The Amazon Sustainability Data Initiative (ASDI) seeks to accelerate sustainability research and innovation by minimizing the cost and time required to acquire and analyze large sustainability datasets. These datasets are publicly available to anyone. In addition, ASDI provides cloud grants to those interested in exploring the use of AWS’ technology and scalable infrastructure to solve big, long-term sustainability challenges with this data. The dual-pronged approach allows sustainability researchers to analyze massive amounts of data in mere minutes, regardless of where they are in the world or how much local storage space or computing capacity they can access. Learn more about ASDI here.
Categories: weather, climate, water, agriculture, satellite imagery, elevation, air quality, energy, disaster response, oceans, socioeconomic, infrastructure, ecosystems, biodiversity, sustainability
If you want to add a dataset or example of how to use a dataset to this registry, please follow the instructions on the Registry of Open Data on AWS GitHub repository.
Unless specifically stated in the applicable dataset documentation, datasets available through the Registry of Open Data on AWS are not provided and maintained by AWS. Datasets are provided and maintained by a variety of third parties under a variety of licenses. Please check dataset licenses and related documentation to determine if a dataset may be used for your application.
If you have a project using a listed dataset, please tell us about it. We may work with you to feature your project in a blog post.
Managed by NOAA
The GraphCast Global Forecast System (GraphCastGFS) is an experimental system set up by the National Centers for Environmental Prediction (NCEP) to produce medium range global forecasts. The horizontal resolution is a 0.25 degree latitude-longitude grid (about 28 km). The model runs 4 times a day at 00Z, 06Z, 12Z and 18Z cycles. Major atmospheric and surface fields including temperature, wind components, geopotential height, specific humidity, and vertical velocity, are available. The products are 6 hourly forecasts up to 10 days. The data format is GRIB2.
The GraphCastGFS system is an experimental weather forecast model built upon the pre-trained Google DeepMind’s GraphCast Machine Learning Weather Prediction (MLWP) model. The GraphCast model is implemented as a message-passing graph neural network (GNN) architecture with “encoder-processor-decoder” configuration. It uses an icosahedron grid with multiscale edges and has around 37 million parameters. This model is pre-trained with ECMWF’s ERA5 reanalysis data. The GraphCastGFSl takes two model states as initial conditions (current and 6-hr previous states) from NCEP 0.25 degree GDAS analysis data and runs GraphCast (37 levels) and GraphCast_operational (13 levels) with a pre-trained model provided by GraphCast. Unit conversion to the GDAS data is conducted to match the input data required by GraphCast and to generate forecast products consistent with GFS from GraphCastGFS’ native forecast data.
The GraphCastGFS version 2 made the following changes from the GraphcastCastGFS version 1.
Managed by OpenMeteoData
Global and high-resolution regional atmospheric models from Météo-France.
Managed by SISSA
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) we...
Managed by Central Weather Bureau
Various kinds of weather raw data and charts from Central Weather Bureau.
Managed by Finnish Meteorological Institute
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 avail...
Managed by Finnish Meteorological Institute
HIRLAM (High Resolution Limited Area Model) is an operational synoptic and mesoscale weather prediction model managed by the Finnish Meteorological Institute.
Managed by IDEAM
Historical and one-day delay data from the IDEAM radar network.
Managed by Met Office
The flagship Numerical Weather Prediction model developed and used at the Met Office, is the Unified Model, the same model is used for both weather and climate prediction. For weather forecasting the Met Office runs several configurations of the Unified Model as part of its operational Numerical Weather Prediction suite. Uncovering 2 years' worth of historical data, updated regularly with a time delay. The Global deterministic model is a global configuration of the Met Office Unified Models providing the most accurate short range deterministic forecast by any national meteorological servic...
Managed by Met Office
The flagship Numerical Weather Prediction model developed and used at the Met Office, is the Unified Model, the same model is used for both weather and climate prediction. For weather forecasting the Met Office runs several configurations of the Unified Model as part of its operational Numerical Weather Prediction suite. Uncovering 2 years' worth of historical data, updated regularly with a time delay. The UK deterministic model is a post processed regional downscaled configuration of the Unified Model, covering the UK and Ireland, with a resolution of approximately 0.018 degrees. The Unit...
Managed by Unidata
Real-time and archival data from the Next Generation Weather Radar (NEXRAD) network.
Managed by NOAA
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; ...
Managed by NOAA
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 obser...
Managed by NOAA
The Unified Forecast System (UFS) is a community-based, coupled, comprehensive Earth Modeling System. It supports multiple applications with different forecast durations and spatial domains. The Global Data Assimilation System (GDAS) Application (App) is being used as the basis for uniting the Global Workflow and Global Forecast System (GFS) model with Joint Effort for Data assimilation Integration (JEDI) capabilities.
The National Centers for Environmental Prediction (NCEP) use GDAS to interpolate data from various observing systems and instruments onto a three-dimensional grid. GDAS obtain...
Managed by NOAA
The Global Ensemble Forecast System (GEFS), previously known as the GFS Global ENSemble (GENS), is a weather forecast model made up of 21 separate forecasts, or ensemble members. The National Centers for Environmental Prediction (NCEP) started the GEFS to address the nature of uncertainty in weather observations, which is used to initialize weather forecast models. The GEFS attempts to quantify the amount of uncertainty in a forecast by generating an ensemble of multiple forecasts, each minutely different, or perturbed, from the original observations. With global coverage, GEFS is produced fo...
Managed by NOAA
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 ex...
Managed by NOAA
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...
Managed by NOAA
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.
G...
Managed by NOAA
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.
Managed by NOAA
The last several hurricane seasons have been active with records being set for the number of tropical storms and hurricanes in the Atlantic basin. These record-breaking seasons underscore the importance of accurate hurricane forecasting. Imperative to increased forecasting skill for hurricanes is the development of the Hurricane Forecast Analysis System or HAFS. To accelerate improvements in hurricane forecasting, this project has the following goals:
Managed by NOAA
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 ...
Managed by NOAA
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.
...
Managed by NOAA
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 t...
