NOAA GEFS analysis

updating
Spatial domain Global
Spatial resolution 0.25 degrees (~20km)
Time domain 2000-01-01 00:00:00 UTC to Present
Time resolution 3.0 hours

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The Global Ensemble Forecast System (GEFS) is a National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) weather forecast model.

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.

This dataset is designed to be used in conjunction with the GEFS forecast 35 day dataset.

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

Dimensions

min max units
latitude -90 90 degrees_north
longitude -180 179.75 degrees_east
time 2000-01-01T00:00:00 Present seconds since 1970-01-01

Variables

units dimensions
categorical_freezing_rain_surface

Categorical freezing rain

 0=no; 1=yes time × latitude × longitude
categorical_ice_pellets_surface

Categorical ice pellets

 0=no; 1=yes time × latitude × longitude
categorical_rain_surface

Categorical rain

 0=no; 1=yes time × latitude × longitude
categorical_snow_surface

Categorical snow

 0=no; 1=yes time × latitude × longitude
downward_long_wave_radiation_flux_surface

Surface downward long-wave radiation flux

Average value in the last 6 hour period (00, 06, 12, 18 UTC) or 3 hour period (03, 09, 15, 21 UTC).

 W/(m^2) time × latitude × longitude
downward_short_wave_radiation_flux_surface

Surface downward short-wave radiation flux

Average value in the last 6 hour period (00, 06, 12, 18 UTC) or 3 hour period (03, 09, 15, 21 UTC).

 W/(m^2) time × latitude × longitude
geopotential_height_cloud_ceiling

Geopotential height

 gpm time × latitude × longitude
maximum_temperature_2m

Maximum temperature

 C time × latitude × longitude
minimum_temperature_2m

Minimum temperature

 C time × latitude × longitude
percent_frozen_precipitation_surface

Percent frozen precipitation

Contains the value -50 when there is no precipitation.

 % time × latitude × longitude
precipitable_water_atmosphere

Precipitable water

 kg/(m^2) time × latitude × longitude
precipitation_surface

Total Precipitation

Average precipitation rate since the previous forecast step.

 mm/s time × latitude × longitude
pressure_reduced_to_mean_sea_level

Pressure reduced to MSL

 Pa time × latitude × longitude
pressure_surface

Surface pressure

 Pa time × latitude × longitude
relative_humidity_2m

2 metre relative humidity

 % time × latitude × longitude
temperature_2m

2 metre temperature

 C time × latitude × longitude
total_cloud_cover_atmosphere

Total Cloud Cover

Average value in the last 6 hour period (00, 06, 12, 18 UTC) or 3 hour period (03, 09, 15, 21 UTC).

 % time × latitude × longitude
wind_u_100m

100 metre U wind component

 m/s time × latitude × longitude
wind_u_10m

10 metre U wind component

 m/s time × latitude × longitude
wind_v_100m

100 metre V wind component

 m/s time × latitude × longitude
wind_v_10m

10 metre V wind component

 m/s 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 GEFS analysis
Temperature at a specific place and time
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/gefs/analysis/[email protected]")
ds['temperature_2m'].sel(time="2025-01-01T00", latitude=0, longitude=0).compute()

Details

Sources

To provide the longest possible historical record, this dataset in constructed from three distinct GEFS forecast archives.

Variable availability

Data is available for all variables at all times with the following exceptions.

Construction

To create a single time dimension we concatenate the first few hours of each forecast. From 2000-01-01 to 2019-12-31 reforecasts are available once per day and this dataset uses the first 21 or 24 hours of each forecast. From 2020-01-01 to present forecasts are available every 6 hours and this dataset uses the first 3 or 6 hours of each forecast. Variables with an instantaneous step_type use the shortest possible lead times (e.g. 0 and 3 hours) while accumulated variables must use one additional forecast step (e.g. 3 and 6 hours) because they do not have an hour 0 forecast value.

Interpolation

For most of the time range of the archive the source data is available at 0.25-degree resolution and a 3 hourly time step and we perform no interpolation. There are two exceptions to this. 1) From 2020-01-01 to 2020-09-23 the source data has a 1.0-degree spatial resolution and a 6 hourly time step. 2) From 2020-09-23 to present the 100m wind components have a 0.5-degree spatial resolution in the source data. To provide a consistent archive in the above two cases we first perform bilinear interpolation in space to 0.25-degree resolution followed by linear interpolation in time to a 3-hourly timestep if necessary. The original, uninterpolated data can be obtained by selecting latitudes and longitudes evenly divisible by 1 and, in case 1), time steps whose hour is divisible by 6.

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.