EPT-2 Technical Report
This addresses weather forecasting for energy and operational applications, representing a strong incremental advance.
The paper tackles Earth system forecasting by introducing EPT-2, an AI model that improves over its predecessor and sets a new state of the art in predicting variables like wind speed and temperature, outperforming leading models such as Microsoft Aurora and ECMWF IFS HRES, with EPT-2e surpassing ECMWF ENS at lower computational cost.
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.