Managed by NOAA
The NOAA NASA Joint Archive (NNJA) of Observations for Earth System Reanalysis is a curated joint observation archive containing Earth system data from 1979 to present prepared by teams at NOAA's Physical Sciences Laboratory and NASA's Global Modeling and Assimilation Office. The goal is to foster collaboration across organizations and develop the ability for direct comparison of Earth System reanalysis results. Providing a singular dataset for observation input use will allow reanalyses to be compared on their unique development qualities by removing the variation from using different...
Managed by NOAA
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 with diameter equal to or less than 2.5 micrometers (PM2.5) using meteorological forecasts based on NCEP’s operational weather forecast models such as North American Mesoscale Models (NAM) and Global Forecast System (GFS), and atmospheric chemistry based on the EPA’s Community Multiscale Air Quality (CMAQ) model. In addition, the modeling system incorporates information related to chemical emissions, including anthropogenic emissions provided by the EPA and fire emissions from NOAA/NESDIS. The NCEP NAQFC AQM output fields in this archive include 72-hr forecast products of model raw and bias-correction predictions, extending back to 1 January 2020. All of the output was generated by the contemporaneous operational AQM, beginning with AQMv5 in 2020, with upgrades to AQMv6 on 20 July 2021, and AQMv7 on 14 May 2024. 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. Since that date, the third prediction system, a regional numerical weather prediction (NWP) model known as the Rapid Refresh (RAP) model, has 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, one Alaska, and the other Hawaii. Currently, for this archive, the ozone, (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. Ozone 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 mg/m2.
Temporally, O3 and PM2.5 are available as maximum and/or averaged values over various time periods. 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 model biases relative to observations.
The AQM produces hourly forecast guidance for O3 and PM2.5 out to 72 hours twice per day, starting at 0600 and 1...
Managed by NOAA
The National Blend of Models (NBM) is a nationally consistent and skillful suite of calibrated forecast guidance based on a blend of both NWS and non-NWS numerical weather prediction model data and post-processed model guidance. The goal of the NBM is to create a highly accurate, skillful and consistent starting point for the gridded forecast.
Managed by NOAA
Please note NWS is Soliciting Comments until April 30, 2024 on Availability of Probabilistic Snow Grids for Select Weather Forecast Offices (WFOs) as an Experimental Element in the National Digital Forecast Database (NDFD) for the Contiguous United States (CONUS). A PDF version of the Public Notice can be found "HERE"
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 Predictio...
Managed by NOAA
The North American Mesoscale Forecast System (NAM) is one of the National Centers For Environmental Prediction’s (NCEP) major models for producing weather forecasts. NAM generates multiple grids (or domains) of weather forecasts over the North American continent at various horizontal resolutions. Each grid contains data for dozens of weather parameters, including temperature, precipitation, lightning, and turbulent kinetic energy. NAM uses additional numerical weather models to generate high-resolution forecasts over fixed regions, and occasionally to follow significant weather events like hur...
Managed by NOAA
The Rapid Refresh (RAP) is a NOAA/NCEP operational weather prediction system comprised primarily of a numerical forecast model and analysis/assimilation system to initialize that model. It covers North America and is run with a horizontal resolution of 13 km and 50 vertical layers. The RAP was developed to serve users needing frequently updated short-range weather forecasts, including those in the US aviation community and US severe weather forecasting community. The model is run for every hour of the day; it is integrated to 51 hours for the 03/09/15/21 UTC cycles and to 21 hours for every ot...
Managed by NOAA
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 2024. 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 real time, experimental RRFS prototype output.
rrfs_a/rrfs_a.20230428/12/control contains the deterministic forecast initialized at 12 UTC on 28 April 2023. Users will find two types of output in GRIB2 format. The first is:rrfs.t00z.natlev.f018.conus_3km.grib2 rrfs.t00z.prslev.f018.conus_3km.grib2 rrfs.t00z.prslev.f002.grib2
Alaska: rrfs.t00z.prslev.f002.ak.grib2
Hawaii: rrfs.t00z.prslev.f002.hi.grib2
Puerto Rico: rrfs.t00z.prslev.f002.pr.grib2rrfs_a/rrfs_a.20231214/00/mem0001 contains the forecast from member 1, and rrfs_a/rrfs_a.20231214/00/enspost_timelag co...
Managed by NOAA
The Real-Time Mesoscale Analysis (RTMA) is a NOAA National Centers For Environmental Prediction (NCEP) high-spatial and temporal resolution analysis/assimilation system for near-surf ace weather conditions. Its main component is the NCEP/EMC Gridpoint Statistical Interpolation (GSI) system applied in two-dimensional variational mode to assimilate conventional and satellite-derived observations.
The RTMA was developed to support NDFD operations and provide field forecasters with high quality analyses for nowcasting, situational awareness, and forecast verification purposes. The system produces ...
Managed by NOAA
The Storm Events Database is an integrated database of severe weather events across the United States from 1950 to this year, with information about a storm event's location, azimuth, distance, impact, and severity, including the cost of damages to property and crops. It contains data documenting: The occurrence of storms and other significant weather phenomena having sufficient intensity to cause loss of life, injuries, significant property damage, and/or disruption to commerce. Rare, unusual, weather phenomena that generate media attention, such as snow flurries in South Florida or the S...
Managed by NOAA
The NOAA Unified Forecast System (UFS) / Global Ensemble Forecast System version 13 (GEFSv13) Replay dataset supports the retrospective forecast archive in preparation for GEFSv13 / GFSv17. It includes a range of atmospheric and oceanic variables—such as temperature, humidity, winds, salinity, and currents—covering global conditions at a nominal horizontal resolution of ¼ degree, enabling detailed weather analysis.
The dataset was generated by replaying the coupled UFS model against pre-existing external reanalyses; ERA5 for atmospheric data and ORAS5 for ocean and ice dynamics. Each simulation stream was initialized from these reanalyses, which were pre-processed for the UFS model components, including the GFDL Finite-Volume Cubed-Sphere Dynamical Core (FV3; 25 km, 127 vertical levels) and the Modular Ocean Model (MOM6; ¼ degree tri-polar grid, 72 vertical levels). This replay methodology enforces a predetermined model state while allowing cross-component fluxes and unconstrained processes to be computed.
