This registry exists to help people discover and share datasets that are available via AWS resources. See recent additions and learn more about sharing data on AWS.
See all usage examples for datasets listed in this registry tagged with meteorological.
You are currently viewing a subset of data tagged with meteorological.
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.
agricultureair qualityanalyticsarchivesatmosphereclimateclimate modeldata assimilationdeep learningearth observationenergyenvironmentalforecastgeosciencegeospatialglobalhistoryimagingindustrymachine learningmachine translationmetadatameteorologicalmodelnetcdfopendapradiationsatellite imagerysolarstatisticssustainabilitytime series forecastingwaterweatherzarr
NASA's goal in Earth science is to observe, understand, and model the Earth system to discover how it is changing, to better predict change, and to understand the consequences for life on Earth. The Applied Sciences Program, within the Earth Science Division of the NASA Science Mission Directorate, serves individuals and organizations around the globe by expanding and accelerating societal and economic benefits derived from Earth science, information, and technology research and development.
The Prediction Of Worldwide Energy Resources (POWER) Project, funded through the Applied Sciences Program at NASA Langley Research Center, gathers NASA Earth observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in energy development, building energy efficiency, and supporting agriculture projects.
The POWER project contains over 380 satellite-derived meteorology and solar energy Analysis Ready Data (ARD) at four temporal levels: hourly, daily, monthly, and climatology. The POWER data archive provides data at the native resolution of the source products. The data is updated nightly to maintain near real time availability (2-3 days for meteorological parameters and 5-7 days for solar). The POWER services catalog consists of a series of RESTful Application Programming Interfaces, geospatial enabled image services, and web mapping Data Access Viewer. These three service offerings support data discovery, access, and distribution to the project’s user base as ARD and as direct application inputs to decision support tools.
The latest data version update includes hourly...
agriculturedisaster responseearth observationgeospatialmeteorologicalsatellite imageryweather
NEW GOES-19 Data!! On April 4, 2025 at 1500 UTC, the GOES-19 satellite will be declared the Operational GOES-East satellite. All products and services, including NODD, for GOES-East will transition to GOES-19 data at that time. GOES-19 will operate out of the GOES-East location of 75.2°W starting on April 1, 2025 and through the operational transition. Until the transition time and during the final stretch of Post Launch Product Testing (PLPT), GOES-19 products are considered non-operational regardless of their validation maturity level. Shortly following the transition of GOES-19 to GOES-East, all data distribution from GOES-16 will be turned off. GOES-16 will drift to the storage location at 104.7°W. GOES-19 data should begin flowing again on April 4th once this maneuver is complete.
NEW GOES 16 Reprocess Data!! The reprocessed GOES-16 ABI L1b data mitigates systematic data issues (including data gaps and image artifacts) seen in the Operational products, and improves the stability of both the radiometric and geometric calibration over the course of the entire mission life. These data were produced by recomputing the L1b radiance products from input raw L0 data using improved calibration algorithms and look-up tables, derived from data analysis of the NIST-traceable, on-board sources. In addition, the reprocessed data products contain enhancements to the L1b file format, including limb pixels and pixel timestamps, while maintaining compatibility with the operational products. The datasets currently available span the operational life of GOES-16 ABI, from early 2018 through the end of 2024. The Reprocessed L1b dataset shows improvement over the Operational L1b products but may still contain data gaps or discrepancies. Please provide feedback to Dan Lindsey (dan.lindsey@noaa.gov) and Gary Lin (guoqing.lin-1@nasa.gov). More information can be found in the [GOES-R ABI Reprocess User Guide](https://github.com/NOAA-Big-Data-Program/nodd-data-docs/blob/main/GOES/GOES-R_ABI_Reprocessed_L1b_User_Guide-v1.1.pdf).
NOTICE: As of January 10th 2023, GOES-18 assumed the GOES-West position and all data files are deemed both operational and provisional, so no ‘preliminary, non-operational’ caveat is needed. GOES-17 is now offline, shifted approximately 105 degree West, where it will be in on-orbit storage. GOES-17 data will no longer flow into the GOES-17 bucket. Operational GOES-West products can be found in the GOES-18 bucket.
GOES satellites (GOES-16, GOES-17, GOES-18 & GOES-19) provide continuous weather imagery and
monitoring of meteorological and space environment data across North America.
