meteorological Institude (KNMI). The OML1BRUZ files are stored in HDF4 based EOS Hierarchical Data Format (HDF-EOS). The radiances for the earth measurements (also referred as signal) and its precision are stored as a 16 bit mantissa and an 8-bit exponent. The signal can be computed using the equation: signal = mantissa x 10^exponent. For the precision, the same exponent is used as for the signal.

OML1BRUZ

The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) Geolocated Earthshine UV Radiance, Zoom-mode (shortname OML1BRUZ) Version 4 product contains geolocated Earth view spectral radiances from the UV detectors in the wavelength range of 264 to 383 nm taken in the global measurement mode. In the zoom-in mode, OMI observes 60 ground pixels (13 km x 12 km at nadir) across the swath (~~750 km width) for each of the 557 channels of Band 2 (307-383 nm) and 30 ground pixels (13 km x 24 km at nadir) across the swath (~~2600 km) for the 159 channels of Band 1 (264-311 nm). There are approximately 14 files of orbital data per day. Each file contains data from the daylit portion of an orbit and is roughly 210 MB in size. This OML1BRUZ zoom-in mode product is only available about once a month. The data in the OML1BRUZ files are stored in the Network Common Data Form (netCDF) format. The lead algorithm scientist for the OMI Level 1 products is Dr. Quintus Kleipool of the Royal Netherlands Meteorological Institude (KNMI).

OML1BRVG

The Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View VIS Radiance, Global-Mode (OML1BRVG) Version-3 product contains geo-located Earth view spectral radiances from the VIS detector in the wavelength range of 349 to 504 nm conducted in the global measurement mode. In the standard global measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath (13 km x 48 km at nadir). Each file contains data from the day lit portion of an orbit (~60 minutes) and is roughly 200 MB in size. There are approximately 14 orbits per day. Once a month, in one orbit, OMI performs dark measurements, it does not perform radiance measurements. In addition, OMI performs spatial zoom measurements one day per month. For that day, this product also contains UV2 measurements that are rebinned from the spatial zoom-in measurements. In original spatial zoom mode the nadir ground pixel size is 13 x 12 km and measurements are available only for the UV2 and VIS wavelengths (306 to 432 nm). The shortname for this OMI Level-1B Product is OML1BRVG. The lead algorithm scientist for this product is Dr. Marcel Dobber from the Royal Netherlands Meteorological Institude (KNMI). The OML1BRVG files are stored in the HDF4 based EOS Hierarchical Data Format (HDF-EOS). The radiances for the earth measurements (also referred as signal) and its precision are stored as a 16-bit mantissa and an 8-bit exponent. The signal can be computed using the equation: signal = mantissa x 10^exponent. For the precision, the same exponent is used as for the signal.

OML1BRVG

The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) Geolocated Earthshine VIS Radiance, Global-mode (shortname OML1BRVG) Version 4 product contains geolocated Earth view spectral radiances from the VIS detectors in the wavelength range of 349 to 504 nm taken in the global measurement mode. In the global mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath (~2600 km) for each of the 751 channels of Band 3 (349-504 nm). There are approximately 14 files of orbital data per day. Each file contains data from the daylit portion of an orbit and is roughly 240 MB in size. This OML1BRVG global-mode product is occasionally unavailable when the instrument is collecting data in the zoom-mode or is making special calibration measurements. The data in the OML1BRVG files are stored in the Network Common Data Form (netCDF) format. The lead algorithm scientist for the OMI Level 1 products is Dr. Quintus Kleipool of the Royal Netherlands Meteorological Institude (KNMI).

