# dynamical.org - full catalog

> Advancing humanity's ability to access, understand, and act on accurate weather and climate data. This is an expanded, single-file dump of every dataset in the dynamical.org catalog: prose descriptions, spatial/temporal domain, resolution, license, data-access assets, and the full variable list. All content is public and requires no authentication. A concise index is at /llms.txt.

**The STAC catalog at https://stac.dynamical.org/catalog.json is the authoritative source of truth** for data product documentation, dataset metadata, and data asset (Zarr/Icechunk) locations. The entries below are generated from it - for the most complete and current metadata (all variables, dimensions, extents, and exact asset URIs), read each dataset's STAC Collection linked below. For convenient programmatic access, use the `dynamical-catalog` Python library (`pip install dynamical-catalog`): `import dynamical_catalog; ds = dynamical_catalog.open("<dataset-id>")`.

For LLM agents, an MCP server is live at https://mcp.dynamical.org/mcp (server name `dynamical-catalog`, streamable HTTP transport) with tools `search_catalog`, `get_dataset_info`, `get_access_pattern`, and `list_recent_runs` - prefer these for programmatic discovery, access snippets, and forecast-run freshness.

Datasets are cloud-optimized weather/climate archives distributed as Icechunk (Zarr) repositories, described by STAC Collections at https://stac.dynamical.org and rendered as human pages under https://dynamical.org/catalog/.

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

### NOAA GFS analysis

- Page: https://dynamical.org/catalog/noaa-gfs-analysis/
- STAC Collection: https://stac.dynamical.org/noaa-gfs-analysis/collection.json
- Spatial domain: Global (resolution: 0.25 degrees (~20km))
- Time domain: 2021-05-01 00:00:00 UTC to Present (resolution: 1 hour)
- License: CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0/)
- Data asset (application/x-icechunk): s3://dynamical-noaa-gfs/noaa-gfs-analysis/v0.1.0.icechunk/

This analysis dataset is an archive of the model's best estimate of past weather. It is created by concatenating the first few hours of each historical forecast to provide a dataset with dimensions time, latitude, and longitude.

Variables: categorical_freezing_rain_surface [1], categorical_ice_pellets_surface [1], categorical_rain_surface [1], categorical_snow_surface [1], downward_long_wave_radiation_flux_surface [W m-2], downward_short_wave_radiation_flux_surface [W m-2], geopotential_height_cloud_ceiling [m], maximum_temperature_2m [degree_Celsius], minimum_temperature_2m [degree_Celsius], percent_frozen_precipitation_surface [percent], precipitable_water_atmosphere [kg m-2], precipitation_surface [kg m-2 s-1], pressure_reduced_to_mean_sea_level [Pa], pressure_surface [Pa], relative_humidity_2m [percent], temperature_2m [degree_Celsius], total_cloud_cover_atmosphere [percent], wind_u_100m [m s-1], wind_u_10m [m s-1], wind_v_100m [m s-1], wind_v_10m [m s-1]

### NOAA GFS forecast

- Page: https://dynamical.org/catalog/noaa-gfs-forecast/
- STAC Collection: https://stac.dynamical.org/noaa-gfs-forecast/collection.json
- Spatial domain: Global (resolution: 0.25 degrees (~20km))
- Time domain: Forecasts initialized 2021-05-01 00:00:00 UTC to Present (resolution: Forecasts initialized every 6 hours)
- Forecast domain: Forecast lead time 0-384 hours (0-16 days) ahead (resolution: Forecast step 0-120 hours: hourly, 123-384 hours: 3 hourly)
- License: CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0/)
- Data asset (application/x-icechunk): s3://dynamical-noaa-gfs/noaa-gfs-forecast/v0.2.7.icechunk/

This dataset is an archive of past and present GFS forecasts. Forecasts are identified by an initialization time (`init_time`) denoting the start time of the model run. Each forecast steps forward in time along the `lead_time` dimension.

Variables: categorical_freezing_rain_surface [1], categorical_ice_pellets_surface [1], categorical_rain_surface [1], categorical_snow_surface [1], downward_long_wave_radiation_flux_surface [W m-2], downward_short_wave_radiation_flux_surface [W m-2], geopotential_height_cloud_ceiling [m], maximum_temperature_2m [degree_Celsius], minimum_temperature_2m [degree_Celsius], percent_frozen_precipitation_surface [percent], precipitable_water_atmosphere [kg m-2], precipitation_surface [kg m-2 s-1], pressure_reduced_to_mean_sea_level [Pa], pressure_surface [Pa], relative_humidity_2m [percent], temperature_2m [degree_Celsius], total_cloud_cover_atmosphere [percent], wind_u_100m [m s-1], wind_u_10m [m s-1], wind_v_100m [m s-1], wind_v_10m [m s-1]

## NOAA GEFS
The Global Ensemble Forecast System (GEFS) is a National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) weather forecast model. GEFS creates 31 separate forecasts (ensemble members) to describe the range of forecast uncertainty.