GO...
agricultureearth observationmeteorologicalnatural resourceweather
Real-time and archival data from the Next Generation Weather Radar (NEXRAD) network.
agricultureclimatemeteorologicalweather
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...
agricultureclimatecogdeafricaearth observationfood securitygeospatialmeteorologicalsatellite imagerystacsustainability
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...
climatecoastaldisaster responseenvironmentalmeteorologicaloceanswaterweather
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...
environmentalgeospatialmeteorological
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.
earth observationenergygeospatialmeteorologicalwater
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).
air qualityatmospherechemistryclimateenvironmentalmeteorologicalmodelweather
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.
air qualityatmospherechemistryclimateenvironmentalmeteorologicalmodelweather
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.
agricultureclimatemeteorologicalweather
The Rapid Refresh Forecast System (RRFS) is the National Oceanic and Atmospheric Administration’s (NOAA) next generation convection-allowing, rapidly-updated ensemble prediction system, currently scheduled for operational implementation in 2026. The operational configuration will feature a 3 km grid covering North America and include deterministic forecasts every hour out to 18 hours, with deterministic and ensemble forecasts to 60 hours four times per day at 00, 06, 12, and 18 UTC.The RRFS will provide guidance to support forecast interests including, but not limited to, aviation, severe convective weather, renewable energy, heavy precipitation, and winter weather on timescales where rapidly-updated guidance is particularly useful.
The RRFS is underpinned by the Unified Forecast System (UFS), a community-based Earth modeling initiative, and benefits from collaborative development efforts across NOAA, academia, and research institutions.
This bucket provides access to real time, experimental RRFS prototype output. And will provide access to final retrospective output once completed.
rrfs_a/rrfs_a.20241201/12/control contains the deterministic forecast initialized at 12 UTC on 01 December 2024. Users will find two types of output in GRIB2 format. The first is:rrfs.t12z.natlev.f018.grib2 rrfs.t12z.prslev.f018.conus.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 ...
atmosphereclimateclimate modeldata assimilationforecastgeosciencegeospatiallandmeteorologicalweatherzarr
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...
air qualityclimateenvironmentalgeospatialmeteorological
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...
agriculturedisaster responseearth observationgeospatialmeteorologicalsatellite imageryweather
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...
agricultureclimatedisaster responseenvironmentalmeteorologicalweather
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...
agricultureclimateearth observationmeteorologicalweather
Open-Meteo integrates weather models from reputable national weather services, offering a swift and efficient weather API. Real-time weather forecasts are unified into a time-series database that provides historical and future weather data for any location worldwide.Through Open-Meteo on AWS Open Data, you can download the Open-Meteo weather database and analysis weather data locally. Docker images are provided to download data and to expose an HTTP API endpoint. Using Open-Meteo SDKs, you can seamlessly integrate weather data into your Python, Typescript, Swift, Kotlin, or Java applications.T...
climateenvironmentalmeteorologicaloceansoceanssustainabilityweather
This dataset includes archival hourly data from the [Sofar Spotter buoy global network] (https://weather.sofarocean.com/) from 2019 to March 2022.
data assimilationelectricityenergyenergy modelingindustrialmeteorologicalsolartransportation
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.
air temperatureatmospheremeteorologicalnear-surface air temperaturenear-surface relative humiditynear-surface specific humidityprecipitationweather
These products are a subset of the ECMWF real-time forecast data and are made available to the public free of charge. They are based on the medium-range (high-resolution and ensemble) and seasonal forecast models. Products are available at 0.4 degrees resolution in GRIB2 format unless stated otherwise.
agricultureearth observationmeteorologicalnatural resourceweather
Historical and one-day delay data from the IDEAM radar network.
air temperatureatmosphereforecastgeosciencegeospatialglobalmeteorologicalmodelnear-surface air temperaturenear-surface relative humiditynetcdfweather
The flagship Numerical Weather Prediction (NWP) 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. The global ensemble (MOGREPS-G) produces forecasts for the whole globe up to a week ahead. The projection used is the Equirectangular Latitude-Longitude and the grid resolution is 20km. The forecast is updated regularly with a 4-hour time delay and formatted via NetCDF. ...