For the land surface, NOAA’s JEDI-based land data assimilation system incorporated snow depth observations from the NCEI Global Historical Climatology Network (GHCN) and satellite-derived snow cover from the U.S. National Ice Center. The JEDI Sea-ice Ocean and Coupled Analysis system (SOCA) adjusted sea-ice thickness and concentration for consistency with ORAS5.
The original dataset spanned January 1994 to October 2023, with plans for ongoing updates and a 1-degree version covering 1958 to 2023. The dataset is hosted on AWS and GCP clou...
Managed by NOAA
The "Unified Forecast System" (UFS) is a community-based, coupled, comprehensive Earth Modeling System. The Hierarchical Testing Framework (HTF) serves as a comprehensive toolkit designed to enhance the testing capabilities within UFS "repositories". It aims to standardize and simplify the testing process across various "UFS Weather Model" (WM) components and associated modules, aligning with the Hierarchical System Development (HSD) approach and NOAA baseline operational metrics.
The HTF provides a structured methodology for test case design and execution, which enhances code management practices, fosters user accessibility, and promotes adherence to established testing protocols. It enables developers to conduct testing efficiently and consistently, ensuring code integrity and reliability through the use of established technologies such as CMake and CTest. When integrated with containerization techniques, the HTF facilitates portability of test cases and promotes reproducibility across different computing environments. This approach reduces the computational overhead and enhances collaboration within the UFS community by providing a unified testing framework.
Acknowledgment - The Unified Forecast System (UFS) atmosphere-ocean coupled model...
Managed by NOAA
The Unified Forecast System (UFS) is a community-based, coupled, comprehensive Earth modeling system. It supports "multiple applications" covering different forecast durations and spatial domains. The Land Data Assimilation (DA) System is an offline version of the Noah Multi-Physics (Noah-MP) land surface model (LSM) used in the UFS Weather Model (WM). Its data assimilation framework uses "[Joint Effort for Data assimilation Integration - JEDI] (https://www.jcsda.org/jcsda-project-jedi)" software. The offline Noah-MP LSM is a stand-alone, uncoupled model used to execute land surface simulations. In this traditional uncoupled mode, near-surface atmospheric forcing data is required as input. Sample forcing and restart data are provided in this data bucket.
The Noah-MP LSM has evolved through community efforts to pursue and refine a modern-era LSM suitable for use in the National Centers for Environmental Prediction (NCEP) operational weather and climate prediction models. This collaborative effort continues with participation from entities such as NCAR, NCEP, NASA, and university groups.
For details regarding the physical parameterizations used in Noah-MP, see "[Niu, et al. (2011)] (https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2010JD015139)". The "[Land DA User’s Guide] (https://land-da.readthedocs.io/en/latest/)" provides information on building and running the Land DA System in offline mode. Users can access additional technical support via the "[UFS GitHub Discussions] (https://github.com/NOAA-EPIC/land-offline_workflow/discussions)" for the L...
Managed by NOAA
The NOAA UFS Marine Reanalysis is a global sea ice ocean coupled reanalysis product produced by the marine data assimilation team of the UFS Research-to-Operation (R2O) project. Underlying forecast and data assimilation systems are based on the UFS model prototype version-6 and the Next Generation Global Ocean Data Assimilation System (NG-GODAS) release of the Joint Effort for Data assimilation Integration (JEDI) Sea Ice Ocean Coupled Assimilation (SOCA). Covering the 40 year reanalysis time period from 1979 to 2019, the data atmosphere option of the UFS coupled global atmosphere ocean sea ice (DATM-MOM6-CICE6) model was applied with two atmospheric forcing data sets: CFSR from 1979 to 1999 and GEFS from 2000 to 2019. Assimilated observation data sets include extensive space-based marine observations and conventional direct measurements of in situ profile data sets.
This first UFS-marine interim reanalysis product is released to the broader weather and earth system modeling and analysis communities to obtain scientific feedback and applications for the development of the next generation operational numerical weather prediction system at the National Weather Service(NWS). The released file sets include two parts 1.) 1979 - 2019 UFS-DATM-MOM6-CICE6 model free runs and 2) 1979-2019 reanalysis cycle outputs (see descriptions embedded in each file set). Analyzed sea ice and ocean variables are ocean temperature, salinity, sea surface height, and sea ice conce...
Managed by NOAA
The "Unified Forecast System (UFS)" is a community-based, coupled, comprehensive Earth Modeling System. It supports " multiple applications" with different forecast durations and spatial domains. The UFS Short-Range Weather (SRW) Application figures among these applications. It targets predictions of atmospheric behavior on a limited spatial domain and on time scales from minutes to several days. The SRW Application includes a prognostic atmospheric model, pre-processor, post-processor, and community workflow for running the system end-to-end. The "SRW Application Users's Guide" includes information on these components and provides detailed instructions on how to build and run the SRW Application. Users can access additional technical support via the "UFS GitHub Discussions"
This data registry contains the data required to run the “out-of-the-box” SRW Application case. The SRW App requires numerous input files to run, including static datasets (fix files containing climatological information, terrain and land use data), initial condition data files, lateral boundary condition data files, and model configuration files (such as namelists). The SRW App experiment generation system also contains a set of workflow end-to-end (WE2E) tests that exercise various configurations of the system (e.g., different grids, physics suites). Data for running a subset of these WE2E tests are also included within this registry.
Users can generate forecasts for dates not included in this data registry by downloading and manually adding raw model files for the desired dates. Many of these model files are publicly available and can be accessed via links on the "Developmental Testbed Center" webs...
Managed by NOAA
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 wav...
Managed by NOAA
The Unified Forecast System (UFS) is a community-based, coupled, comprehensive Earth Modeling System. The ufs-weather-model (UFS-WM) is the model source of the UFS for NOAA’s operational numerical weather prediction applications. The UFS-WM Regression Test (RT) is the testing software to ensure that previously developed and tested capabilities in UFS-WM still work after code changes are integrated into the system. It is required that UFS-WM RTs are performed successfully on the required Tier-1 platforms whenever code changes are made to the UFS-WM. The results of the UFS-WM RTs are summarized in log files and these files will be committed to the UFS-WM repository along with the code changes. Currently, the UFS-WM RTs have been developed to support several applications targeted for operational implementations including the global weather forecast, subseasonal to seasonal forecasts, hurricane forecast, regional rapid refresh forecast, and ocean analysis.
