Earth Data School
About
Full catalog/NWS GRAPHCASTGFS PDS
NWS GRAPHCASTGFS PDS·dataset

Experimental AI global forecasts, EAGLE/GraphCast (NOAA)

NOAA EAGLE (Experimental AI Global and Limited-Area Ensemble) Global Deterministic and Ensemble Forecasts
atmosphere NOAA NOAA active
In plain English

What it measures. Medium-range global weather forecasts produced by an AI model on a roughly 28 km grid, run four times a day.

How it's made. An experimental NOAA system built on Google DeepMind's pre-trained GraphCast machine-learning model generates these EAGLE forecasts (now superseded operationally by newer AI models).

How & where you'd use it. It supports ongoing development of AI-based weather and longer-range seasonal forecasting; the data here is experimental and not the official operational product.

What's measured

aws-pdsagricultureclimatedisaster responseenvironmentalmeteorologicalweather

Coverage & cadence

  • Time span— → ongoing

What you can do with it

  • Map air pollutants — NO₂, aerosols, ozone
  • Track greenhouse gases and Earth's energy budget
  • Feed weather and air-quality analysis
Official description

Update Effective on December 17, 2025, the NOAA/NWS National Centers for Environmental Prediction (NCEP) implemented three new models : the Artificial Intelligence Global Forecast System (AIGFS), the Artificial Intelligence Global Ensemble Forecast System (AIGEFS), and the Hybrid Global Ensemble Forecast System (HGEFS). The AIGFS/AIGEFS are the operational replacement for EAGLE SOLO/Ensemble. The EAGLE SOLO/Ensemble forecasts hosted here are based on the GraphCastGFS described below . Please note that this bucket for EAGLE SOLO/Ensemble will continue hosting experimental forecasts to support ongoing development of EAGLE global weather, sub-seasonal to seasonal (S2S) forecast models once they are ready. The EAGLE SOLO/ensemble forecasts are generated from GraphCast Global Forecast System (GraphCastGFS). GraphCastGFS is an experimental system set up by the National Centers for Environmental Prediction (NCEP) to produce medium range global forecasts. It is built upon Google DeepMind’s pre-trained GraphCast, a Machine Learning Weather Prediction (MLWP) model. The horizontal resolution is a 0.25 degree latitude-longitude grid (about 28 km). The model runs 4 times a day at 00Z, 06Z, 12Z and 18Z cycles. Major atmospheric and surface fields including temperature, wind components, geopotential height, specific humidity, and vertical velocity, are available. The products are 6 hourly forecasts up to 16 days. The data format is GRIB2. Model upgrade information: The GraphCastGFS version 1.0 takes two model states as initial conditions (current and 6-hr previous states) from NCEP 0.25 d

Get the data

noaa_access.py
# NOAA Open Data on AWS — public S3, no login
import s3fs

fs = s3fs.S3FileSystem(anon=True)
# find this dataset's bucket in the docs link in the sidebar, then:
# files = fs.ls("noaa-<bucket>/...")
# open NetCDF/GRIB with xarray, COGs with rioxarray
NOAA Open Data is on public AWS S3 — no login at all (anonymous access).