TG-Net:A Physically Interpretable Deep Learning Forecasting Model for Thunderstorm Gusts  

作　　者：Yunqing LIU Lu YANG Mingxuan CHEN Jianwei SI Maoyu WANG Wenyuan LI Jingfeng XU 

机构地区：[1]Faculty of Information Science and Engineering,Ocean University of China,Qingdao,266100,China [2]Institute of Urban Meteorology,China Meteorological Administration,Beijing,100089,China [3]Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD),Nanjing University of Information Science&Technology,Nanjing,210044,China

出　　处：《Journal of Meteorological Research》2025年第1期59-78,共20页气象学报(英文版)

基　　金：Supported by the National Key Research and Development Program of China(2022YFC3004103);Beijing Natural Science Foundation(8222051);China Meteorological Administration Key Innovation Team(CMA2022ZD04 and CMA2022ZD07);Nanjing Joint Institute for Atmospheric Sciences Beijige Open Research Fund(BJG202407).

摘　　要：Thunderstorm gusts are a common and hazardous type of severe convective weather,characterized by a small spatial scale,short duration,and significant destructive power.They often lead to severe disasters,highlighting the critical importance of their accurate forecasting.Previous studies have explored the environmental factors and spatiotemporal distribution characteristics of thunderstorm gusts,highlighting the need for improved forecasting methods.In recent years,artificial intelligence techniques have shown promise in enhancing the accuracy of thunderstorm gust forecasting,with various machine learning algorithms and models having been developed.This paper proposes a multiscale feature fusion module called Thunderstorm Gusts Block(TG-Block)and a deep learning model named Thunderstorm Gusts net(TG-net)based on the Attention U-net and TG-TransUnet models,and employs interpretable methods such as Integrated Gradient,Deep Learning Importance Features,and Shapley Additive exPlanations to validate the model’s practical relevance and reliability.The analysis of feature importance underscores the model’s ability to capture key thermodynamic and multiscale weather characteristic information for thunderstorm gust nowcasting.It is,however,worth emphasizing that these conclusions are only based on a limited number of thunderstorm gust examples,and the evaluation results may be affected by specific weather types and sample sizes.Nonetheless,TG-net has been put into real-time operation at the Institute of Urban Meteorology,and we will continue to rigorously validate its performance and make any necessary optimizations and enhancements based on feedback to ensure the robustness and stability of the model.

关 键 词：thunderstorm gusts deep learning INTERPRETABILITY multisource data weather forecasting 

分 类 号：P44[天文地球—大气科学及气象学]