Catalog > NOAA GFS > NOAA GFS forecast

NOAA GFS forecast

coming soon
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

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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.

This dataset is designed to be used in conjunction with the NOAA GFS analysis, hourly dataset.

Dimensions

min max units
init_time 2021-05-01T00:00:00 Present seconds since 1970-01-01
latitude -90 90 degrees_north
lead_time 0 days 00:00:00 16 days 00:00:00 seconds
longitude -180 179.75 degrees_east

Variables

units dimensions
categorical_freezing_rain_surface 0=no; 1=yes init_time × lead_time × latitude × longitude
categorical_ice_pellets_surface 0=no; 1=yes init_time × lead_time × latitude × longitude
categorical_rain_surface 0=no; 1=yes init_time × lead_time × latitude × longitude
categorical_snow_surface 0=no; 1=yes init_time × lead_time × latitude × longitude
downward_long_wave_radiation_flux_surface W/(m^2) init_time × lead_time × latitude × longitude
downward_short_wave_radiation_flux_surface W/(m^2) init_time × lead_time × latitude × longitude
expected_forecast_length seconds init_time
geopotential_height_cloud_ceiling gpm init_time × lead_time × latitude × longitude
ingested_forecast_length seconds init_time
maximum_temperature_2m C init_time × lead_time × latitude × longitude
minimum_temperature_2m C init_time × lead_time × latitude × longitude
percent_frozen_precipitation_surface % init_time × lead_time × latitude × longitude
precipitable_water_atmosphere kg/(m^2) init_time × lead_time × latitude × longitude
precipitation_surface mm/s init_time × lead_time × latitude × longitude
pressure_reduced_to_mean_sea_level Pa init_time × lead_time × latitude × longitude
pressure_surface Pa init_time × lead_time × latitude × longitude
relative_humidity_2m % init_time × lead_time × latitude × longitude
temperature_2m C init_time × lead_time × latitude × longitude
total_cloud_cover_atmosphere % init_time × lead_time × latitude × longitude
valid_time seconds since 1970-01-01 init_time × lead_time
wind_u_100m m/s init_time × lead_time × latitude × longitude
wind_u_10m m/s init_time × lead_time × latitude × longitude
wind_v_100m m/s init_time × lead_time × latitude × longitude
wind_v_10m m/s init_time × lead_time × latitude × longitude

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

Examples

Open notebook in github
Open notebook in colab
dynamical.org - NOAA GFS forecast
Maximum temperature in a forecast
import xarray as xr  # xarray>=2025.1.2 and zarr>=3.0.4 for zarr v3 support

ds = xr.open_zarr("https://data.dynamical.org/noaa/gfs/forecast/[email protected]")
ds['temperature_2m'].sel(init_time="2025-01-01T00", latitude=0, longitude=0).max().compute()

Details

Storage

Storage for this dataset is generously provided by Source Cooperative, a Radiant Earth initiative.

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.

introducing a new pod; GFS forecasts incoming