Coupled Multi-Resolution Generative Modelling for High-Resolution Hourly Regional Weather Forecasting

Despite AI-driven weather forecasting has made rapid progress, this progress has primarily focused on global models, which require processing planetary-scale data on low-resolution grids. To address this gap, given the need for high-resolution forecasts for specific regions in many research and applications, we propose a computationally efficient generative framework for short- to medium-term hourly regional forecasts. This framework ingests multi-resolution, multi-source geophysical inputs, combining 0.25° 3D atmospheric fields with 0.1° surface fields. To avoid simply stitching together heterogeneous grids, we design a coupled architecture to enable interaction between the evolving 3D atmospheric state and high-resolution surface and precipitation-related signals. The training process uses ERA5 data, satellite-derived products, and radar precipitation observations. We describe the end-to-end modeling pipeline and evaluation protocol and discuss uncertainty-aware regional forecasts achieved through generative methods.