NOAA GFS forecast

Spatial domain	Global
Spatial resolution	0.25 degrees (~20km)
Time domain	Forecasts initialized 2021-05-01 00:00:00 UTC to Present
Time resolution	Forecasts initialized every 6 hours
Forecast domain	Forecast lead time 0-384 hours (0-16 days) ahead
Forecast resolution	Forecast step 0-120 hours: hourly, 123-384 hours: 3 hourly

STAC (browse)

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.

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.

Related Datasets

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

Examples

	Quickstart (Github)
	Quickstart (Colab)
	Heating degree days: GFS vs AIFS (Github)
	Heating degree days: GFS vs AIFS (Colab)

dynamical.org - NOAA GFS forecast

Maximum temperature in a forecast

import dynamical_catalog  # dynamical-catalog>=0.5.0

ds = dynamical_catalog.open("noaa-gfs-forecast")
ds["temperature_2m"].sel(init_time="2025-01-01T00", latitude=0, longitude=0).max().compute()

Dimensions

	min	max	units
init_time	2021-05-01T00:00:00Z	Present	seconds since 1970-01-01

latitude	-90	90	degree_north

lead_time	0	1382400	seconds

longitude	-180	179.75	degree_east

Variables

	units	dimensions
categorical_freezing_rain_surface (`cfrzr`) Categorical freezing rain Presence/absence over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z). 0=no; 1=yes.	1	init_time × lead_time × latitude × longitude

categorical_ice_pellets_surface (`cicep`) Categorical ice pellets Presence/absence over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z). 0=no; 1=yes.	1	init_time × lead_time × latitude × longitude

categorical_rain_surface (`crain`) Categorical rain Presence/absence over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z). 0=no; 1=yes.	1	init_time × lead_time × latitude × longitude

categorical_snow_surface (`csnow`) Categorical snow Presence/absence over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z). 0=no; 1=yes.	1	init_time × lead_time × latitude × longitude

downward_long_wave_radiation_flux_surface (`sdlwrf`) Surface downward long-wave radiation flux Average over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z).	W m-2	init_time × lead_time × latitude × longitude

downward_short_wave_radiation_flux_surface (`sdswrf`) Surface downward short-wave radiation flux Average over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z).	W m-2	init_time × lead_time × latitude × longitude

geopotential_height_cloud_ceiling (`gh`) Geopotential height	m	init_time × lead_time × latitude × longitude

maximum_temperature_2m (`tmax`) Maximum temperature Maximum over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z).	degree_Celsius	init_time × lead_time × latitude × longitude

minimum_temperature_2m (`tmin`) Minimum temperature Minimum over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z).	degree_Celsius	init_time × lead_time × latitude × longitude

percent_frozen_precipitation_surface (`cpofp`) Percent frozen precipitation	percent	init_time × lead_time × latitude × longitude

precipitable_water_atmosphere (`pwat`) Precipitable water	kg m-2	init_time × lead_time × latitude × longitude

precipitation_surface (`prate`) Precipitation rate Average precipitation rate since the previous forecast step. Units equivalent to mm/s.	kg m-2 s-1	init_time × lead_time × latitude × longitude

pressure_reduced_to_mean_sea_level (`prmsl`) Pressure reduced to MSL	Pa	init_time × lead_time × latitude × longitude

pressure_surface (`sp`) Surface pressure	Pa	init_time × lead_time × latitude × longitude

relative_humidity_2m (`2r`) 2 metre relative humidity	percent	init_time × lead_time × latitude × longitude

temperature_2m (`2t`) 2 metre temperature	degree_Celsius	init_time × lead_time × latitude × longitude

total_cloud_cover_atmosphere (`tcc`) Total cloud cover Average over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z).	percent	init_time × lead_time × latitude × longitude

wind_u_100m (`100u`) 100 metre U wind component	m s-1	init_time × lead_time × latitude × longitude

wind_u_10m (`10u`) 10 metre U wind component	m s-1	init_time × lead_time × latitude × longitude

wind_v_100m (`100v`) 100 metre V wind component	m s-1	init_time × lead_time × latitude × longitude

wind_v_10m (`10v`) 10 metre V wind component	m s-1	init_time × lead_time × latitude × longitude

Don't see what you're looking for? Let us know at [email protected].

Details

License

Dataset licensed under CC BY 4.0.

Attribution and citation

NOAA NWS NCEP GFS data processed by dynamical.org from NOAA Open Data Dissemination archives.

Or NOAA GFS from dynamical.org.

Source

The source grib files this archive is constructed from are provided by NOAA Open Data Dissemination (NODD) and accessed from the AWS Open Data Registry. Operational data is additionally accessed from NOAA NOMADS.

Storage

Storage for this dataset is generously provided by Source Cooperative, a Radiant Earth initiative. Icechunk storage generously provided by AWS Open Data.

Compression

The data values in this dataset have been rounded in their binary floating point representation to improve compression. See Klöwer et al. 2021 for more information on this approach. The exact number of rounded bits can be found in our reformatting code.