air temperatureatmosphereforecastgeosciencegeospatialglobalmeteorologicalmodelnear-surface air temperaturenear-surface relative humiditynetcdfweather
The flagship Numerical Weather Prediction (NWP) 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. The regional ensemble (MOGREPS-UK) produces forecasts for an area covering the UK for the next five days. In the UK ensemble the model parameters (temperature, pressure, wind, humidity, etc.) are forecast at grid points separated by about 2.2 km, and the model has 70 ver...
agricultureclimatemeteorologicalweather
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 measurement...
earth observationenergygeospatialmeteorologicalsolar
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.
agricultureclimateearth observationenvironmentalmeteorologicalmodelsustainabilitywaterweather
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...
meteorologicalsatellite imageryweather
Collection of spatially and temporally aligned GOES-16 ABI satellite imagery, NEXRAD radar mosaics, and GOES-16 GLM lightning detections.
agricultureearth observationmeteorologicalweather
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...
climateenvironmentalmeteorologicaloceanssustainabilityweather
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; ...
agricultureclimatemeteorologicalweather
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...
agricultureclimatedisaster responseenvironmentalmeteorologicalweather
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...
climate projectionsCMIP5CMIP6earth observationenergygeospatialmeteorologicalsolar
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.
atmosphereclimateearth observationforecastgeosciencehydrologymeteorologicalmodeloceansweather
Global real-time Earth system data deemed by the World Meteorological Organisation (WMO) as essential for provision of services for the protection of life and property and for the well-being of all nations. Data is sourced from all WMO Member countries / territories and retained for 24-hours. Met Office and NOAA operate this Global Cache service curating and publishing the dataset on behalf of WMO.
agricultureclimateclimate modelclimate projectionsdisaster responseelectricityenergyenvironmentalgeospatialmeteorologicalsolarsustainabilityweather
Wildfire projections for California and her environs in support of California's Fifth Climate Assessment supported with historical weather observations and renewable energy capacity profiles for grid operations.
agricultureclimatedisaster responseearth observationenvironmentalmeteorologicalmodelweather
Global and high-resolution regional atmospheric models from Météo-France.
agricultureclimatemeteorologicalweather
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...
agricultureclimatedisaster responseenvironmentalmeteorologicalweather
The Global Forecast System (GFS) is a weather forecast model produced by the National Centers for Environmental Prediction (NCEP). Dozens of atmospheric and land-soil variables are available through this dataset, from temperatures, winds, and precipitation to soil moisture and atmospheric ozone concentration. The GFS data files stored here can be immediately used for OAR/ARL’s NOAA-EPA Atmosphere-Chemistry Coupler Cloud (NACC-Cloud) tool, and are in a Network Common Data Form (netCDF), which is a very common format used across the scientific community. These particular GFS files contain a comprehensive number of global atmosphere/land variables at a relatively high spatiotemporal resolution (approximately 13x13 km horizontal, vertical resolution of 127 levels, and hourly), are not only necessary for the NACC-Cloud tool to adequately drive community air quality applications (e.g., U.S. EPA’s Community Multiscale Air Quality model; https://www.epa.gov/cmaq), but can be very useful for a myriad of other applications in the Earth system modeling communities (e.g., atmosphere, hydrosphere, pedosphere, etc.). While many other data file and record formats are indeed available for Earth system and climate research (e.g., GRIB, HDF, GeoTIFF), the netCDF files here are advantageous to the larger community because of the comprehensive, high spatiotemporal information they contain, and because they are more scalable, appendable, shareable, self-describing, and community-friendly (i.e., many tools available to the community of users). Out of the four operational GFS forecast cycles per day (at 00Z, 06Z, 12Z and 18Z) this particular netCDF dataset is updated daily (/inputs/yyyymmdd/) for the 12Z cycle and includes 24-hr output for both 2D (gfs.t12z.sfcf$0hh.nc) and 3D variables (gfs.t12z.atmf$0hh.nc).
Also available are netCDF formatted Global Land Surface Datasets (GLSDs) developed by Hung et al. (2024). The GLSDs are based on numerous satellite products, and have been gridded to match the GFS spatial resolution (~13x13 km). These GLSDs contain vegetation canopy data (e.g., land surface type, vegetation clumping index, leaf area index, vegetative canopy height, and green vegetation fraction) that are supplemental to and can be combined with the GFS meteorological netCDF data for various applications, including NOAA-ARL's canopy-app. The canop...
agricultureclimatemeteorologicalweather
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.
