NOAA GFS analysis

Name: NOAA GFS analysis
Creator: dynamical.org
License: https://creativecommons.org/licenses/by/4.0/

time-optimized

Spatial domain	Global
Spatial resolution	0.25 degrees (~20km)
Time domain	2021-05-01 00:00:00 UTC to Present
Time resolution	1 hour

STAC (browse) · validation report

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 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.

Related Datasets

Examples

	Quickstart (Github)
	Quickstart (Colab)

dynamical.org - NOAA GFS analysis

Temperature at a specific place and time

import dynamical_catalog  # dynamical-catalog>=0.7.0

ds = dynamical_catalog.open("noaa-gfs-analysis")
ds["temperature_2m"].sel(time="2026-01-01T00", latitude=0, longitude=0).compute()

Dimensions

	min	max	units
latitude	-90	90	degree_north

longitude	-180	179.75	degree_east

time	2021-05-01T00:00:00Z	Present	seconds since 1970-01-01

Variables

Access a variable by its name (the bold identifier, e.g. ds["categorical_freezing_rain_surface"]).

Dimensions: time × latitude × longitude

variable	units
categorical_freezing_rain_surface Categorical freezing rain (`cfrzr`)Presence/absence over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z). 0=no; 1=yes.	1
categorical_ice_pellets_surface Categorical ice pellets (`cicep`)Presence/absence over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z). 0=no; 1=yes.	1
categorical_rain_surface Categorical rain (`crain`)Presence/absence over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z). 0=no; 1=yes.	1
categorical_snow_surface Categorical snow (`csnow`)Presence/absence over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z). 0=no; 1=yes.	1
downward_long_wave_radiation_flux_surface Surface downward long-wave radiation flux (`sdlwrf`)Average over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z).	W m-2
downward_short_wave_radiation_flux_surface Surface downward short-wave radiation flux (`sdswrf`)Average over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z).	W m-2
geopotential_height_cloud_ceiling Geopotential height (`gh`)	m
maximum_temperature_2m Maximum temperature (`tmax`)Maximum over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z).	degree_Celsius
minimum_temperature_2m Minimum temperature (`tmin`)Minimum over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z).	degree_Celsius
percent_frozen_precipitation_surface Percent frozen precipitation (`cpofp`)	percent
precipitable_water_atmosphere Precipitable water (`pwat`)	kg m-2
precipitation_surface Precipitation rate (`prate`)Average precipitation rate since the previous forecast step. Units equivalent to mm/s.	kg m-2 s-1
pressure_80m 80 metre pressure (`80sp`)	Pa
pressure_reduced_to_mean_sea_level Pressure reduced to MSL (`prmsl`)	Pa
pressure_surface Surface pressure (`sp`)	Pa
relative_humidity_2m 2 metre relative humidity (`2r`)	percent
temperature_2m 2 metre temperature (`2t`)	degree_Celsius
temperature_80m 80 metre temperature (`80t`)	degree_Celsius
total_cloud_cover_atmosphere Total cloud cover (`tcc`)Average over the previous 1-6 hours, reset every 6-hour forecast step (00Z, 06Z, 12Z, 18Z).	percent
wind_u_100m 100 metre U wind component (`100u`)	m s-1
wind_u_10m 10 metre U wind component (`10u`)	m s-1
wind_u_80m 80 metre U wind component (`80u`)	m s-1
wind_v_100m 100 metre V wind component (`100v`)	m s-1
wind_v_10m 10 metre V wind component (`10v`)	m s-1
wind_v_80m 80 metre V wind component (`80v`)	m s-1

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.

Construction

GFS starts a new model run every 6 hours and dynamical.org has created this analysis by concatenating the first 6 hours of each forecast along the time dimension.

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.

Chunks & shards

This dataset is stored in Zarr format, which splits each variable into a grid of chunks — the smallest unit read from storage. Chunks are grouped into larger shards (the objects actually written to storage), which keeps the object count manageable for long-archive datasets. When possible, aligning your reads with this dataset's chunk grid can significantly improve data access speed.

The element count and coordinate span of this dataset:

dimension	chunk	shard
time	1440 (60 days)	1440 (60 days)
latitude	50 (12.5°)	400 (100°)
longitude	50 (12.5°)	400 (100°)
uncompressed	13.7 MiB	878.9 MiB

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.

Related dataset

NOAA GEFS analysis provides a much longer historical record.