The SRW v3 container in the Hands-On container tutorial has been
renamed to ubuntu22.04-intel-srw-release-public-v3.0.0-rt.img. It can be accessed via the public repository here.

Unifying Innovations in Forecasting Capabilities Workshop 2026
Join us this July at the College Park Marriott Hotel & Conference Center in College Park, Maryland or online for the Unifying Innovations in Forecasting Capabilities Workshop 2026 (UIFCW26).
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Who We Are

The Earth Prediction Innovation Center (EPIC) is a collaboration within the Weather Enterprise – government, industry, and academia — to improve weather forecasting systems, and empower the UFS. It is being spearheaded by the National Oceanic and Atmospheric Administration (NOAA) along with its contracting partner, Raytheon.

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Find out more about our EPIC impact on the Earth Systems modeling community. Whether you’re a seasoned academic, government researcher, industry innovator, or new to coding, EPIC has something for you, too.

 

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The Relationship Between the Earth Prediction Innovation Center (EPIC) and the United Forecast System (UFS).

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Empowering Success Through Technical Support and Resources

The UFS is composed of several applications, each utilizing various components. Learn about the various applications and which components they utilize.

The NWS forecasters are primary stakeholders of the UFS’ products, and therefore, continued engagement with them is critical. Read the latest finalized list of forecasters’ requests.

News and Events

July 10, 2026

The Earth Prediction Innovation Center (EPIC) and the Unified Forecast System (UFS) community are proud to announce the public release of the UFS-Data Assimilation (DA) Diagnostics Toolkit v1.0, a new Python-based diagnostics framework for comprehensive analysis of UFS / Joint Effort for Data assimilation Integration (JEDI) DA experiments. The toolkit has been incorporated into the Consortium for Advanced Data Assimilation Research and Education (CADRE) 2026 Data Assimilation Training and Science Workshop, providing participants with a unified environment for evaluating analysis increments, observation-space behavior, error consistency, and scale-dependent characteristics of hybrid 3D-Var experiments. 

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