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.
Cite
@article{arxiv.2507.09703,
title = {EPT-2 Technical Report},
author = {Roberto Molinaro and Niall Siegenheim and Niels Poulsen and Jordan Dane Daubinet and Henry Martin and Mark Frey and Kevin Thiart and Alexander Jakob Dautel and Andreas Schlueter and Alex Grigoryev and Bogdan Danciu and Nikoo Ekhtiari and Bas Steunebrink and Leonie Wagner and Marvin Vincent Gabler},
journal= {arXiv preprint arXiv:2507.09703},
year = {2025}
}