HRRRCast: Hourly, High-resolution Ensemble Forecasts Join Project EAGLE
Artificial intelligence (AI) and machine learning (ML) are rapidly transforming numerical weather prediction (NWP), enabling faster and more scalable forecasting systems. As part of the National Oceanic and Atmospheric Administration’s (NOAA) broader AI strategy, the Earth Prediction Innovation Center (EPIC) is advancing next-generation forecasting through the Experimental Artificial Intelligence Global and Limited-area Ensemble (EAGLE) Project. A key regional component of this effort is the HRRRCast ensemble system, developed by NOAA’s Office of Oceanic and Atmospheric Research (OAR) Global Systems Laboratory (GSL). HRRRCast ensemble was trained with four years of HRRR analysis data. HRRRCast ensemble This ensemble system now provides, in a computationally efficient way, information on forecast uncertainties at convection allowing scales.
Nested-EAGLE Application v1.0.0 Release
The Earth Prediction Innovation Center (EPIC) and the NOAA Artificial Intelligence for Numerical Weather Prediction (AI4NWP) Working Group are proud to announce the public release of the nested-Experimental AI Global and Limited-area Ensemble forecast system (nested-EAGLE) v1.0.0, which complements the Environmental Modeling Center’s (EMC) GraphCast-based release of global-EAGLE-solo and global-EAGLE ensemble forecast systems (which are now operational).
AMS AI Presentations
NOAA offered several Artificial Intelligence (AI) focused presentations, which highlighted a growing ecosystem designed to support the development, verification, and operational transition of machine learning–based weather prediction models. Together, the key presentations below demonstrate frameworks such as Anemoi, wxvx, and NOAA’s Project Experimental AI Global and Limited-area Ensemble forecast system (EAGLE). The presentations highlight scalable infrastructure, flexible verification tools, and research-to-operations pipelines enabling the community to build, test, and deploy next-generation forecasting systems with greater efficiency and reliability.
AMS Short Course Outcomes
On January 25, 2026, the National Oceanic and Atmospheric Administration (NOAA)’s Earth Prediction Innovation Center (EPIC) hosted a hands-on Artificial Intelligence (AI) Short Course during the 106th American Meteorological Society (AMS) Annual Meeting at the George R. Brown Convention Center in Houston, Texas. This hybrid workshop brought together students, researchers, NOAA scientists, private sector professionals, and international collaborators to explore scalable AI weather prediction frameworks, operational transition pathways, and verification tools supporting next-generation forecast systems. The short course emphasized practical implementation, enabling participants to run AI models using community notebooks and open-source tools that are actively shaping NOAA’s operational AI strategy.
Release of the NOAA-NASA Observational Archive for Reanalysis in an AI-friendly Format
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.
NOAA Research Develops an AI-Powered Sibling to its Flagship Weather Model
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.
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 […]