...
agricultureclimatedisaster responseenvironmentalmeteorologicaloceansweather
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...
atmosphereearth observationenvironmentalgeophysicsgeoscienceglobalmeteorologicalmodelnetcdfprecipitationsatellite imageryweather
The Tropical Cyclone Precipitation, Infrared, Microwave and Environmental Dataset (TC PRIMED) is a dataset centered around passive microwave observations of global tropical cyclones from low-Earth-orbiting satellites. TC PRIMED is a compilation of tropical cyclone data from various sources, including 1) tropical cyclone information from the National Oceanic and Atmospheric Administration (NOAA) National Weather Service National Hurricane Center (NHC) and Central Pacific Hurricane Center (CPHC) and the U.S. Department of Defense Joint Typhoon Warning Center, 2) low-Earth-orbiting satellite obse...
atmosphereelectricitymeteorologicalmodelsustainabilityturbulenceweatherzarr
Large Eddy Simulation (LES) data of the Winds of the North Sea in 2050 (WINS50) project.
atmosphereclimateclimate modelearth observationgeosciencegeospatialmeteorologicalsimulationsweatherzarr
Downscaled future and historical climate projections for California and her environs in support of California's Fifth Climate Assessment
climatecoastaldisaster responseenvironmentalmeteorologicaloceanssustainabilitywaterweather
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.
...
air temperatureatmosphereforecastmeteorologicalmodelnear-surface air temperaturenear-surface relative humiditynear-surface specific humidityocean circulationocean currentsocean sea surface heightocean simulationocean velocityoceanstime series forecastingweather
DMI forecast data consist of various models where each model contains different set of parameters relating to a specific domain like ocean (WAM), storm flooding (DKSS) or weather (HARMONIE)
atmosphereelectricitymeteorologicalmodelsustainabilityweather
ERA5 reanalysis data on AWS, preprocessed for use with the Weather Research and Forecasting (WRF) model.
atmospheremeteorologicalnear-surface air temperaturenetcdfprecipitation
EM-Earth provides data for precipitation, mean air temperature, air temperature range, and dew-point temperature at 0.1° spatial resolution over global land areas from 1950 to 2019. EM-Earth provides hourly/daily deterministic estimates, and daily probabilistic estimates (25 ensemble members), to meet the diverse requirements of hydrometeorological applications.
agriculturedisaster responseearth observationgeospatialmeteorologicalsatellite imageryweather
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...
agricultureclimatemeteorologicalweather
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 ...
agricultureearth observationmeteorologicalnatural resourcesustainabilityweather
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...
agricultureclimatemeteorologicalweather
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...
agricultureclimatemeteorologicalsustainabilityweather
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...
agricultureclimatemeteorologicalweather
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). ...
atmosphereclimatedata assimilationforecastgeosciencegeospatiallandmeteorologicalmodelnetcdfweather
NSF NCAR is providing a NetCDF-4 structured version of the 0.25 degree atmospheric ECMWF Reanalysis 5 (ERA5) to the AWS ODSP. ERA5 is produced using high-resolution forecasts (HRES) at 31 kilometer resolution (one fourth the spatial resolution of the operational model) and a 62 kilometer resolution ten member 4D-Var ensemble of data assimilation (EDA) in CY41r2 of ECMWF's Integrated Forecast System (IFS) with 137 hybrid sigma-pressure (model) levels in the vertical, up to a top level of 0.01 hPa. Atmospheric data on these levels are interpolated to 37 pressure levels (the same levels as in...
air qualityclimateearth observationmeteorologicalweather
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.
agricultureclimatedisaster responseenvironmentalmeteorologicalweather
The FourCastNet Global Forecast System (FourCastNetGFS) is an experimental system set up by the National Centers for Environmental Prediction (NCEP) to produce medium range global forecasts. The model runs on a 0.25 degree latitude-longitude grid (about 28 km) and 13 pressure levels. The model produces forecasts 4 times a day at 00Z, 06Z, 12Z and 18Z cycles. Major atmospheric and surface fields including temperature, wind components, geopotential height, relative humidity and 2 meter temperature and 10 meter winds are available. The products are 6 hourly forecasts up to 10 days. The data format is GRIB2.