### NOAA GEFS forecast, 35 day

- Page: https://dynamical.org/catalog/noaa-gefs-forecast-35-day/
- STAC Collection: https://stac.dynamical.org/noaa-gefs-forecast-35-day/collection.json
- Spatial domain: Global (resolution: 0-240 hours: 0.25 degrees (~20km), 243-840 hours: 0.5 degrees (~40km))
- Time domain: Forecasts initialized 2020-10-01 00:00:00 UTC to Present (resolution: Forecasts initialized every 24 hours)
- Forecast domain: Forecast lead time 0-840 hours (0-35 days) ahead (resolution: Forecast step 0-240 hours: 3 hourly, 243-840 hours: 6 hourly)
- License: CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0/)
- Data asset (application/x-icechunk): s3://dynamical-noaa-gefs/noaa-gefs-forecast-35-day/v0.2.0.icechunk/

This dataset is an archive of past and present GEFS forecasts. Forecasts are identified by an initialization time (`init_time`) denoting the start time of the model run as well as by the `ensemble_member`. Each forecast has a 3 hourly forecast step along the `lead_time` dimension. This dataset contains only the 00 hour UTC initialization times which produce the full length, 35 day forecast.

Variables: categorical_freezing_rain_surface [1], categorical_ice_pellets_surface [1], categorical_rain_surface [1], categorical_snow_surface [1], downward_long_wave_radiation_flux_surface [W m-2], downward_short_wave_radiation_flux_surface [W m-2], geopotential_height_500hpa [m], geopotential_height_cloud_ceiling [m], maximum_temperature_2m [degree_Celsius], minimum_temperature_2m [degree_Celsius], percent_frozen_precipitation_surface [percent], precipitable_water_atmosphere [kg m-2], precipitation_surface [kg m-2 s-1], pressure_reduced_to_mean_sea_level [Pa], pressure_surface [Pa], relative_humidity_2m [percent], temperature_2m [degree_Celsius], total_cloud_cover_atmosphere [percent], wind_u_100m [m s-1], wind_u_10m [m s-1], wind_v_100m [m s-1], wind_v_10m [m s-1]

### NOAA GEFS analysis

- Page: https://dynamical.org/catalog/noaa-gefs-analysis/
- STAC Collection: https://stac.dynamical.org/noaa-gefs-analysis/collection.json
- Spatial domain: Global (resolution: 0.25 degrees (~20km))
- Time domain: 2000-01-01 00:00:00 UTC to Present (resolution: 3.0 hours)
- License: CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0/)
- Data asset (application/x-icechunk): s3://dynamical-noaa-gefs/noaa-gefs-analysis/v0.1.2.icechunk/

This analysis dataset is an archive of the model's best estimate of past weather. It is created by concatenating the first few hours of each historical forecast to provide a dataset with dimensions time, latitude, and longitude.

Variables: categorical_freezing_rain_surface [1], categorical_ice_pellets_surface [1], categorical_rain_surface [1], categorical_snow_surface [1], downward_long_wave_radiation_flux_surface [W m-2], downward_short_wave_radiation_flux_surface [W m-2], geopotential_height_500hpa [m], geopotential_height_cloud_ceiling [m], maximum_temperature_2m [degree_Celsius], minimum_temperature_2m [degree_Celsius], percent_frozen_precipitation_surface [percent], precipitable_water_atmosphere [kg m-2], precipitation_surface [kg m-2 s-1], pressure_reduced_to_mean_sea_level [Pa], pressure_surface [Pa], relative_humidity_2m [percent], temperature_2m [degree_Celsius], total_cloud_cover_atmosphere [percent], wind_u_100m [m s-1], wind_u_10m [m s-1], wind_v_100m [m s-1], wind_v_10m [m s-1]

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

### NOAA HRRR forecast, 48 hour

- Page: https://dynamical.org/catalog/noaa-hrrr-forecast-48-hour/
- STAC Collection: https://stac.dynamical.org/noaa-hrrr-forecast-48-hour/collection.json
- Spatial domain: Continental United States (resolution: 3 km)
- Time domain: Forecasts initialized 2018-07-13 12:00:00 UTC to Present (resolution: Forecasts initialized every 6 hours)
- Forecast domain: Forecast lead time 0-48 hours ahead (resolution: Hourly)
- License: CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0/)
- Data asset (application/x-icechunk): s3://dynamical-noaa-hrrr/noaa-hrrr-forecast-48-hour/v0.1.0.icechunk/

This dataset is an archive of past and present HRRR forecasts. Forecasts are identified by an initialization time (`init_time`) denoting the start time of the model run. Each forecast has an hourly forecast step along the `lead_time` dimension. This dataset contains only the 00, 06, 12, and 18 hour UTC initialization times which produce the full length, 48 hour forecast.