OML1BRVZ

The Aura Ozone Monitoring Instrument (OMI) Level-1B (L1B) Geo-located Earth View VIS Radiance, Zoom-in-Mode (OML1BRVZ) Version-3 product contains geo-located Earth view spectral radiances from the VIS detectors in the wavelength range of 349 to 504 nm using spectral and spatial zoom-in measurement modes. In zoom-in measurement mode, OMI observes 60 ground pixels (13 km x 24 km at nadir) across the swath. Each file contains data from the day lit portion of an orbit (~60 minutes) and is roughly 190 MB in size. There are approximately 14 orbits per day. OMI performs spatial zoom-in measurements one day per month. For that day, this product also contains VIS measurements that are rebinned from the spatial zoom-in measurements. The lead algorithm scientist for this product is Dr. Marcel Dobber from the Royal Netherlands Meteorological Institude (KNMI). The OML1BRVZ files are stored in the HDF4 based EOS Hierarchical Data Format (HDF-EOS). The radiances for the earth measurements (also referred as signal) and its precision are stored as a 16 bit mantissa and an 8-bit exponent. The signal can be computed using the equation: signal = mantissa x 10^exponent. For the precision, the same exponent is used as for the signal.

OML1BRVZ

The Aura Ozone Monitoring Instrument (OMI) Level 1B (L1B) Zoom-in Earthshine UV Radiance, Zoom-mode (shortname OML1BRVZ) Version 4 product contains geolocated Earth view spectral radiances from the VIS detectors in the wavelength range of 349 to 504 nm taken in the zoom-in measurement mode. In the zoom-in mode, OMI observes 60 ground pixels (13 km x 12 km at nadir) across the swath (~750 km width) for each of the 751 channels of Band 3 (349-504 nm). There are approximately 14 files of orbital data per day. Each file contains data from the daylit portion of an orbit and is roughly 240 MB in size. This OML1BRVZ zoom-in mode product is only available about once a month. The data in the OML1BRVZ files are stored in the Network Common Data Form (netCDF) format. The lead algorithm scientist for the OMI Level 1 products is Dr. Quintus Kleipool of the Royal Netherlands Meteorological Institude (KNMI).

OMAERO

The Level-2 Aura Ozone Monitoring Instrument (OMI) Aerosol Product (OMAERO) is now available from NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. This is the second public release of version 003. The data was re-processed in late 2011 using an improved algorithm (processing version 1.2.3.1). After some quick validation the reprocessed data was released to the public in March 2012. The shortname for this Level-2 Aerosol Product is OMAERO_V003. There are two Level-2 Aura OMI aerosol products OMAERUV and OMAERO. The OMAERUV product uses the near-UV algorithm. The OMAERO product is based on the multi-wavelength algorithm and that uses up to 20 wavelength bands between 331 nm and 500 nm. OMAERO retrieval algorithm is developed by the KNMI OMI Team Scientists. Drs. Deborah Stein-Zweers, Martin Sneep and Pepijn Veefkind are now the key investigators of this product. The OMAERO product contains Aerosol Optical Depths, Single Scattering Albedo, and other ancillary and geolocation information. The OMAERO files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERO data product is about 6 Mbytes.

OMAEROG

This Level-2G daily global gridded product OMAEROG is based on the pixel level OMI Level-2 Aerosol product OMAERO, based on the multi-wavelength algorithm that uses up to 20 wavelength bands between 331 nm and 500 nm. OMAEROG is a special Level-2 gridded product where pixel level products are binned into 0.25 x 0.25 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMAEROG data product contains almost all parameters that are in OMAERO. For example, in addition to the extinction optical depth and single scattering albedo, it also contains aerosol indices, cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and extensive quality flags. The OMAEROG files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMAEROG data product is about 78 Mbytes.

OMAEROe

The OMI science team produces this Level-3 Aura/OMI Global Aerosol Data Products OMAEROe (0.25deg Lat/Lon grids). The OMAEROe product selects best aerosol value from the Level2G good quality data that are reported in each grid, based on the multi-wavelength algorithm that uses up to 20 wavelength bands between 331 nm and 500 nm. The selection criteria is based on the shortest optical path length (secant of solar zenith angle + secant of viewing zenith angle). The OMAEROe files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMAEROe data product is about 7 Mbytes. (The shortname for this Level-3 Global Gridded Aerosol Product is OMAEROe)

OMAERUV

The Aura Ozone Monitoring Instrument level-2 near UV Aerosol data product 'OMAERUV', recently re-processed using an enhanced algorithm, is now released (April 2012) to the public. The data are available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC). The shortname for this Level-2 near-UV Aerosol Product is OMAERUV_V003. The OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Absorption and Aerosol Extinction Optical Depths, and Single Scattering Albedo at three different wavelengths (354, 388 and 500 nm), Aerosol Index, and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). The OMAERUV files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 6 Mbytes.