The FourCastNetGFS system is an experimental weather forecast model built upon the pre-trained Nvidia’s FourCastNet Machine Learning Weather Prediction (MLWP) model version 2. The FourCastNet (Bonev et al, 2023) was developed by Nvidia using Adaptive Fourier Neural Operators. It uses a Fourier transform-based token-mixing scheme with the vision transformer architecture. This model is pre-trained with ECMWF’s ERA5 reanalysis data. The FourCastNetGFS takes one model state as initial condition from NCEP 0.25 degree GDAS analysis data and runs FourCastNet with weights from the pretrained FourCas...
agricultureclimatedisaster responseenvironmentalmeteorologicalweather
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.
agricultureearth observationforecasthydrologymeteorologicalnatural resourceweather
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...
agricultureair qualityair temperatureatmosphereclimateclimate modelclimate projectionsCMIP5CMIP6ecosystemselevationenvironmentalEulerianeventsfloodsfluid dynamicsgeosciencegeospatialhdf5healthHPChydrologyinfrastructureland coverland usemeteorologicalmodelnear-surface air temperaturenear-surface relative humiditynear-surface specific humiditynetcdfopen source softwarephysicspost-processingprecipitationradiationsimulationsuswaterweather
The data are a subset of the EPA Dynamically Downscaled Ensemble (EDDE), Version 1. EDDE is a collection of physics-based modeled data that represent 3D atmospheric conditions for historical and future periods under different scenarios. The EDDE Version 1 datasets cover the contiguous United States at a horizontal grid spacing of 36 kilometers at hourly increments. EDDE Version 1 includes simulations that have been dynamically downscaled from multiple global climate models (GCMs) under both mid- and high-emission scenarios from the Fifth Coupled Model Intercomparison Project (CMIP5) using the...
agricultureair qualityair temperatureatmosphereclimateclimate modelclimate projectionsCMIP5CMIP6ecosystemselevationenvironmentalEulerianeventsfloodsfluid dynamicsgeosciencegeospatialhdf5healthHPChydrologyinfrastructureland coverland usemeteorologicalmodelnear-surface air temperaturenear-surface relative humiditynear-surface specific humiditynetcdfopen source softwarephysicspost-processingprecipitationradiationsimulationsuswaterweather
The data are a subset of the EPA Dynamically Downscaled Ensemble (EDDE), Version 2. EDDE is a collection of physics-based modeled data that represent 3D atmospheric conditions for historical and future periods under different scenarios. The EDDE Version 2 datasets cover the contiguous United States at a horizontal grid spacing of 12 kilometers at hourly increments. EDDE Version 2 will include simulations that have been dynamically downscaled from multiple global climate models (GCMs) under multiple emission scenarios from the Sixth Coupled Model Intercomparison Project (CMIP6) using the Weath...
agricultureclimateearth observationmeteorologicalweather
HIRLAM (High Resolution Limited Area Model) is an operational synoptic and mesoscale weather prediction model managed by the Finnish Meteorological Institute.
climatecoastaldisaster responseenvironmentalglobalmarine navigationmeteorologicaloceanssustainabilitywaterweather
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...
agricultureclimatemeteorologicalsustainabilityweather
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...
climatecoastaldisaster responseenvironmentalmeteorologicaloceanswaterweather
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
agricultureclimatemeteorologicalsustainabilityweather
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 ...
agricultureclimatedisaster responseenvironmentalmeteorologicalweather
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...
agricultureclimatemeteorologicalweather
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...
agriculturemeteorologicalwaterweather
NOTE - The legacy on-premises version of the Global Hydroestimator (GHE) is being retired. It is being replaced by the global Enterprise Rain Rate algorithm. You can find Enterprise Rain Rate products in the new bucket listed under the Resources section.
