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EPT-2 Technical Report

Roberto Molinaro
Niall Siegenheim
Niels Poulsen
Jordan Dane Daubinet
Henry Martin
Mark Frey
Kevin Thiart
Alexander Jakob Dautel
Andreas Schlueter
Alex Grigoryev
Bogdan Danciu
Nikoo Ekhtiari
Bas Steunebrink
Leonie Wagner
Marvin Vincent Gabler
Main:5 Pages
15 Figures
Bibliography:2 Pages
Appendix:2 Pages
Abstract

We present EPT-2, the latest iteration in our Earth Physics Transformer (EPT) family of foundation AI models for Earth system forecasting. EPT-2 delivers substantial improvements over its predecessor, EPT-1.5, and sets a new state of the art in predicting energy-relevant variables-including 10m and 100m wind speed, 2m temperature, and surface solar radiation-across the full 0-240h forecast horizon. It consistently outperforms leading AI weather models such as Microsoft Aurora, as well as the operational numerical forecast system IFS HRES from the European Centre for Medium-Range Weather Forecasts (ECMWF). In parallel, we introduce a perturbation-based ensemble model of EPT-2 for probabilistic forecasting, called EPT-2e. Remarkably, EPT-2e significantly surpasses the ECMWF ENS mean-long considered the gold standard for medium- to longrange forecasting-while operating at a fraction of the computational cost. EPT models, as well as third-party forecasts, are accessible via thethis http URLplatform.

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