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Welcome
Welcome to the Get Code page for Project EAGLE (Experimental AI Global and Limited-area Ensemble forecast system), specifically for the nested-EAGLE application.
Nested-EAGLE 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). Project EAGLE provides NOAA, the weather enterprise (including government, industry, and academia), and broader community members with the ability to rapidly test, develop, and demonstrate Artificial Intelligence (AI) models for global and limited-area ensemble forecasting.
Description
This page provides information on how to access the nested-EAGLE code base, to set up a nested-EAGLE environment, to run the application, and to find documentation and supporting resources.
For any publications based on work using nested-EAGLE, please include the following citation:
The Earth Prediction Innovation Center (EPIC) and the NOAA AI for Numerical Weather Prediction (AI4NWP) Working Group. (2026). The nested-EAGLE (Experimental AI Global and Limited-area Ensemble forecast system) (v1.0.0). Zenodo. https://doi.org/10.5281/zenodo.19672026
Getting Started
This initial release of nested-EAGLE targets NOAA employees and affiliates and is supported on NOAA Research and Development High Performance Computing System (RDHPCS) machine Ursa, which provides the GPU resources required for a timely execution of this current iteration of EAGLE-Machine Learning (ML) model training workflow. NOAA users satisfying the following two prerequisites will find the nested-EAGLE pipeline preconfigured and ready to run; see note below for other users:
- Access to NOAA’s RDHPCS machine Ursa, or similar HPC system
- Access to a project with GPU node availability
Note that while the current release is supported on Ursa, advanced users may replicate nested-EAGLE on other HPC systems. Subsequent releases will broaden the support of the nested-EAGLE application on the native Microsoft Azure cloud and other suitable HPC systems.
Once these prerequisites are met, users can navigate to the nested-EAGLE’s User’s Guide documentation page and follow the links to the Quick Start Guide. The guide provides step-by-step instructions for setting up a nested-EAGLE environment on Ursa and running the nested-EAGLE pipeline.
Documentation & User Support
If you encounter a problem using the nested-EAGLE pipeline that appears to be a bug, please report it through GitHub Issues. For idea sharing, tips, and discussion, please use GitHub Discussions. For general questions regarding trouble shooting,please use GitHub Discussion Q&A. These discussions and Q&A topics may eventually populate EPIC’s Technical FAQ board for AI modeling at NOAA. For a global-EAGLE application tutorial, see the EAGLE-based Tutorial which uses a Google colab notebook to train and run an AI model. The nested-EAGLE v1.0.0 User’s Guide provides the most comprehensive information about the application, including links to more detailed technical documentation for:
- preprocessing
- training
- forecasting
- postprocessing
- verification
- visualization
Preprocessing is supported by the NOAA Physical Sciences Laboratory (PSL)’s ufs2arco for data ingestion, reformatting, and creation. Training and forecasting are computed with the European Centre for Medium-Range Weather Forecast (ECMWF) and partner agencies’ Anemoi-training and Anemoi-inference. Forecast output postprocessing for verification and visualization (V&V) is accomplished via PSL’s eagle-tools. Verification statistics are computed via NOAA Global Systems Laboratory (GSL)’s wxvx package.
Releases
The latest release of the nested-EAGLE is v1.0.0.
Learn more about the Nested-EAGLE Application v1.0.0 Release
Release v.1.0.0
Get Started with the nested-EAGLE
Resources
Public Release Code