OMAERUV

The Aura Ozone Monitoring Instrument level-2 near UV Aerosol data product OMAERUV (Version 004) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The OMAERUV retrieval algorithm is developed by the US OMI Team Scientists. Dr. Omar Torres (GSFC/NASA) is the principal investigator of this product. The OMAERUV product contains Aerosol Optical Depth, Aerosol Single Scattering Albedo, Absorption Optical Depth, UV Aerosol Index, and Aerosol Optical Depth over clouds at three wavelengths (354, 388, and 500 nm), and other ancillary and geolocation parameters, in the OMI field of view (13x24 km). The OMAERUV files are stored in the version 4.0 Network Common Data Form (NetCDF). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMAERUV data product is about 17 Mbytes.

OMAERUVG

This Level-2G daily global gridded product OMAERUVG is based on the pixel level OMI Level-2 AERUV product OMAERUV. This Level-2G daily global gridded product OMAERUVG is based on the pixel level OMI Level-2 Aerosol product OMAERUV. OMAERUVG data product is a special Level-2 gridded product where pixel level products are binned into 0.25x0.25 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMAERUVG files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits mapped on the Global 0.25x0.25 deg Grids. The maximum file size for the OMAERUVG data product is about 50 Mbytes.

OMAERUVd

The OMI science team produces this Level-3 daily global gridded product OMAERUVd (1 deg Lat/Lon grids). The OMAERUVd product is produced with all data pixels that fall in a grid box with quality filtered and then averaged, based on the pixel level OMI Level-2 Aerosol data product OMAERUV. The OMAERUV data product is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data. The OMAERUVd data product contains extinction and absorption optical depths at three wavelenghts (355 nm, 388 nm and 500 nm). The OMAERUVd files are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMAERUVd data product is about 0.2 Mbytes.

OMNO2

The Version 4.0 Aura Ozone Monitoring Instrument (OMI) Nitrogen Dioxide (NO2) Standard Product (OMNO2) is now available from the NASA Goddard Earth Sciences Data and Information Services Center. The major V4.0 updates include: (1) use of a new daily and OMI ﬁeld of view speciﬁc geometry dependent surface Lambertian Equivalent Reﬂectivity (GLER) product in both NO2 and cloud retrievals; (2) use of improved cloud parameters (eﬀective cloud fraction and cloud optical centroid pressure) from a new cloud algorithm (OMCDO2N) that are retrieved consistently with NO2 using a new algorithm for O2-O2 slant column data and the GLER product for terrain reﬂectivity; (3) use of a more accurate terrain pressure calculated using OMI ground pixel-averaged terrain height and monthly mean GMI terrain pressure; and (4) improved treatment over snow/ice surfaces by using the concept of scene LER and scene pressure. The details can be found in the updated OMNO2 readme document (see Documentation). The OMNO2 product contains slant column NO2 (total amount along the average optical path from the sun into the atmosphere, and then toward the satellite), the total NO2 vertical column density (VCD), the stratospheric and tropospheric VCDs, air mass factors (AMFs), scattering weights for calculation of AMFs, and other ancillary data. The short name for the Level-2 swath type column NO2 products is OMNO2. Other OMNO2-associated NO2 products include the Level-2 gridded column product, OMNO2G, and the Level-3 gridded column product, OMNO2d. The OMNO2 files are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each Level-2 file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMNO2 is ~24 MB.