At this time, there are 123 regression tests to support the UFS applications. The tests are evolving along with the development merged to the UFS-WM code repository. The regression test framework has been developed in the UFS-WM to run these tests on tier-1 supported systems. Each of the regression tests require a set of input data files and configuration files. The configuration files include namelist and model configuration files residing within the UFS-WM code repository. The input data includes initial conditions, climatology data, and fixed data sets such as orographic data and grid sp...
Managed by NOAA
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). ...
Managed by SMN
The Servicio Meteorológico Nacional de Argentina (SMN-Arg), the National Meteorological Service of Argentina, shares its deterministic forecasts generated with WRF 4.0 (Weather and Research Forecasting) initialized at 00 and 12 UTC every day.
This forecast includes some key hourly surface variables –2 m temperature, 2 m relative humidity, 10 m wind magnitude and direction, and precipitation–, along with other daily variables, minimum and maximum temperature.
The forecast covers Argentina, Chile, Uruguay, Paraguay and parts of Bolivia and Brazil in a Lambert conformal projection, with 4 km...
Managed by SondeHub
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, a...
Managed by Mark S. Veillette
Collection of spatially and temporally aligned GOES-16 ABI satellite imagery, NEXRAD radar mosaics, and GOES-16 GLM lightning detections.
Managed by Euro-Argo
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.
Managed by CSIRO
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 ...
Managed by National Center for Atmospheric Research
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 atmospher...
Managed by International Center for Tropical Agriculture
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.
Managed by UCLA Center for Climate Science
High-resolution historical and future climate simulations from 1980-2100
Managed by Radiant Earth Foundation
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 a...
Managed by Cal-Adapt Analytics Engine https://analytics.cal-adapt.org/
Downscaled future and historical climate projections for California and her environs in support of California's Fifth Climate Assessment
Managed by National Center for Atmospheric Research
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 o...
Managed by National Center for Atmospheric Research
Data from ARISE-SAI Experiments with CESM2
Managed by National Center for Atmospheric Research
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 ...
Managed by ESGF and Pangeo
The sixth phase of global coupled ocean-atmosphere general circulation model ensemble.
Managed by NOAA
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.
...
Managed by Space Weather Technology, Research and Education Center (TREC) at University of Colorado, Boulder
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.
Managed by Scenarios Network for Alaska + Arctic Planning at the International Arctic Research Center, University of Alaska, Fairbanks
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)....
Managed by Helmholtz Centre Hereon / GERICS
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.
Managed by Global Carbon Budget Office at the University of Exeter, UK
The Global Carbon Budget (GCB) is recognised globally as the most comprehensive report on global carbon emissions and sinks. This dataset, updated every year, includes estimates of land and ocean carbon fluxes from the suite of models used in the report.
Managed by Meta
Global and regional Canopy Height Maps (CHM). Created using machine learning models on high-resolution worldwide Maxar satellite imagery.
Managed by Scenarios Network for Alaska + Arctic Planning at the International Arctic Research Center, University of Alaska, Fairbanks
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 ru...
Managed by Met Office
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
Managed by National Center for Atmospheric Research
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 a...
Managed by NASA
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).
Managed by NASA
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...
Managed by NOAA
NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).
Climate Data Records are created by merging data from surface, atmosphere, and space-based systems across decades. NOAA’s Climate Data Records provides authoritative and traceable long-term climate records. NOAA developed CDRs by applying modern data analysis methods to historical global satellite data. This process can clarify the underlying climate trends within the data and allows researchers and other users to identify economic and scientific value in these records. NCEI maintains and extends CDRs by applying the same methods to present-day and future satellite measurements.
Atmospheric Climate Data Records are measurements of several global variables to help characterize the atmosphere...
Managed by NOAA
NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).
Climate Data Records are created by merging data from surface, atmosphere, and space-based systems across decades. NOAA’s Climate Data Records provides authoritative and traceable long-term climate records. NOAA developed CDRs by applying modern data analysis methods to historical global satellite data. This process can clarify the underlying climate trends within the data and allows researchers and other users to identify economic and scientific value in these records. NCEI maintains and extends CDRs by applying the same methods to present-day and future satellite measurements.
Fundamental CDRs are composed of sensor data (e.g. calibrated radiances, brightness temperatures) that have been ...
Managed by NOAA
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 ...
Managed by NOAA
NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).
Climate Data Records are created by merging data from surface, atmosphere, and space-based systems across decades. NOAA’s Climate Data Records provides authoritative and traceable long-term climate records. NOAA developed CDRs by applying modern data analysis methods to historical global satellite data. This process can clarify the underlying climate trends within the data and allows researchers and other users to identify economic and scientific value in these records. NCEI maintains and extends CDRs by applying the same methods to present-day and future satellite measurements.
Oceanic Climate Data Records are measurements of oceans and seas both surface and subsurface as well as frozen st...
Managed by NOAA
NOAA's Climate Data Records (CDRs) are robust, sustainable, and scientifically sound climate records that provide trustworthy information on how, where, and to what extent the land, oceans, atmosphere and ice sheets are changing. These datasets are thoroughly vetted time series measurements with the longevity, consistency, and continuity to assess and measure climate variability and change. NOAA CDRs are vetted using standards established by the National Research Council (NRC).
Climate Data Records are created by merging data from surface, atmosphere, and space-based systems across decades. NOAA’s Climate Data Records provides authoritative and traceable long-term climate records. NOAA developed CDRs by applying modern data analysis methods to historical global satellite data. This process can clarify the underlying climate trends within the data and allows researchers and other users to identify economic and scientific value in these records. NCEI maintains and extends CDRs by applying the same methods to present-day and future satellite measurements.
Terrestrial CDRs are composed of sensor data that have been improved and quality controlled over time, together w...
Managed by NOAA
The NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) consists of four climate variables derived from the GHCN-D dataset: maximum temperature, minimum temperature, average temperature and precipitation. Each file provides monthly values in a 5x5 lat/lon grid for the Continental United States. Data is available from 1895 to the present. On an annual basis, approximately one year of "final" nClimGrid will be submitted to replace the initially supplied "preliminary" data for the same time period. Users should be sure to ascertain which level of data is required for their research.
