摘要
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.
暴雨是我国最重要的自然灾害之一.大量的研究表明,暴雨的频率和强度在全球变暖的背景下正在逐年增强.但是如何成功的预测短期暴雨,特别是发生在复杂地形下的暴雨,仍然是一个巨大的挑战.本项研究采用BP神经网络和天气学诊断相结合的方法,探索了一种四川盆地西部复杂地形下的暴雨预报模型.该模型有效改善了喇叭口地形下,受低层偏东风影响的暴雨预报准确性.机器学习与天气学理论的结合,提升了模型的物理基础和预测成功率,同时该方法也为发展具有本地特征的暴雨预报客观工具,提供了一定的参考价值.
基金
supported by the National Key Research and Development Program on Monitoring,Early Warning and Prevention of Major Natural Disasters [grant number 2018YFC1506006]
the National Natural Science Foundation of China [grant numbers 41805054 and U20A2097]。