OMNO2

The Collection 4, Version 5.0, Aura Ozone Monitoring Instrument (OMI) Nitrogen Dioxide (NO2) Standard Product (OMNO2) is now available from the NASA Goddard Earth Sciences Data and Information Services Center. The major V5.0 updates include the use of: (1) improved Collection 4 Level-1B radiance and irradiance data in the retrieval of NO2 and O2-O2 slant columns; (2) a-priori NO2 profiles and other model-derived information from a high-resolution (25 km) Global Modeling Initiative (GMI) simulation; (3) a new algorithm for improved de-striping and data flagging to correct for cross-track artifacts and row-anomaly; (4) updated geometry dependent surface Lambertian Equivalent Reﬂectivity (GLER) and consistently-retrieved O2-O2 cloud products; and (5) improved snow/ice database and treatment in the GLER, cloud, and NO2 algorithms. The details can be found in the updated OMNO2 readme document (see Documentation). The OMNO2 product contains NO2 slant column density (SCD), total NO2 vertical column density (VCD), the stratospheric and tropospheric VCDs, air mass factors (AMFs), scattering weights for AMF calculation, and other ancillary data used in the OMNO2 algorithm. The OMI NO2 files are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). The short name for the Level-2 swath type column NO2 products is OMNO2. Each Level-2 file contains data from the day lit portion of an orbit (53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMNO2 is ~21 MB. Other OMNO2-associated NO2 products include the Level-2 gridded column product, OMNO2G, and the Level-3 gridded column product, OMNO2d.

OMNO2d

This is Level-3 daily global gridded (0.25x0.25 degree) Nitrogen Dioxide Product (OMNO2d). OMNO2d data product is a Level-3 Gridded Product where pixel level data of good quality are binned and "averaged" into 0.25x0.25 degree global grids. This product contains Total column NO2 and Total Tropospheric Column NO2, for all atmospheric conditions, and for sky conditions where cloud fraction is less than 30 percent. Nitrogen dioxide is an important chemical species in both, the stratosphere where it plays a key role in ozone chemistry, and in the troposphere where it is a precursor to ozone production. In the troposphere, it is produced in various combustion processes and in lightning and is an indicator of poor air quality. The OMNO2d data are stored in version 5 EOS Hierarchical Data Format (HDF-EOS). Each file contains data from the day lit portion of the orbit (~14 orbits). The average file size for the OMNO2d data product is about 12 Mbytes.

OMNO2G

This Level-2G daily global gridded product OMNO2G is based on the pixel level OMI Level-2 NO2 product OMNO2. OMNO2G data product is a special Level-2 Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All data pixels that fall in a rid box are saved Without Averaging. Nitrogen dioxide is an important chemical species in both the stratosphere, where it plays a key role in ozone chemistry, and in the troposphere, where it is a precursor to ozone production. In the troposphere, it is produced in various combustion processes and in lightning and is an indicator of poor air quality. The OMNO2G data product contains almost all parameters that are contained in OMNO2 product. The OMNO2G data are stored in version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of the orbit (~14 orbits). The average file size for the OMNO2G data product is about 115 Mbytes.

OMDOAO3G

This Level-2G daily global gridded product OMDOAO3G is based on the pixel level OMI Level-2 DOAO3 product OMDOAO3. This Level-2G global total column ozone product is derived from OMDOAO3 which is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI visible radiance values between 331.1 and 336.1 nm. In addition to the total ozone column this product also contains some auxiliary derived and ancillary input parameters, e.g. ozone slant column density, ozone ghost column density, etc. The short name for this Level-2 OMI ozone product is OMDOAO3G and the lead algorithm scientist for this product and for OMDOAO3 (the data source of OMDOAO3G) is Dr. Pepijn Veefkind from KNMI. The OMDOAO3G product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (approximately 14 orbits) and is roughly 80 MB in size.

OMDOAO3e

The OMI science team produces this Level-3 Aura/OMI Global OMDOAO3e Data Products (0.25deg Lat/Lon grids). This Level-3 global total column ozone product is derived from OMDOAO3 which is based on the Differential Absorption Spectroscopy (DOAS) fitting technique that essentially uses the OMI visible radiance values between 331.1 and 336.1 nm. In addition to the total ozone column (best quality data, satisfying the shortest path length) and its precision this product also contains some ancillary parameters such as cloud fraction, cloud height, etc. The short name for this Level-3 OMI ozone product is OMDOAO3e and the lead Algorithm scientist for this product and for OMDOAO3 (the data source of OMDOAO3e) is Dr. Pepijn Veefkind from KNMI. The OMDOAO3e product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each daily file contains data from the day lit portion of the orbits (approximately 14 orbits) and is roughly 8 MB in size.

