我国短时强降水研究进展  

Research Progress on Short-Duration Heavy Precipitation in China

作　　者：赵强[1,2] 郑永光[3] 井宇 冯典[1] 刘菊菊 ZHAO Qiang;ZHENG Yongguang;JING Yu;FENG Dian;LIU Juju(Shaanxi Meteorological Observatory,Xi’an 710014,China;State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing 100081,China;National Meteorological Center,Beijing 100081,China)

机构地区：[1]陕西省气象台,陕西西安710014 [2]中国气象科学研究院灾害天气国家重点实验室,北京100081 [3]国家气象中心,北京100081

出　　处：《地球科学进展》2025年第1期21-38,共18页Advances in Earth Science

基　　金：国家自然科学基金项目(编号:42175017);灾害天气国家重点实验室开放课题(编号:2024LASW-B29);陕西省自然科学基础研究计划项目(编号:2023-JC-QN-0367)资助.

摘　　要：短时强降水是我国最主要的强对流灾害天气之一,易造成城市内涝和山洪、泥石流以及滑坡等次生地质灾害。回顾了近年来我国短时强降水的主要研究进展,并简要对比了美国和欧洲的相关研究成果,涵盖了短时强降水的时空分布特征、大气环流形势和环境条件、雷达回波特征和雨滴谱特征以及地形和城市化对短时强降水的影响及其机制;总结了人工智能在我国短时强降水潜势预报和短时临近预报中的应用。随着全球变暖,短时强降水频次和强度都呈增加趋势,今后需要进一步研究其形成机制和环境条件,提升观测时空分辨率,加强新型观测资料应用,通过融合分析多源且稠密的观测资料,提升高分辨率的快速更新循环同化数值模式预报能力,改进和发展深度学习预报模型和算法,尤其是研发深度学习大模型来提升短时强降水的预报预警能力。Short-duration heavy precipitation is one of the most substantial severe convective disasters in China and is prone to causing urban waterlogging and secondary geological disasters,such as mountain torrents,mudslides,and landslides.This paper reviews recent progress in short-duration heavy precipitation research in China and briefly compares relevant findings from the United States and Europe.It covers the spatiotemporal distribution characteristics and diurnal variation patterns of short-duration heavy precipitation,atmospheric circulation patterns and environmental conditions that influence its occurrence and development in major regions of China,radar echo characteristics and raindrop distributions,impact of topography and urbanization on its formation and development,and application of artificial intelligence in potential forecasting,short-term forecasting,and nowcasting of short-duration heavy precipitation in China.With global warming,the frequency and intensity of short-duration heavy precipitation events have increased.In the future,further research will be required to enhance understanding of the formation mechanisms and environmental conditions,improve the spatiotemporal resolution of observations,expand the use of new observation data,and enhance forecasting capabilities in high-resolution,rapid-update cycle assimilation numerical weather prediction models through the fusion and analysis of dense multisource observation data.Additionally,optimizing deep learning models and algorithms—particularly in the development of largescale deep learning models—will be crucial for improving forecasting and early warning capabilities for short-duration heavy precipitation.

关 键 词：短时强降水 日变化 环流分型 雨滴谱 热岛效应 客观预报方法 

分 类 号：P426.6[天文地球—大气科学及气象学]