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 long-range 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

In recent years, Artificial Intelligence Weather Prediction (AIWP) models have achieved performance comparable to, or even surpassing, traditional Numerical Weather Prediction (NWP) models by leveraging reanalysis data. However, a less-explored approach involves training AIWP models directly on observational data, enhancing computational efficiency and improving forecast accuracy by reducing the uncertainties introduced through data assimilation processes. In this study, we propose OMG-HD, a novel AI-based regional high-resolution weather forecasting model designed to make predictions directly from observational data sources, including surface stations, radar, and satellite, thereby removing the need for operational data assimilation. Our evaluation shows that OMG-HD outperforms both the European Centre for Medium-Range Weather Forecasts (ECMWF)'s high-resolution operational forecasting system, IFS-HRES, and the High-Resolution Rapid Refresh (HRRR) model at lead times of up to 12 hours across the contiguous United States (CONUS) region. We achieve up to a 13% improvement on RMSE for 2-meter temperature, 17% on 10-meter wind speed, 48% on 2-meter specific humidity, and 32% on surface pressure compared to HRRR. Our method shows that it is possible to use AI-driven approaches for rapid weather predictions without relying on NWP-derived weather fields as model input. This is a promising step towards using observational data directly to make operational forecasts with AIWP models.

Accurate weather forecasting plays a vital role in saving lives, aiding emergency management, and reducing the economic impact of severe weather events [Bauer et al., 2015]. The traditional paradigm of weather forecasting is numerical weather prediction (NWP), which focuses on nonlinear partial differential equations to simulate atmospheric dynamics and physical processes [Benjamin et al., 2019]. In recent years, with the advancement of artificial intelligence (AI) technology and the continuous accumulation of massive weather data, data-driven methods have been increasingly incorporated into various stages and different scales of weather forecasting [Ravuri et al., 2021, Schultz et al., 2021, Weyn et al., 2021]. Particularly in the past two years, numerous data-driven models addressing the short to mediumrange (0-10 day) forecasting problem have emerged [Bi et al., 2023, Lam et al., 2023, Chen et al., 2023, Lang et al., 2024]. These models have surpassed the operational Integrated Forecast System (IFS) from European Centre for Medium-Range Weather Forecasts (ECMWF) in metrics such as Root Mean Square Error (RMSE) and Anomaly Correlation Coefficient (ACC). These breakthroughs have instilled confidence that data-driven models can be significant tools for enhancing the accuracy and computational efficiency of weather forecasting.