OMO3PR

The Aura Ozone Monitoring Instrument Level-2 Ozone Profile data product OMO3PR (Version 003) is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI Level-2 ozone profile product, OMO3PR at the pixel resolution 13x 48 km (at nadir), is based on the optimal estimation algorithm (Rodgers, 2000) with climatological ozone profiles as a-priori information. The OMO3PR retrieval algorithm uses spectral radiance values from the UV1 channel (270 nm to 308.5 nm) and from the first part of the UV2 channel (311.5 nm to 33 0 nm). OMO3PR product provides ozone values (in Dobson unit) for 18 atmospheric layers. It also provides a-priori ozone profile values, error covariance matrix, averaging kernel and some ancillary information such as time, latitude, longitude, solar zenith and viewing zenith angles and quality flags. The short name for this Level-2 OMI ozone profile product is OMO3PR. The lead scientist for this product is Dr. Johan de Haan. The OMO3PR product files are stored in the version 5 Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (approximately 53 minutes). There are approximately 14 orbits per day thus the total data volume is approximately 150 GB/day.

OMTO3G

This Level-2G daily global gridded product OMTO3G is based on the pixel level OMI Level-2 Total Ozone Product OMTO3. The OMTO3 product is from the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. The OMTO3G data product is a special Level-2 Global Gridded Product where pixel level data are binned into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time for the 24-hour period beginning at 00:00:00 UTC. All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMTO3G data product contains almost all parameters that are contained in the OMTO3. For example, in addition to the total column ozone it also contains cloud fraction, cloud pressure, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The OMTO3G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 150 Mbytes.

OMTO3

The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Version 003) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. OMI provides two Level-2 (OMTO3 and OMDOAO3) total column ozone products at pixel resolution (13 x 24 km at nadir) that are based on two different algorithms. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, UV aerosol index, SO2 index, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm lead for this product is NASA OMI scientist Dr. Pawan K. Bhartia. The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB.

OMTO3

The Aura Ozone Monitoring Instrument (OMI) Level-2 Total Column Ozone Data Product OMTO3 (Collection Version 004) is available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. This level-2 global total column ozone product (OMTO3) is based on the enhanced TOMS version-8 algorithm that essentially uses the ultraviolet radiance data at 317.5 and 331.2 nm at a pixel resolution of 13 x 24 km at nadir. OMI hyper-spectral measurements help in the corrections for the factors that induce uncertainty in ozone retrievals (e.g., cloud and aerosol, sea-glint effects, profile shape sensitivity, SO2 and other trace gas contamination). In addition to the total ozone values this product also contains some auxiliary derived and ancillary input parameters including N-values, effective Lambertian scene-reflectivity, cloud fraction, cloud pressure, ozone below clouds, terrain height, geolocation, solar and satellite viewing angles, and quality flags. The shortname for this Level-2 OMI total column ozone product is OMTO3. The algorithm for this product was originally developed by a team led by Dr. Pawan K. Bhartia at NASA Goddard Space Flight Center. The current product lead is Dr. Can Li. The OMTO3 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMTO3 data product is approximately 35 MB.

OMSO2e

The OMI science team produces this Level-3 Aura/OMI Global OMSO2e Data Products (0.25 degree Latitude/Longitude grids). In this Level-3 daily global SO2 data product, each grid contains only one observation of Total Column Density of SO2 in the Planetary Boundary Layer (PBL), based on an improved Principal Component Analysis (PCA) Algorithm. This single observation is the "best pixel", selected from all "good" L2 pixels of OMSO2 that overlap this grid and have UTC time between UTC times of 00:00:00 and 23:59:59.999. In addition to the SO2 Vertical column value some ancillary parameters, e.g., cloud fraction, terrain height, scene number, solar and satellite viewing angles, row anomaly flags, and quality flags have been also made available corresponding to the best selected SO2 data pixel in each grid. The OMSO2e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5) using the grid model.