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.
agricultureclimatemeteorologicalweather
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 ...
climatecoastaldisaster responseenvironmentalglobalmeteorologicaloceanswaterweather
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...
climatecoastaldisaster responseenvironmentalglobalmeteorologicaloceanswaterweather
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...
agricultureclimatemeteorologicalweather
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:
agricultureclimatemeteorologicalweather
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...
agricultureclimatecogmeteorologicalweather
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.
agricultureclimatemeteorologicalweather
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...
agricultureclimatemeteorologicaloceanssustainabilityweather
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...
agricultureclimatemeteorologicalweather
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...
agricultureclimatemeteorologicalweather
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 ...
agricultureclimatemeteorologicalweather
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...
climatemeteorologicalsolarweather
Space weather forecast and observation data is collected and disseminated by NOAA’s Space Weather Prediction Center (SWPC) in Boulder, CO. SWPC produces forecasts for multiple space weather phenomenon types and the resulting impacts to Earth and human activities. A variety of products are available that provide these forecast expectations, and their respective measurements, in formats that range from detailed technical forecast discussions to NOAA Scale values to simple bulletins that give information in laymen's terms. Forecasting is the prediction of future events, based on analysis and...
agricultureclimatemeteorologicalsustainabilityweather
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...
agricultureclimatemeteorologicalweather
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...
agricultureclimatemeteorologicalweather
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...
agricultureclimatedisaster responseenvironmentalmeteorologicaloceansweather
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...
agricultureclimatemeteorologicalweather
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...
agricultureclimatemeteorologicalweather
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...
agricultureclimatemeteorologicalweather
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...
agricultureclimatemeteorologicalweather
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...
climatemeteorologicalsolarweather
The WSA-Enlil heliospheric model provides critical information regarding the propagation of solar Coronal Mass Ejections (CMEs) and transient structures within the heliosphere. Two distinct models comprise the WSA-Enlil modeling system; 1) the Wang-Sheeley-Arge (WSA) semi-empirical solar coronal model, and 2) the Enlil magnetohydrodynamic (MHD) heliospheric model. MHD modeling of the full domain (solar photosphere to Earth) is extremely computationally demanding due to the large parameter space and resulting characteristic speeds within the system. To reduce the computational burden and improve the timeliness (and hence the utility in forecasting space weather disturbances) of model results, the domain of the MHD model (Enlil) is limited from 21.5 Solar Radii (R_s) to just beyond the orbit of Earth, while the inner portion, spanning from the solar photosphere to 21.5R_s, is characterized by the WSA model. This coupled modeling system is driven by solar synoptic maps composed of numerous magnetogram observations from the National Solar Observatory’s (NSO) Global Oscillation Network Group (GONG). Such maps provide a full surface description of solar photospheric magnetic flux density, while not accounting for the evolution of surface features for regions outside the view of the observatories.
In its current configuration (NOAA WSA-Enlil V3.0), the modeling system consists of WSA V5.4 and Enlil V2.9e. The system relies upon the zero point corrected GONG synoptic maps (mrzqs) to define the inner photospheric boundary.
The operational data files provided in this bucket include NetCDF files containing 3-dimensional gridded neutral density from 100 to 1000 km, Total Electron Content (TEC), and Maximum Usable Frequency (MUF).
The full 3D datasets from the operational model are provided here as compressed tar files with naming convention wsa_enlil.mrid########.full3d.tgz. These files consist of the full set of 3D datacubes (tim..nc), all time series results stored at predefined observation points (evo..nc), and supplemented by the operational CME fits (conefiles) and the operationally...
climatemeteorologicalsolarweather
The coupled Whole Atmosphere Model-Ionosphere Plasmasphere Electrodynamics (WAM-IPE) Forecast System (WFS) is developed and maintained by the NOAA Space Weather Prediction Center (SWPC). The WAM-IPE model provides a specification of ionosphere and thermosphere conditions with real-time nowcasts and forecasts up to two days in advance in response to solar, geomagnetic, and lower atmospheric forcing. The WAM is an extension of the Global Forecast System (GFS) with a spectral hydrostatic dynamical core utilizing an enthalpy thermodynamic variable to 150 vertical levels on a hybrid pressure-sigma grid, with a model top of approximately 3 x 10-7 Pa (typically 400-600km depending on levels of solar activity). Additional upper atmospheric physics and chemistry, including electrodynamics and plasma processes, are included. The IPE model provides the plasma component of the atmosphere. It is a time-dependent, global 3D model of the ionosphere and plasmasphere from 90 km to approximately 10,000 km. WAM fields of winds, temperature, and molecular and atomic atmospheric composition are coupled to IPE to enable the plasma to respond to changes driven by the neutral atmosphere.
