An early look at NOAA’s Project EAGLE to accelerate AI weather prediction advances for the United States
Today we are sharing some early progress on Project EAGLE (Experimental AI Global and Limited-area Ensemble forecast system), which is a joint effort between NOAA Research Laboratories and the Earth Prediction Innovation Center (EPIC) in the Office of Oceanic and Atmospheric Research (OAR), and the National Weather Service (NWS).
Over the past couple of years NOAA researchers have embraced global and regional Artificial Intelligence (AI) based models (Frolov et al., 2024). In a short time, NOAA has delivered a reforecast database featuring community-developed AI models initialized with operational Global Forecast System data (Radford et al., 2025); an AI model trained on analysis from NOAA’s global data assimilation system (Tabas et al., 2025); a high-resolution model for Central U.S. severe weather (Flora and Potvin, 2025); and the first attempt to decouple AI forecasts from operational initial conditions (Slivinski et al., 2025). EAGLE is the next phase of this endeavor.
WoFSCast: A GraphCast-based emulator for the Warn-on-Forecast System
Authors: Corey Potvin, Montgomery Flora, Adam Clark, Patrick Burke, Lou Wicker AI Numerical Weather Weather Prediction (NWP) emulators like Google DeepMind’s GraphCast could revolutionize weather prediction by providing more skillful forecasts at much less computational expense. While these emulators work well for global scales since decades of reanalyses at these scales are available for training, finer […]
Introducing GEFSv13 Replay Dataset Designed for Training of AI Models for Coupled Prediction
Authors: Sergey Frolov The NOAA Unified Forecast System (UFS) / Global Ensemble Forecast System version 13 (GEFSv13) replay data set was developed to provide initial conditions for the retrospective forecast archive in support of the next implementation of the NOAA medium range forecast system (GEFSv13 / GFSv17). Increasingly, this dataset is being used for training of the coupled […]
