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

作　　者：冯宇轩 庄潇然 康志明 曾康 吴海英[1] 李特 FENG Yuxuan;ZHUANG Xiaoran;KANG Zhiming;ZENG Kang;WU Haiying;LI Te(Jiangsu Meteorological Observatory,Nanjing 210041,China;Institute of Desert Meteorology,China Meteorological Administration,Urumqi 830002,China)

机构地区：[1]江苏省气象台,江苏南京210041 [2]中国气象局乌鲁木齐沙漠气象研究所,新疆乌鲁木齐830002

出　　处：《热带气象学报》2024年第6期954-965,共12页Journal of Tropical Meteorology

基　　金：全国暴雨研究开放基金(BYKJ2024Q23);中国气象局创新发展专项(CXFZ2023J008);中国气象局能力提升联合研究专项(22NLTSZ001、24NLTSQ015);中国气象局揭榜挂帅项目(CMAJBGS202212,CMAJBGS202211);中国气象局重点创新团队(CMA2022ZD04);中国气象局复盘总结专项(FPZJ2023-046)共同资助;江苏省气象局重点项目(KZ202201);江苏省气象局青年基金(KQ202305、KQ202301);国家自然科学基金(42105006、42105008);北极阁基金(BJG202304)。

摘　　要：为了实现江苏地区雷暴大风智能预警信号的自动生成以提升雷暴大风临近预报预警能力,在建立分钟级、公里级网格风场数据集的基础上,区分雷暴大风、系统性大风和混合性大风过程,并结合生成对抗网络(Generative Adversarial Network,GAN,针对雷暴大风建模)和具有物理约束架构的时空卷积神经网络(Physical Dynamics Network,PhyDNet,针对系统性大风和混合性大风建模),发展了基于深度学习的江苏地区0~2 h雷暴大风临近预报模型(Blending),实现面向区县的客观、分级预警信号自动生成技术,并对比探究了Blending和PhyDNet_ALL(不区分大风类型,直接采用PhyDNet建模)两种深度学习方法分别生成的客观预警信号与主观预警信号在2023年汛期的表现。结果表明:(1)相较于主观预警信号,由深度学习方法生成的客观预警信号能有效提升预警信号提前量;(2)深度学习方法可以提前预报出强对流大风天气及其演变过程;(3)由于Blending针对雷暴大风单独建模,确保对流系统强度以及中小尺度特征的维持,因此Blending可以更好地描述极端雷暴大风的演变特征,在橙色和红色预警信号提前量上显著高于PhyDNet_ALL。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 

分 类 号：P435[天文地球—大气科学及气象学]