This dataset uses the native HRRR Lambert Conformal Conic projection, with spatial indexing along the `x` and `y` dimensions. The example notebook shows how to use the embedded spatial reference to select geographic areas of interest.

Variables: categorical_freezing_rain_surface [1], categorical_ice_pellets_surface [1], categorical_rain_surface [1], categorical_snow_surface [1], composite_reflectivity [dBZ], dew_point_temperature_2m [degree_Celsius], downward_long_wave_radiation_flux_surface [W m-2], downward_short_wave_radiation_flux_surface [W m-2], geopotential_height_cloud_ceiling [m], percent_frozen_precipitation_surface [percent], precipitable_water_atmosphere [kg m-2], precipitation_surface [kg m-2 s-1], pressure_reduced_to_mean_sea_level [Pa], pressure_surface [Pa], relative_humidity_2m [percent], snow_area_fraction_surface [1], snow_thickness_surface [m], snow_water_equivalent_surface [m], snowfall_surface [m s-1], temperature_2m [degree_Celsius], total_cloud_cover_atmosphere [percent], wind_gust_surface [m s-1], wind_u_10m [m s-1], wind_u_80m [m s-1], wind_v_10m [m s-1], wind_v_80m [m s-1]

### NOAA HRRR analysis

- Page: https://dynamical.org/catalog/noaa-hrrr-analysis/
- STAC Collection: https://stac.dynamical.org/noaa-hrrr-analysis/collection.json
- Spatial domain: Continental United States (resolution: 3 km)
- Time domain: 2014-10-01 00:00:00 UTC to Present (resolution: 1 hour)
- License: CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0/)
- Data asset (application/x-icechunk): s3://dynamical-noaa-hrrr/noaa-hrrr-analysis/v0.2.0.icechunk/

This analysis dataset is an archive of the model's best estimate of past weather. It is created by concatenating the first hour of each historical forecast to provide a dataset with dimensions time, x, and y.

This dataset uses the native HRRR Lambert Conformal Conic projection, with spatial indexing along the `x` and `y` dimensions. The example notebook shows how to use the embedded spatial reference to select geographic areas of interest.

Variables: categorical_freezing_rain_surface [1], categorical_ice_pellets_surface [1], categorical_rain_surface [1], categorical_snow_surface [1], composite_reflectivity [dBZ], dew_point_temperature_2m [degree_Celsius], downward_long_wave_radiation_flux_surface [W m-2], downward_short_wave_radiation_flux_surface [W m-2], geopotential_height_cloud_ceiling [m], percent_frozen_precipitation_surface [percent], precipitable_water_atmosphere [kg m-2], precipitation_surface [kg m-2 s-1], pressure_reduced_to_mean_sea_level [Pa], pressure_surface [Pa], relative_humidity_2m [percent], snow_area_fraction_surface [1], snow_thickness_surface [m], snow_water_equivalent_surface [m], snowfall_surface [m s-1], temperature_2m [degree_Celsius], total_cloud_cover_atmosphere [percent], wind_gust_surface [m s-1], wind_u_10m [m s-1], wind_u_80m [m s-1], wind_v_10m [m s-1], wind_v_80m [m s-1]

## NOAA MRMS
The NOAA Multi-Radar/Multi-Sensor System (MRMS) integrates data from multiple radars and radar networks, surface observations, numerical weather prediction (NWP) models, and climatology to generate seamless, high spatio-temporal resolution mosaics at low latency focused on hail, wind, tornado, quantitative precipitation estimations, convection, icing, and turbulence.

### NOAA MRMS CONUS analysis, hourly

- Page: https://dynamical.org/catalog/noaa-mrms-conus-analysis-hourly/
- STAC Collection: https://stac.dynamical.org/noaa-mrms-conus-analysis-hourly/collection.json
- Spatial domain: Continental United States (resolution: 0.01 degrees (~1km))
- Time domain: 2014-11-01 00:00:00 UTC to Present (resolution: 1 hour)
- License: CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0/)
- Data asset (application/x-icechunk): s3://dynamical-noaa-mrms/noaa-mrms-conus-analysis-hourly/v0.3.0.icechunk/

This analysis dataset is an archive of MRMS radar and multi-sensor precipitation and weather analyses over the contiguous United States (CONUS).