EpiNOAA is an analysis ready dataset that consists of a daily time-series of nClimGrid measures (maximum temperature, minimum temperature, average temperature, and precipitation) at the county scale. Each file provides daily values for the Continental United States. Data are available from 1951 to the present. Daily data are updated every 3 days with a preliminary data file and replaced with the scaled (i.e., quality controlled) data file every three months. This derivative data product is an enhancement from the original daily nClimGrid dataset in that all four weather parameters are now p...
Managed by NOAA
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. norma...
Managed by Queensland Government
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. SIL...
Managed by NOAA’s National Geodetic Survey
One of the National Geospatial-Intelligence Agency’s (NGA) and the National Oceanic and Atmospheric Administration’s (NOAA) missions is to ensure the safety of navigation on the seas by
maintaining the most current information and the highest quality services for U.S. and global transport networks. To achieve this mission, we need accurate coastal bathymetry over diverse
environmental conditions. The SCuBA program focused on providing critical information to improve existing bathymetry resources and techniques with two specific objectives. The first objective
was to validate National Aeronautics and Space Administration’s (NASA) Ice, Cloud and land Elevation SATellite-2 (ICESat-2), an Earth observing, space-based light detection and ranging (LiDAR)
capability, as a useful bathymetry tool for nearshore bathymetry information in differing environmental conditions. Upon validating the ICESat-2 bathymetry retrievals relative to sea floor
type, water clarity, and water surface dynamics, the next objective is to use ICESat-2 as a calibration tool to improve existing Satellite Derived Bathymetry (SDB) coastal bathymetry products
with poor coastal depth information but superior spatial coverage. Current resources that monitor coastal bathymetry can have large vertical depth errors (up to 50 percent) in the nearshore
region; however, derived results from ICESat-2 shows promising results for improving the accuracy of the bathymetry information in the nearshore region.
Project Overview
One of NGA’s and NOAA’s primary missions is to provide safety of navigation information. However, coastal depth information is still lacking in some regions—specifically, remote regions. In fact, it has been reported that 80 percent of the entire seafloor has not been mapped. Traditionally, airborne LiDARs and survey boats are used to map the seafloor, but in remote areas, we have to rely on satellite capabilities, which currently lack the vertical accuracy desired to support safety of navigation in shallow water. In 2018, NASA launched a space-based LiDAR system called ICESat-2 that has global coverage and a polar orbit originally designed to monitor the ice elevation in polar regions. Remarkably, because it has a green laser beam, ICESat-2 also happens to collect bathymetry information ICESat-2. With algorithm development provided by University of Texas (UT) Austin, NGA Research and Development (R&D) leveraged the ICESat-2 platform to generate SCuBA, an automated depth retrieval algorithm for accurate, global, refraction-corrected underwater depths from 0 m to 30 m, detailed in Figure 1 of the documentation. The key benefit of this product is the vertical depth accuracy of depth retrievals, which is ideal for a calibration tool. NGA and NOAA National Geodetic Survey (NGS), partnered to make this product available to the public for all US territories.
...
Managed by NOAA
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" 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". After chunking the files, they were converted to a monthly Zarr file. Then, each monthly Zarr file was combined using "to_zarr" 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)
Managed by NOAA
Global Hydro-Estimator provides a global mosaic imagery of rainfall estimates from multi-geostationary satellites, which currently includes GOES-16, GOES-15, Meteosat-8, Meteosat-11 and Himawari-8. The GHE products include: Instantaneous rain rate, 1 hour, 3 hour, 6 hour, 24 hour and also multi-day rainfall accumulation.
Managed by NOAA
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.
...
Managed by NOAA
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 NOA...
NOAA's Coastal Ocean Reanalysis (CORA) for the Gulf of Mexico and East Coast (GEC) is produced using verified hourly water levels from the Center of Operational Oceanographic Products & Services (CO-OPS), through hydrodynamic modeling from Advanced Circulation "ADCIRC" and Simulating WAves Nearshore "SWAN" models. Data are assimilated, processed, corrected, and processed again before quality assurance and skill assessment with additional verified tide station-based observations.
Details for CORA Dataset
Timeseries - 1979 to 2022
Size - Approx. 20.5TB
Domain - Lat 5.8 to 45.8 ; Long -98.0 to -53.8
Nodes - 1813443 centroids, 3564104 elements
Grid cells - Currently apporximately 505
Spatial Resolution ...
Managed by University of Connecticut; Guangxi University
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.
Managed by QED
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 ...
Managed by Intelinair, Inc.
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 201...
Managed by Esri
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
Managed by Innovative Solutions for Decision Agriculture (iSDA)
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.
Managed by Descartes Labs
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 daytim...
Managed by AMS Kepler
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.
Managed by Indigo Ag, Inc.
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 becam...
Managed by Polar Geospatial Center
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...
Managed by AMS Kepler
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 ...
Managed by Capella Space
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 ...
Managed by Digital Earth Africa
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 H...
Managed by Digital Earth Africa
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 clou...
Managed by Digital Earth Africa
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 t...
Managed by Digital Earth Africa
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 Cope...
Managed by Digital Earth Africa
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...
Managed by Digital Earth Africa
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 m...
Managed by Digital Earth Africa
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 ...
Managed by Digital Earth Africa
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...
Managed by Digital Earth Africa
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 t...
Managed by Digital Earth Africa
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:
Managed by Digital Earth Africa
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 rough...
Managed by Digital Earth Africa
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 fr...
Managed by Digital Earth Africa
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 dat...
Managed by VITO
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 we...
Managed by VITO
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...
Managed by Polar Geospatial Center
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 ...
Managed by Earth Big Data LLC
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. ...
Managed by Radiant Earth Foundation
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.
Managed by NOAA
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-L...
Managed by NOAA
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...
Managed by Astraea
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...
Managed by NOAA
**DATA FEED ISSUES - Due to major damage to critical infrastructure in the Asheville, NC area from Hurricane Helene, our GOES data feeds to the cloud have been impacted. We are working with local authorities and service providers in hopes that we can restore these feeds ASAP. Note that major celluar and network (fiber) infrastructure have been damaged and this may take time to adress. Thank you for your continued support.**
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.