OMSO2

The Aura Ozone Monitoring Instrument (OMI) level 2 sulphur dioxide (SO2) total column product (OMSO2) has been updated with a principal component analysis (PCA)-based algorithm (v2) with new SO2 Jacobian lookup tables and a priori profiles that significantly improve retrievals for anthropogenic SO2. The data files (or granules) contain different estimates of the vertical column density (VCD) of SO2 depending on the users investigating anthropogenic or volcanic sources. Files also contain quality flags, geolocation and other ancillary information. The lead scientist for the OMSO2 product is Can Li. The OMSO2 files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the daylit half of an orbit (~53 minutes). There are approximately 14 orbits per day. The resolution of the data is 13x24 km2 at nadir, with a swath width of 2600 km and 60 pixels per scan line every 2 seconds.

OMSO2G

This Level-2G daily global gridded product OMSO2G is based on the pixel level OMI Level-2 SO2 product OMSO2. OMSO2G data product is a special Level-2 gridded product where pixel level products are binned into 0.125x0.125 degree global grids. It contains the data for all scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999 . All data pixels that fall in a grid box are saved without averaging. Scientists can apply a data filtering scheme of their choice and create new gridded products. The OMSO2G data product contains almost all parameters that are contained in OMSO2 files. For example, in addition to three values of SO2 Vertical column corresponding to three a-priori vertical profiles used in the retrieval algorithm, and ancillary parameters, e.g., UV aerosol index, cloud fraction, cloud pressure, geolocation, solar and satellite viewing angles, and quality flags. The OMSO2G files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3G data product is about 146 Mbytes.

OMLER

The OMI Earth Surface Reflectance Climatology product, OMLER (Global 0.5 degrees Lat/Lon grid) which is based on Version 003 Level-1B top of atmosphere upwelling radiance and incoming irradiance. OMI calibrated and geolocated radiances from 159 channels in UV1(264-311 nm), 557 channels in UV2 (307-383 nm) and 751 channels in VIS (349-504) spectral regions, spectral irradiances, calibration measurements, and all derived geophysical atmospheric products (Level-2 and 3) are archived at the NASA Goddard DAAC. OMLER spectral surface reflectance product contains monthly and yearly climatology of the Earth's surface Lambert Equivalent Reflectance (LER) for 23 wavelengths in the spectral range 309 to 500 nm, at a spatial resolution of 0.5 by 0.5 degrees. This LER is defined as the required reflectance of an isotropic surface needed to match the observed top of the atmosphere (TOA) reflectance in a pure Rayleigh scattering atmosphere under cloud free conditions and no aerosols. The climatology is based on statistical analysis of the three years of OMI version 03 radiance data (Oct 2004-Oct 2007). This product also provides minimum spectral surface reflectivity observed during the three year period. The OMLER product file is produced in the version 5 Hierarchical Data Format (HDF-EOS5). It is roughly 300 MB in size.

OMUVB

The Aura Ozone Monitoring Instrument (OMI) Version 003 Surface UV Irradiance Product (OMUVB) is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for the public access. The shortname for this Level-2 OMI Surface UVB product is OMUVB. The algorithm scientists for this product are: Dr. Jari Hovila, Dr. Antii Arola and Dr. Johanna Taminnen. The OMUVB product contains erythemally weighted daily dose and dose rate, and spectral irradiances at 305, 310, 324, and 380 nm. It also contains quality flags, cloud optical depth, Lambertian Equivalent Reflectivity, Total Column Ozone amount, and other ancillary information. The OMUVB files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes). There are approximately 14 orbits per day. The maximum file size for the OMUVB data product is about 10 Mbytes.

OMUVBG

This is Level-2G daily global gridded Aura-OMI Spectral Surface UVB Irradiance and Erythemal Dose product (OMUVBG). The OMUVBG is a special Level-2 Global Gridded type data Product (referred as Level 2G or L2G) where Level-2 or swath pixel data are binned (but not averaged)into 0.25x0.25 degree global grids. It contains the data for all L2 scenes that have observation time between UTC times of 00:00:00 and 23:59:59.9999. All ancillary parameters such as latitude, longitude, time, solar and viewing angles are also saved for each pixel. First two dimensions of each parameter correspond to spatial (Lat/Lon based) Grid ID and third dimension identifies the pixel or observed scene (referred as 'candidates' ID). Scientists can apply a data filtering scheme of their choice, average good quality pixels data in each grid and create their Level-3 products. The OMUVBG files are available in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from the day lit portion of the globe. The maximum file size for the OMUVBG data product is about 128 MBytes.