Variables: categorical_precipitation_type_surface [1], flash_qpe_ffg_max_surface [percent], precipitation_pass_1_surface [kg m-2 s-1], precipitation_pass_2_surface [kg m-2 s-1], precipitation_radar_only_surface [kg m-2 s-1], precipitation_surface [kg m-2 s-1]

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

### ECMWF AIFS Single forecast

- Page: https://dynamical.org/catalog/ecmwf-aifs-single-forecast/
- STAC Collection: https://stac.dynamical.org/ecmwf-aifs-single-forecast/collection.json
- Spatial domain: Global (resolution: 0.25 degrees (~20km))
- Time domain: Forecasts initialized 2024-04-01 00:00:00 UTC to Present (resolution: Forecasts initialized every 6 hours)
- Forecast domain: Forecast lead time 0-360 hours (0-15 days) ahead (resolution: 6 hourly)
- License: CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0/)
- Data asset (application/x-icechunk): s3://dynamical-ecmwf-aifs-single/ecmwf-aifs-single-forecast/v0.1.0.icechunk/

This dataset is an archive of past and present ECMWF AIFS Single forecasts. Forecasts are identified by an initialization time (`init_time`) denoting the start time of the model run. Each forecast steps forward in time along the `lead_time` dimension, from 0 to 360 hours (15 days) at a 6 hourly step.

Variables: dew_point_temperature_2m [degree_Celsius], downward_long_wave_radiation_flux_surface [W m-2], downward_short_wave_radiation_flux_surface [W m-2], geopotential_height_500hpa [m], geopotential_height_850hpa [m], geopotential_height_925hpa [m], precipitation_surface [kg m-2 s-1], pressure_reduced_to_mean_sea_level [Pa], pressure_surface [Pa], temperature_2m [degree_Celsius], temperature_850hpa [degree_Celsius], temperature_925hpa [degree_Celsius], total_cloud_cover_atmosphere [percent], wind_u_100m [m s-1], wind_u_10m [m s-1], wind_v_100m [m s-1], wind_v_10m [m s-1]

## ECMWF AIFS ENS
The Artificial Intelligence Forecasting System (AIFS) is a data driven forecast model developed by the European Centre for Medium-Range Weather Forecasts (ECMWF). AIFS ENS is the ensemble configuration of AIFS, containing 51 ensemble members. AIFS is trained on ECMWF's ERA5 re-analysis and ECMWF's operational numerical weather prediction (NWP) analyses.

### ECMWF AIFS ENS forecast

- Page: https://dynamical.org/catalog/ecmwf-aifs-ens-forecast/
- STAC Collection: https://stac.dynamical.org/ecmwf-aifs-ens-forecast/collection.json
- Spatial domain: Global (resolution: 0.25 degrees (~20km))
- Time domain: Forecasts initialized 2025-07-02 00:00:00 UTC to Present (resolution: Forecasts initialized every 6 hours)
- Forecast domain: Forecast lead time 0-360 hours (0-15 days) ahead (resolution: 6 hourly)
- License: CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0/)
- Data asset (application/x-icechunk): s3://dynamical-ecmwf-aifs-ens/ecmwf-aifs-ens-forecast/v0.1.0.icechunk/

This dataset is an archive of past and present ECMWF AIFS ENS forecasts. Forecasts are identified by an initialization time (`init_time`) denoting the start time of the model run, as well as by the `ensemble_member`. Each forecast steps forward in time along the `lead_time` dimension.

Variables: dew_point_temperature_2m [degree_Celsius], downward_long_wave_radiation_flux_surface [W m-2], downward_short_wave_radiation_flux_surface [W m-2], geopotential_height_500hpa [m], geopotential_height_850hpa [m], geopotential_height_925hpa [m], precipitation_surface [kg m-2 s-1], pressure_reduced_to_mean_sea_level [Pa], pressure_surface [Pa], temperature_2m [degree_Celsius], temperature_850hpa [degree_Celsius], temperature_925hpa [degree_Celsius], total_cloud_cover_atmosphere [percent], wind_u_100m [m s-1], wind_u_10m [m s-1], wind_v_100m [m s-1], wind_v_10m [m s-1]

## ECMWF IFS ENS
The Integrated Forecasting System (IFS) is a global forecast model developed by ECMWF. ENS is an ensemble configuration of IFS, containing 51 ensemble members. IFS consists of a numerical model of the Earth system, which includes an atmospheric model at its heart, coupled with models of other Earth system components such as the ocean. The data assimilation system combines the latest weather observations with a recent forecast to obtain the best possible estimate of the current state of the Earth system.

