An Intelligent and Targeted Forecasting Technology for Severe Convective Rainfall Systems in Shanghai

Severe convective weather systems, characterized by their small spatial scale and rapid, violent development, frequently give rise to disasters such as rainstorms, lightning, gales, and hail. Accurate forecast of such systems has long been a challenging issue of weather forecasting and a dilemma for disaster prevention and mitigation in Shanghai. This research introduces an intelligent forecasting technology for severe convective rainfall systems in Shanghai, encompassing adaptive radar observation of strong convection targets, identification and prediction of typical convective features, machine learning-based correction of numerical prediction errors, and system integration.

In this technology, to address the problem of unbalanced samples with a scarcity of heavy rainfall cases, an autoencoder for noise reduction and ordinal boosting regression module is designed. A FocalLoss method is employed to weight the Loss function, thereby transforming the regression task of precipitation values into multiple classification tasks. An adaptive scale selection method is constructed to better represent the heavy rainfall system. In this method, the spatio-temporal scales of convective systems are adaptively selected, enabling targeted correction of heavy rainfall prediction.

Finally, an intelligent monitoring and early warning system capable of predicting the three-dimensional structure and evolution of strong convective systems has been established and put into operation. This system was evaluated during the flood seasons of 2022-2023. The results indicate that the TS score for 24-hour heavy rainfall (50mm) was significantly enhanced compared to the operational numerical weather prediction system of Shanghai Meteorological Service (SMS). This technology has been extended to application in urban disaster prevention and mitigation.