NEW GOES-18 Data!!! GOES-18 is now provisional and data has began streaming. Data files will be available between Provisional and the Operational Declaration of the satellite, however, data will have the caveat GOES-18 Preliminary, Non-Operational Data. The exception is during the interleave period when ABI Radiances and Cloud and Moisture Imagery data will be shared operationally via the NOAA Open Data Dissemination Program.
GOES satellites (GOES-16, GOES-17, & GOES-18) provide continuous weather imagery and
monitoring of meteorological and space environment data across North America.
...
Managed by NOAA
NOAA/NESDIS Global Mosaic of Geostationary Satellite Imagery (GMGSI) visible (VIS), shortwave infrared (SIR), longwave infrared (LIR) imagery, and water vapor imagery (WV) are composited from data from several geostationary satellites orbiting the globe, including the GOES-East and GOES-West Satellites operated by U.S. NOAA/NESDIS, the Meteosat-10 and Meteosat-9 satellites from theMeteosat Second Generation (MSG) series of satellites operated by European Organization for the Exploitation of Meteorological Satellites (EUMETSAT), and the Himawari-9 satellite operated by the Japan Meteorological ...
Managed by NOAA
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 assessin...
Managed by The New Jersey Office of GIS, NJ Office of Information Technology
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.
Managed by Remote Sensing Big Data Intelligent Application Laboratory, School of Aeronautics and Astronautics, Sun Yat-sen University
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.
Managed by JAXA
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 datase...
Managed by JAXA
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...
Managed by Natural Resources Canada
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.
Managed by Polar Geospatial Center
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...
Managed by Sinergise
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.
Managed by LiveEO
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 ...
Managed by Earth Observatory of Singapore, Nanyang Technological University
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 effec...
Managed by Sinergise
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 on-going studies. L1C data are available from June 2015 globally. L2A data are available from November 2016 over Europe region and globally since January 2017.
Managed by Element 84
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 t...
Managed by Sinergise
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.
Managed by Meteorological Environmental Earth Observation
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 201...
Managed by Meteorological Environmental Earth Observation
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 ro...
Managed by 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).
Managed by University of Illinois
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...
Managed by United States Geological Survey
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.
Managed by World Bank Group
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...
Managed by Sinergise
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 Ge...
Managed by Washington DC government
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.
Managed by NOAA
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...
Managed by NOAA
The NOAA Continuously Operating Reference Stations (CORS) Network (NCN), managed by NOAA/National Geodetic Survey (NGS), provide Global Navigation Satellite System (GNSS) data, supporting three dimensional positioning, meteorology, space weather, and geophysical applications throughout the United States. The NCN is a multi-purpose, multi-agency cooperative endeavor, combining the efforts of hundreds of government, academic, and private organizations. The stations are independently owned and operated. Each agency shares their GNSS/GPS carrier phase and code range measurements and station metadata with NGS, which are analyzed and distributed free of charge. ...
Managed by The New Jersey Office of GIS, NJ Office of Information Technology
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 Com...
Managed by GeoSampa - o mapa digital da cidade de São Paulo
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.
Managed by Joint Nature Conservation Committee
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
Managed by Mapzen, a Linux Foundation project
A global dataset providing bare-earth terrain heights, tiled for easy usage and provided on S3.
Managed by Hobu, Inc.
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 co...
Managed by CMAS CENTER
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 Wareho...
Managed by Environmental Protection Agency
Detailed air model results from EPA’s Risk-Screening Environmental Indicators (RSEI) model.
Managed by GEOS-Chem Support Team
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.
Managed by GEOS-Chem Support Team
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.
Managed by 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.
Managed by NASA
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.
Managed by Finnish Meteorological Institute
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.
Managed by The Atmospheric Composition Analysis Group
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.
Managed by National Renewable Energy Laboratory
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 wi...
Managed by National Renewable Energy Laboratory
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).
Managed by National Renewable Energy Laboratory
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.
Managed by National Renewable Energy Laboratory
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.
Managed by National Renewable Energy Laboratory
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.
Managed by National Renewable Energy Laboratory
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.
Managed by National Renewable Energy Laboratory
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 ...
Managed by National Renewable Energy Laboratory
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...
Managed by NASA
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 serves NASA and Society by expanding and accelerating the realization of societal and economic benefits from Earth science, information, and technology research and development.
The NASA Prediction Of Worldwide Energy Resources (POWER) Project, a NASA Applied Sciences program, improves the accessibility and usage NASA Earth Observations (EO) supporting community research in three focus areas: 1) renewable energy development, 2) building energy efficiency, and 3) agroclimatology applications. POWER can help communities be resilient amid observed climate variability through the easy access of solar and meteorological data via a variety of access methods.
The latest POWER version includes hourly-based source Analysis Ready Data (ARD), in addition to enhanced daily, monthly, annual, and climatology ARD. The daily time-series spans 40 years for meteorology available from 1981 and 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 20 years from 2001. The hourly data will provide users the ARD needed to model the energy performance of building systems, providing information directly amenable to decision support tools introducing the industry standard EPW (EnergyPlus Weather file).
POWER also provides parameters at daily, monthly, annual, and user-defined time periods, spanning from 1984 through to within a week of real time. Additionally, POWER provides are user-defined analytic capabilities, including custom climatologies and climatological-based reports for parameter anomalies, ASHRAE® compatible climate design condition statistics, and building climate zones.
The ARD and climate analytics will be readily accessible through POWER's integrated services suite, including the Data Access Viewer (DAV). The DAV has recently been improved to incorporate updated parameter groupings, new analytical capabilities, and the new data formats. POWER also provides a complete API (Application Programming Interface) that allows uses...
Managed by National Renewable Energy Laboratory
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.
Managed by National Renewable Energy Laboratory
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.
Managed by National Renewable Energy Laboratory
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.
Managed by National Renewable Energy Laboratory
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.
Managed by National Renewable Energy Laboratory
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...
Managed by National Renewable Energy Laboratory
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 noteboo...
Managed by Meta
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 ...
Managed by GeoNet
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...
Managed by 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.
Managed by Maxar
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.
Managed by NOAA
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/App...
Managed by Northern California Earthquake Data Center
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.
Managed by Grillo
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.
