Multi-scale Physics-informed Transformer With Spatio-temporal Feature Adapter For Extreme Precipitation Nowcasting

Extreme precipitation, as a core causative factor of meteorological disasters, poses significant challenges for accurate short-term forecasting due to the chaotic nature of precipitation systems and their multi-scale spatio-temporal evolution. Traditional numerical models are notably affected by error accumulation, while existing deep learning models still face dual limitations in physical fidelity and multi-scale feature extraction. To Address these issues, we propose an innovative Multi-scale Physics-informed Transformer with spatio-temporal feature adapter for extreme precipitation nowcasting, termed MPFormer. Our framework comprises two core components: the deterministic Evolution Network and the stochastic Generative Network. The Evolution Network integrates a novel Scale-Aware Temporal Residual Modulation Transformer (STRMT) encoder that captures multi-scale storm dynamics through residual temporal attention. The Generative Network introduces spatio-temporal adapters as lightweight transfer modules for probabilistic modeling. We develop a Multi-scale Physics-informed Loss with three innovations: 1) dynamic weight scheduling for feature fusion, 2) physical constraints preserving storm evolution patterns, and 3) entropy-based uncertainty calibration. Experiments based on MRMS radar data from North America demonstrate that the model can generate high-resolution forecasts (2km grid) with a 3-hour lead time over an area of 2048×2048 square kilometers. Compared to the state-of-the-art technologies, the proposed framework shows significant effectiveness and superiority in metrics such as CSIN, offering a new paradigm that combines physical interpretability with engineering practicality for extreme weather warnings and disaster prevention in smart cities.