### ECMWF IFS ENS forecast, 15 day, 0.25 degree

- Page: https://dynamical.org/catalog/ecmwf-ifs-ens-forecast-15-day-0-25-degree/
- STAC Collection: https://stac.dynamical.org/ecmwf-ifs-ens-forecast-15-day-0-25-degree/collection.json
- Spatial domain: Global (resolution: 0.25 degrees (~20km))
- Time domain: Forecasts initialized 2024-04-01 00:00:00 UTC to Present (resolution: Forecasts initialized every 24 hours)
- Forecast domain: Forecast lead time 0-360 hours (0-15 days) ahead (resolution: Forecast step 0-144 hours: 3 hourly, 144-360 hours: 6 hourly)
- License: CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0/)
- Data asset (application/x-icechunk): s3://dynamical-ecmwf-ifs-ens/ecmwf-ifs-ens-forecast-15-day-0-25-degree/v0.1.0.icechunk/

This dataset is an archive of past and present ECMWF IFS ENS forecasts. Forecasts are identified by an initialization time (`init_time`) denoting the start time of the model run, as well as by the `ensemble_member`. Along the `lead_time` dimension, each forecast begins at a 3 hourly forecast step (0-144 hours) and switches to a 6 hourly step for days 6 through 15 of the forecast (hours 144-360). This dataset contains the 00 UTC initialization times only.

Variables: categorical_precipitation_type_surface [1], dew_point_temperature_2m [degree_Celsius], downward_long_wave_radiation_flux_surface [W m-2], downward_short_wave_radiation_flux_surface [W m-2], geopotential_height_500hpa [m], geopotential_height_850hpa [m], geopotential_height_925hpa [m], precipitation_surface [kg m-2 s-1], pressure_reduced_to_mean_sea_level [Pa], pressure_surface [Pa], temperature_2m [degree_Celsius], temperature_850hpa [degree_Celsius], temperature_925hpa [degree_Celsius], total_cloud_cover_atmosphere [percent], wind_gust_10m [m s-1], wind_u_100m [m s-1], wind_u_10m [m s-1], wind_v_100m [m s-1], wind_v_10m [m s-1]

## DWD ICON-EU
ICON-EU is a regional weather forecast model operated by Deutscher Wetterdienst (DWD), Germany's national meteorological service. ICON-EU is a nested configuration of DWD's global ICON (Icosahedral Non-hydrostatic) model that provides high-resolution forecasts over Europe.

### DWD ICON-EU forecast, 5 day

- Page: https://dynamical.org/catalog/dwd-icon-eu-forecast-5-day/
- STAC Collection: https://stac.dynamical.org/dwd-icon-eu-forecast-5-day/collection.json
- Spatial domain: Europe (resolution: 0.0625 degrees (~7km))
- Time domain: Forecasts initialized 2026-02-10 00:00:00 UTC to Present (resolution: Forecasts initialized every 6 hours)
- Forecast domain: Forecast lead time 0-120 hours (0-5 days) ahead (resolution: Forecast step 0-78 hours: hourly, 81-120 hours: 3 hourly)
- License: CC-BY-4.0 (https://creativecommons.org/licenses/by/4.0/)
- Data asset (application/x-icechunk): s3://dynamical-dwd-icon-eu/dwd-icon-eu-forecast-5-day/v0.2.0.icechunk/

This dataset is an archive of past and present ICON-EU forecasts. Forecasts are identified by an initialization time (`init_time`) denoting the start time of the model run and step forward in time along the `lead_time` dimension. This dataset contains only the 00, 06, 12, and 18 hour UTC initialization times which produce the full length, 5 day forecast.

Variables: cloud_cover_high [percent], cloud_cover_low [percent], cloud_cover_medium [percent], convective_available_potential_energy_atmosphere [J kg-1], dew_point_temperature_2m [degree_Celsius], downward_diffuse_short_wave_radiation_flux_surface [W m-2], downward_direct_short_wave_radiation_flux_surface [W m-2], precipitable_water_atmosphere [kg m-2], precipitation_surface [kg m-2 s-1], pressure_reduced_to_mean_sea_level [Pa], pressure_surface [Pa], relative_humidity_2m [percent], snow_thickness_surface [m], snow_water_equivalent_surface [m], temperature_2m [degree_Celsius], total_cloud_cover_atmosphere [percent], wind_gust_10m [m s-1], wind_u_10m [m s-1], wind_v_10m [m s-1]