{"ID":2895268,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.09703","arxiv_id":"2507.09703","title":"EPT-2 Technical Report","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 the app.jua.ai platform.","short_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...","url_abs":"https://arxiv.org/abs/2507.09703","url_pdf":"https://arxiv.org/pdf/2507.09703v1","authors":"[\"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\"]","published":"2025-07-13T16:32:13Z","proceeding":"cs.LG","tasks":"[\"cs.LG\",\"cs.AI\"]","methods":"[\"Transformer\"]","has_code":false}