Physics-Guided Generative Nowcasting of Extreme Precipitation with Dual-Polarization Radar and Neural ODE-Diffusion Framework

Extreme precipitation poses significant risks to society and infrastructure, highlighting the urgent need for accurate short-term nowcasting. While deep learning models have shown promise in precipitation forecasting, they often lack integration with physical principles, leading to inconsistencies and limited skill in predicting convective evolution. In this study, we introduce RainCast—a novel generative nowcasting framework that synergistically combines deterministic physical modeling with stochastic generative networks to improve the accuracy and physical consistency of extreme rainfall forecasts.

RainCast integrates a deterministic branch based on Neural Ordinary Differential Equations (Neural ODE) to simulate large-scale advective processes and a generative branch built upon a conditional diffusion model to capture fine-scale stochastic variability. The model is guided by key physical features such as flow fields, vorticity, and divergence derived from dual-polarization radar observations, which provide essential dynamical information about convective systems. We train and evaluate the framework using vertically integrated liquid water (VIL) data from dual-polarization radars in China (GD-SPOL) and North America (SEVIR).

Quantitative assessments demonstrate that RainCast significantly outperforms existing nowcasting methods such as SimVP, SwinLSTM, and NowcastNet. On the GD-SPOL dataset, RainCast improves the Critical Success Index (CSI) for intense convection (VIL ≥ 160) by up to 14.1% at 90-minute lead times. Structural similarity metrics also show substantial gains, with reductions in Fréchet Video Distance (FVD) by 25.4% and Learned Perceptual Image Patch Similarity (LPIPS) by 44.6%. Case studies further illustrate RainCast’s ability to realistically simulate the evolution of organized convective systems, including squall lines and multicell storms, while maintaining physical coherence in wind field retrievals.

Our results underscore the value of embedding physical guidance into generative deep learning architectures for convective nowcasting. The RainCast framework represents a meaningful step toward more reliable, interpretable, and physically consistent nowcasting of extreme precipitation, with potential applications in operational meteorology and disaster preparedness.