Managed by Pacific Atlas
OSM is a free, editable map of the world, created and maintained by volunteers. Regular OSM data archives are made available in Amazon S3.
Managed by Southern California Earthquake Data Center
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.
Managed by Data.world
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.
Managed by Euro-Argo
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.
Managed by NOAA
Community provided bathymetry data collected in collaboration with the International Hydrographic Organization.
Managed by Farallon Institute
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 fro...
Managed by NOAA
NOTICE - The Coast Survey Development Laboratory (CSDL) in NOAA/National Ocean Service (NOS)/Office of Coast Survey is upgrading the Surge and Tide Operational Forecast System (STOFS, formerly ESTOFS) to Version 2.1. A Service Change Notice (SCN) has been issued and can be found "HERE"
NOAA's Surge and Tide Operational Forecast System: Three-Dimensional Component for the Atlantic Basin (STOFS-3D-Atlantic). STOFS-3D-Atlantic runs daily (at 12 UTC) to provide users with 24-hour nowcasts (analyses of near present conditions) and up to 96-hour forecast guidance of water level conditions, and 2- and 3-dimensional fields of water temperature, salinity, and currents. The water level outputs represent the combined tidal and subtidal water surface elevations and are referenced to xGEOID20B
STOFS-3D-Atlantic has been developed to serve the marine navigation, weather forecasting, and disaster mitigation user communities. It is developed in a collaborative effort between the NOAA/National Ocean Service (NOS)/Office of Coast Survey, the NOAA/National Weather Service (NWS)/National Centers for Environmental Prediction (NCEP) Central Operations (NCO), and the Virginia Institute of Marine Science.
STOFS-3D-Atlantic employs the Semi-implicit Cross-scale Hydroscience Integrated System Model (SCHISM) as the hydrodynamic model core. Its unstructured grid consists of 2,926,236 nodes and 5,654,157 triangular or quadrilateral elements. Grid resolution is 1.5-2 km near the shoreline, ~600 m for the floodplain, down to 8 m for watershed rivers (at least 3 nodes across each river cross-section), and around 2-10 m for levees. Along the U.S. coastline, the land boundary of the domain aligns with the 10-m contour above xGEOID20B, encompassing the coastal transitional zone most vulnerable to coastal and inland flooding.
STOFS-3D-Atlantic makes uses of outputs from the National Water Model (NWM) to include inland hydrology and extreme precipitation effects on coastal flooding; forecast guidance from the NCEP Global Forecast System (GFS) and High-Resolution Rapid Refresh (HRRR) model as the surface meteorological forci...
Managed by NOAA's National Ocean Service, the Integrated Ocean Observing System (IOOS)
This repository contains references to datasets published to the NOAA Open Data Dissemination Program. These reference datasets serve as index files to the original data by mapping to the Zarr V2 specification. When multidimensional model output is read through zarr, data can be lazily loaded (i.e. retrieving only the data chunks needed for processing) and data reads can be scaled horizontally to optimize object storage read performance.
The process used to optimize the data is called kerchunk. RPS runs the workflow in their AWS cloud environment every time a new data notification is received from a relevant source data bucket.
These are the current datasets being cloud-optimized. Refer to those pages for file naming conventions and other information regarding the specific model implementations:
NOAA Operational Forecast System (OFS)
NOAA Global Real-Time Ocean Forecast System (Global RTOFS)
NOAA National Water Model Short-Range Forecast
Filenames follow the source dataset’s conventions. For example, if the source file is
nos.dbofs.fields.f024.20240527.t00z.nc
Then the cloud-optimized filename is the same, with “.zarr” appended
nos.dbofs.fields.f024.20240527.t00z.nc.zarr
Data Aggregations
We also produce virtual aggregations to group an entire forecast model run, and the “best” available forecast.
Best Forecast (continuously updated) - nos.dbofs.fields.best.nc.zarr
Full Model Run - nos.dbofs.fields.forecast.[YYYYMMDD].t[CC]z.nc.zarr
Managed by NOAA
NOAA is soliciting public comment on petential changes to the Real Time Ocean Forecast System (RTOFS) through March 27, 2024. Please see Public Notice at (https://www.weather.gov/media/notification/pdf_2023_24/pns24-12_rtofs_v2.4.0.pdf)
NOAA's Global Real-Time Ocean Forecast System (Global RTOFS) provides users with nowcasts (analyses of near present conditions) and forecast guidance up to eight days of ocean temperature and salinity, water velocity, sea surface elevation, sea ice coverage and sea ice thickness.
The Global Operational Real-Time Ocean Forecast System (Global RTOFS) is based on an eddy resolving 1/12° global HYCOM (HYbrid Coordinates Ocean Model) (https://www.hycom.org/), which is coupled to the Community Ice CodE (CICE) Version 4 (https://www.arcus.org/witness-the-arctic/2018/5/highlight/1). The RTOFS grid has a 1/12 degree horizontal resolution and 41 hybrid vertical levels on a global tripolar grid.
Since 2020, the RTOFS system implements a multivariate, multi-scale 3DVar data assimilation algorithm (Cummings and Smedstad, 2014) using a 24-hour update cycle. The data types presently assimilated include
(1) satellite Sea Surface Temperature (SST) from METOP-B, JPSS-VIIRS, and in-Situ SST, from ships, fixed and drifting buoys
(2) Sea Surface Salinity (SSS) from SMAP, SMOS, and buoys
(3) profiles of Temperature and Salinity from Animal-borne, Alamo floats, Argo floats, CTD, fixed buoys, gliders, TESAC, and XBT
(4) Absolute Dynamic Topography (ADT) from Altika, Cryosat, Jason-3, Sentinel 3a, 3b, 6a
(5) sea ice concentration from SSMI/S, AMSR2
The system is designed to incorporate new observing systems as the data becomes available.
Once the observations go through a fully automated quality control and thinning process, the increments, or corrections, are obtained by executing the 3D variational algorithm. The increments are then added to the 24-hours forecast fields using a 6-hourly incremental analysis update. An earlier version of the system is described in Garraffo et al (2020).
