Objective Warning Signal Generation Method for Thunderstorm Gale in Jiangsu and Its Application for the 2023 Rainy Season
FENG Yuxuan
ZHUANG Xiaoran
KANG Zhiming
ZENG Kang
WU Haiying
LI Te
Abstract：To achieve the automatic generation of objective warning signals for thunderstorm gales in Jiangsu and to enhance nowcasting capabilities,a minute-scale and kilometer-scale wind field gridded dataset was established.This dataset distinguishes between different types of wind.By integrating a generative adversarial network for thunderstorm gale modeling and a PhyDNet for the modeling of wind associated with weather systems and mixed-type wind,we developed a deep-learning-based 0-2 hour nowcasting model(Blending)for thunderstorm gales in Jiangsu.Then,we compared the subjective and objective warning signals generated by the PhyDNet_ALL(which uses PhyDNet modeling without distinguishing wind types)and Blending for the 2023 rainy season.The results show that:(1)Compared to subjective warning signals,objective warning signals generated by deep learning methods effectively improved the lead time of warning signals.(2)Deep learning methods can predict thunderstorm gales and their evolution process in advance.(3)Blending,which models convective gales separately,ensures the maintenance of convection intensity and small-scale features,allowing it to better describe the evolution characteristics of extreme convective gales and significantly outperform PhyDNet_ALL in terms of improving the lead time of orange and red alerts.
Keywords：thunderstorm galenowcastingdeep learningwarning signal
Publication Date：2024-12-28
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：12( 954-965 )