A Deep Learning Method Integrating Meteorological Data for Heavy Precipitation Nowcasting in the Alps Region

Forecasting short-term heavy precipitation is crucial for the early warning of disasters such as flash floods, landslides, and urban flooding. However, under complex topographic conditions, traditional numerical forecasts still fall short in capturing high-resolution heavy precipitation events, and conventional radar extrapolation methods struggle to accurately characterize the nonlinear evolution of weather systems during advection, deformation, and intensity adjustment processes. To address the challenge of short-term heavy rainfall forecasting in high-altitude, complex terrain, this paper proposes Nowcast with Flow-Net (Nwf-Net), a short-term precipitation forecasting framework that integrates deep learning with multi-source meteorological data. This framework consists of a Morphological Evolution Track Module (MET) and a Rainfall Intensity Correction Module (RIC) connected in series: the former combines upper-air wind fields with traditional optical flow algorithms to jointly characterize the displacement of and morphological changes in radar echoes; the latter utilizes a deep recurrent neural network to correct the intensity of forecast results, thereby enhancing the model’s ability to characterize the evolution of strong convective echoes. Experiments in the Alpine region demonstrate that Nwf-Net achieves CSI, HSS, and F1 scores of 0.392, 0.506, and 0.546, respectively, at 32 dBz. These results outperform those of traditional numerical models and some mainstream models, indicating that Nwf-Net can accurately capture multiscale severe convective information and consistently generate precise forecasts.