OMG-HD: A High-Resolution AI Weather Model for End-to-End Forecasts from Observations

Recently, artificial intelligence (AI) models have upended numerical weather prediction (NWP) by achieving performance comparable to or even surpassing that of physics-based NWP models while also significantly reducing computational costs. However, these AI solutions generally operate with initial conditions produced by NWP data assimilation, which remains costly and can suffer from approximations. We introduce OMG-HD, an end-to-end AI weather forecasting model designed to make predictions directly from observational data, including surface observations, radar, and satellite, thus bypassing the data assimilation step. OMG-HD provides kilometer-scale, 12-hour forecasts across the contiguous United States (CONUS) that exhibit greater skill than the leading operational NWP models. Compared to the High-Resolution Rapid Refresh (HRRR), we achieve a 13-48% improvement in RMSE for 2-meter temperature, 10-meter wind speed, 2-meter specific humidity, and surface pressure. These results demonstrate the feasibility of AI-driven end-to-end approaches for operational weather forecasting free of NWP data, offering a promising step towards faster and more accurate weather forecasts to support weather-dependent decision-making.