OMUVBd

This is Level-3 daily global gridded Aura-OMI Spectral Surface UVB Irradiance and Erythemal Dose product (OMUVBd). The OMUVBd product contains global erythemally weighted daily dose and erythemal dose rate at local solar noon at 1.0x1.0 deg grids. The OMUVBd files are available in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from the day lit portion of the globe. The maximum file size for the OMUVBd data product is about 5 MBytes.

OMTO3e

The OMI science team produces this Level-3 Aura/OMI Global TOMS-Like Total Column Ozone gridded product OMTO3e (0.25deg Lat/Lon grids). The OMTO3e product selects the best pixel (shortest path length) data from the good quality filtered level-2 total column ozone data (OMTO3) that fall in the 0.25 x 0.25 degree global grids. Each file contains total column ozone, radiative cloud fraction and solar and viewing zenith angles. The OMTO3e files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3e data product is about 2.8 Mbytes.

OMTO3d

The OMI science team produces this Level-3 daily global TOMS-Like Total Column Ozone gridded product OMTO3d (1 deg Lat/Lon grids). The OMTO3d product is produced by gridding and averaging only good quality level-2 total column ozone orbital swath data (OMTO3, based on the enhanced TOMS version-8 algorithm) on the 1x1 degree global grids. The OMTO3d files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains daily data from approximately 15 orbits. The maximum file size for the OMTO3d data product is about 0.65 Mbytes.

OMPIXCORZ

The Version-3 Aura Ozone Monitoring Instrument (OMI) Pixel Corner Product in zoom-in mode, OMPIXCORZ, is now available from the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The shortname for this Level-2 OMI product is OMPIXCORZ. The algorithm lead for this product is the US OMI scientists Dr. Thomas Kurosu from the Harvard-Smithsonian Center, Cambridge, MA. The OMPIXCORZ product contains ground locations of the OMI pixel corners in the zoom-in scanning mode. The motivation for the development of the OMI ground pixel corner products was the common need for: the visualization of derived OMI data products, the provision of ground pixel area for computations of trace gas emissions per area, the facilitation of the development of cross-platform pixel mapping applications (e.g., between OMI and MODIS), and to generally aid validation studies, to name just a few. The OMPIXCORZ files are stored in the version 5 EOS Hierarchical Data Format (HDF-EOS5). Each file contains data from the day lit portion of an orbit (~53 minutes) . There are approximately 14 orbits approximately one day per month. The average file size for the OMPIXCORZ data product is about 8 Mbytes.

OMUANC

The Primary Ancillary Data Geo-Colocated to OMI/Aura UV2 1-Orbit L2 Swath 13x24km (OMUANC) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include snow cover, sea ice cover, land cover, terrain height, row anomaly flag, and pixel area. The OMI team also provides a corresponding product for the OMI VIS swath, OMVANC. This product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. The OMUANC files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.

OMVANC

The Primary Ancillary Data Geo-Colocated to OMI/Aura VIS 1-Orbit L2 Swath 13x24km (OMVANC) provides selected parameters from GEOS-5 Forward Processing for Instrument Teams (FP-IT) assimilated product produced by the Global Modeling and Assimilation Office (GMAO) co-located in space and time with the OMI UV-2 swath. The fields in this product include snow cover, sea ice cover, land cover, terrain height, row anomaly flag, and pixel area. The OMI team also provides a corresponding product for the OMI UV2 swath, OMUANC. This product has been generated for convenient use by the OMI/Aura team in their L2 algorithms, and for research where those L2 products are used. The original GEOS-5 FP-IT data are reported on a 0.625 deg longitude by 0.5 deg latitude grid, whereas the OMI UV-2 spatial resolution is 13km x 24km at nadir. The OMVANC files are in netCDF4 format which is compatible with most netCDF and HDF5 readers and tools. Each file is approximately 45mb in size. The lead for this product is Zachary Fasnacht of SSAI. Joanna Joiner is the responsible NASA official.

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