热带气象学报2024，Vol.40Issue(6)：954-965,12.DOI:10.16032/j.issn.1004-4965.2024.084
江苏省级雷暴大风智能预警信号生成技术及其在2023年汛期的应用评估
Objective Warning Signal Generation Method for Thunderstorm Gale in Jiangsu and Its Application for the 2023 Rainy Season
摘要
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.关键词
雷暴大风/临近预报/深度学习/预警信号Key words
thunderstorm gale/nowcasting/deep learning/warning signal分类
天文与地球科学引用本文复制引用
冯宇轩,庄潇然,康志明,曾康,吴海英,李特..江苏省级雷暴大风智能预警信号生成技术及其在2023年汛期的应用评估[J].热带气象学报,2024,40(6):954-965,12.基金项目
全国暴雨研究开放基金(BYKJ2024Q23) （BYKJ2024Q23）
中国气象局创新发展专项(CXFZ2023J008) （CXFZ2023J008）
江苏省气象局重点项目(KZ202201)中国气象局能力提升联合研究专项(22NLTSZ001、24NLTSQ015) （KZ202201）
中国气象局揭榜挂帅项目(CMAJBGS202212,CMAJBGS202211) （CMAJBGS202212,CMAJBGS202211）
中国气象局重点创新团队(CMA2022ZD04) （CMA2022ZD04）
国家自然科学基金(42105006、42105008) （42105006、42105008）
江苏省气象局青年基金(KQ202305、KQ202301) （KQ202305、KQ202301）
北极阁基金(BJG202304)、中国气象局复盘总结专项(FPZJ2023-046)共同资助 （BJG202304）