Garraffo, Z.D., J.A. Cummings, S. Paturi, Y. Hao, D. Iredell, T. Spindler, B. Balasubramanian, I. Rivin, H-C. Kim, A. Mehra, 2020. Real Time Ocean-Sea Ice Coupled Three Dimensional Variational Global Data Assimilative Ocean Forecast System. In Research Activities in Earth System Modeling, edited by E. Astakhova, WMO, World Climate Research Program Report No.6, July 2020.
Cummings, J. A. and O. M. Smedstad. 2013. Variational Data Assimilation for the Global Ocean.
Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol II)
S. Park and L. Xu (eds), Springer, Chapter 13, 303-343.
Global...
Managed by NOAA
NOTICE - The Coast Survey Development Laboratory (CSDL) in NOAA/National Ocean Service (NOS)/Office of Coast Survey has upgraded the Surge and Tide Operational Forecast System (STOFS, formerly ESTOFS) to Version 2.1. A Service Change Notice (SCN) has been issued and can be found "HERE"
NOAA's Global Surge and Tide Operational Forecast System 2-D (STOFS-2D-Global) provides users with nowcasts (analyses of near present conditions) and forecast guidance of water level conditions for the entire globe. STOFS-2D-Global has been developed to serve the marine navigation, weather forecasting, and disaster mitigation user communities. STOFS-2D-Global was developed in a collaborative effort between the NOAA/National Ocean Service (NOS)/Office of Coast Survey, the NOAA/National Weather Service (NWS)/National Centers for Environmental Prediction (NCEP) Central Operations (NCO), the University of Notre Dame, the University of North Carolina, and The Water Institute of the Gulf. The model generates forecasts out to 180 hours four times per day; forecast output includes water levels caused by the combined effects of storm surge and tides, by astronomical tides alone, and by sub-tidal water levels (isolated storm surge).
The hydrodynamic model employed by STOFS-2D-Global is the ADvanced CIRCulation (ADCIRC) finite element model. The model is forced by GFS winds, mean sea level pressure, and sea ice. The unstructured grid used by STOFS-2D-Global consists of 12,785,004 nodes and 24,875,336 triangular elements. Coastal res...
Managed by NOAA
The National Bathymetric Source (NBS) project creates and maintains high-resolution bathymetry composed of the best available data. This project enables the creation of next-generation nautical charts while also providing support for modeling, industry, science, regulation, and public curiosity. Primary sources of bathymetry include NOAA and U.S. Army Corps of Engineers hydrographic surveys and topographic bathymetric (topo-bathy) lidar (light detection and ranging) data. Data submitted through the NOAA Office of Coast Survey’s external source data process are also included, with gaps...
Managed by NOAA
Founded in 1807, NOAA’s Office of Coast Survey is the nation’s first scientific agency and today is responsible for supporting nearly $5.4 trillion in economic activity through providing advanced marine navigation services. The Office of Coast Survey collects and qualifies hydrographic, bathymetric, and topographic data, from NOAA platforms and many other data providers. These data and associated deliverables are posted here for various users to access, including but not limited to the "National Bathymetric Source Program" for incorporation into compilations of the best available bat...
Managed by NOAA
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...
Managed by NOAA
S-102 is a data and metadata encoding specification that is part of the S-100 Universal Hydrographic Data Model, an international standard for hydrographic data exchange. This collection of data contains bathymetric surfaces from NOAA/NOS/OCS National Bathymetric Source, for various U.S. coastal and offshore waters and the great lakes. These datasets are encoded as HDF5 files conforming to the S-102 specification.
Managed by NOAA
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 f...
Managed by NOAA
Water-column sonar data archived at the NOAA National Centers for Environmental Information.
Managed by NOAA
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
Managed by NOAA
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 20...
Managed by University of Reading, Department of Meteorology
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.
Managed by National Archives and Records Administration (NARA)
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 inclu...
Managed by National Archives and Records Administration (NARA)
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 inclu...
Managed by UCR 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:
Managed by GLEIF
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 ma...
Managed by Hobu, Inc.
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 pa...
Managed by BuildZero
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.
Managed by Swiss Geoportal
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).
Managed by Virginia Department of Conservation and Recreation
The Virginia Coastal Resilience Master Plan builds on the 2020 Virginia Coastal Resilience Master Planning Framework, which outlined the goals and principles of the Commonwealth’s statewide coastal resilience strategy. Recognizing the urgent challenge flooding already poses, the Commonwealth developed Phase One of the Master Plan on an accelerated timeline and focused this first assessment on the impacts of tidal and storm surge coastal flooding on coastal Virginia. The Master Plan leveraged the combined efforts of more than two thousand stakeholders, subject matter experts, and government personnel. We centered the development of this plan around three core components:
A Technical Study compiled essential data, research, processes, products, and resilience efforts in the Coastal Resilience Database, which forms much of basis of this plan and the Coastal Resilience Web Explorer;
A Technical Advisory Committee supported coordination across key stakeholders and ensured the incorporation of the best available subject matter knowledge, data, and methods into this plan; and
Stakeholder Engagement captured diverse resilience perspectives from residents, local and regional officials, and other stakeholders across Virginia’s coastal communities to drive regionally specific resilience priorities.Data products used and generated for the Virginia Coastal Resilience.
This dataset represents the data that was developed for the technical study. Appendix F - Data Product List provides a list of available data. Other Appendix documents provide the inpu...
Managed by Impact Observatory
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 ...
Managed by North American Cartographic Information Society (nacis.org)
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.
Managed by Center for Conservation Bioacoustics, Cornell University (https://elephantlisteningproject.org)
Archival soundscapes recorded in the rainforest landscapes of Central Africa, with a focus on the vocalizations of African forest elephants (Loxodonta cyclotis).
Managed by iNaturalist is an independent, tax-exempt, 501(c)(3), not-for-profit organization based in the United States of America (EIN/Tax ID: 92-1296468).
iNaturalist is a community science effort in which participants share observations of living organisms that they encounter and document with photographic evidence, location, and date. The community works together reviewing these images to identify these observations to species. This collection represents the licensed images accompanying iNaturalist observations.
Managed by The Global Biodiversity Information Facility (GBIF)
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 a...
Managed by Royal Botanic Gardens and Domain Trust
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, syste...
Managed by Orcasound
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.
Managed by Monterey Bay Aquarium Research Institute
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.
Managed by Sea Around Us
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.
Managed by Conabio
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.