The operational WAM-IPE is currently running in two different Concepts of Operation (CONOPS) to produce results of Nowcast and Forecast. The WAM-IPE real-time nowcast system (WRS) ingests real-time solar wind parameters every 5 minutes from NOAA’s spacecrafts located at Lagrange point 1 (L1) between the Sun and Earth in order to capture rapid changes in the ionosphere and thermosphere due to the sudden onset of geomagnetic storms. The nowcast is reinitialized every six hours to include the latest forcing from the lower atmosphere. The forecast system (WFS) runs four times daily (0, 6, 12, 18 UT), providing two-day forecasts. Observed solar wind parameters are used whenever observational values are available, for the forecast portion, the forecasted 3-hour Kp and daily F10.7 issued by SWFO are ingested into the model to estimate solar wind parameters. Lower atmospheric data assimilation only carries out twice daily at 0 and 12 UT cycles to maintain the stability of the coupled model. Model version v1.2 became available in July 2023, featuring the implementation of the WRS into operations, as well as improvements to the Kp-derived solar wind parameters utilized by WFS forecasts.
The data files within this bucket are provided strictly on a non-operational basis, with no guarantee of timely delivery or availability. There may exist temporal gaps in coverage.
The top-level versioned directories (v1.x) include NetCDF files from operational runs providing 3-dimensional gridded neutral density every 10 minutes with an altitude range from 100 to 1000 km. wfs.YYYYMMDD subdirectories contain two-day forecasts, updated four times daily (cycle initialization 00, 06, 12, 18 UT). wrs.YYYYMMDD subdirectories contain real-time nowcast neutral density outputs, reinitialized ever...
air qualitymeteorologicalregulatoryweather
The data are part of the 2022 Modeling Platform used to support regulatory actions and technical analyses conducted by the EPA's Office of Air Quality Planning and Standards. Specifically, this data includes Weather Research and Forecasting Model (v4.4.2) conducted at a 12-km resolution over the Continental United States (12US). MCIP-processed files and wrfcamx-processed (12US1 domain) are also available as part of this dataset to assist in the use of emissions processing and photochemical modeling. These files may be used in downstream applications to generate emissions, photochemical ...
earth observationmeteorologicalnatural resourceweather
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...
air temperatureatmospheremeteorologicaloceansradiation
Enhancement of Measurements on Ships of Opportunity (SOOP)-Air Sea Flux sub-facility collects underway meteorological and oceanographic observations during scientific and Antarctic resupply voyages in the oceans adjacent to Australia. Data product is quality controlled bulk air-sea fluxes and input observations. Research Vessel Real Time Air-Sea Fluxes, equips the Marine National Facility (MNF) (Research Vessels Southern Surveyor and Investigator), the Australian Antarctic Division (Research and Supply Vessels Aurora Australis and Nuyina), and Research Vessel Tangaroa with "climate qualit...
air temperatureatmospheremeteorologicaloceansprecipitationradiation
Enhancement of Measurements on Ships of Opportunity (SOOP)-Air Sea Flux sub-facility collects underway meteorological and oceanographic observations during scientific and Antarctic resupply voyages in the oceans adjacent to Australia. Data product is quality controlled observations. Research Vessel Real Time Air-Sea Fluxes, equips the Marine National Facility (MNF) (Research Vessels Southern Surveyor and Investigator), the Australian Antarctic Division (Research and Supply Vessels Aurora Australis and Nuyina), and Research Vessel Tangaroa with "climate quality" meteorological measure...
air qualityenergyenvironmentalmeteorological
This dataset includes detailed information about coal power plants, their locations, capacities, emissions, and other relevant attributes around the Indian Gangetic Plain.
environmentalmeteorologicalweather
This is an archive of pure AI-based weather prediction reforecasts produced collaboratively between the Cooperative Institute for Research in the Atmosphere (CIRA) and the NOAA Global Systems Laboratory (NOAA-GSL).
Currently, FourCastNetv2-small, Pangu-Weather, and GraphCast are included, with more models to come. Each of these models has been initialized with both NOAA GFS (directories with no extension) and ECMWF IFS initial conditions (directories ending in "_IFS"). The datasets are updated with near-real-time data twice per day (00Z and 12Z initializations).
...