A Hybrid Model For Short-Term Rainstorm Forecasting Based On A Back-Propagation Neural Network And Synoptic Diagnosis

Rainstorms are one of the most important types of natural disaster in China. In order to enhance the ability to forecast rainstorms in the short term, this paper explores how to combine a back-propagation neural network (BPNN) with synoptic diagnosis for predicting rainstorms, and analyzes the hit rates of rainstorms for the above two methods using the county of Tianquan as a case study. Results showed that the traditional synoptic diagnosis method still has an important referential meaning for most rainstorm types through synoptic typing and statistics of physical quantities based on historical cases, and the threat score (TS) of rainstorms was more than 0.75. However, the accuracy for two rainstorm types influenced by low-level easterly inverted troughs was less than 40%. The BPNN method efficiently forecasted these two rainstorm types; the TS and equitable threat score (ETS) of rainstorms were 0.80 and 0.79, respectively. The TS and ETS of the hybrid model that combined the BPNN and synoptic diagnosis methods exceeded the forecast score of multi-numerical simulations over the Sichuan Basin without exception. This kind of hybrid model enhanced the forecasting accuracy of rainstorms. The findings of this study provide certain reference value for the future development of refined forecast models with local features.