摘要
强对流天气系统发展剧烈,常造成严重的暴雨、雷电、大风和冰雹等灾害。对强对流天气的准确预报一直是国际气象领域研究的难点和瓶颈问题,也是大城市防灾减灾关注的重点。强对流预报技术的发展已经从单纯依赖于雷达的图像识别,发展到雷达、卫星、高分辨率数值模式以及人工智能大数据预测融合应用的新阶段。在回顾国际强对流预报技术进展的基础上,介绍了上海气象业务部门近年来攻关研发的雷达自适应组网观测、雷达外推短临预测和数值预报误差机器学习订正等关键新技术及集成建立的上海强对流智能监测预报系统。这些技术成果的业务应用,在城市防灾减灾和精细化治理中已发挥了重要作用,可供相关研究人员参考。
Severe convective weather systems often cause rainstorms,lightning,gales,hail,and other disasters owing to their small spatial scale and rapid and violent development.Accurate forecasting has always been a difficult and bottleneck problem in the international meteorological field,and it is the focus of disaster prevention and mitigation in Shanghai.This research introduces key technologies developed by the Shanghai Meteorological Department in recent years,such as self-adaptive networking observation of strong convection targets,intelligent identification and prediction of strong convection abrupt structural features,machine learning correction of numerical prediction errors,and system integration.Based on this,an intelligent monitoring and early warning system that can simulate the three-dimensional structure and evolution of a strong convective system is established and applied,which has significantly improved the early warning capability fo severe convection in Shanghai.Relevant technical achievements have provided support for major services such as the China International Import Expo(CIIE)and have been promoted for application in urban disaster prevention and mitigation.
作者
马雷鸣
林红
储海
尹春光
张军平
陈磊
王海宾
徐康
范旭亮
MA Leiming;LIN Hong;CHU Hai;YIN Chunguang;ZHANG Junping;CHEN Lei;WANG Haibin;XU Kang;FAN Xuliang(Shanghai Central Meteorological Observatory,Shanghai 200030,China;Shanghai Meteorological Service,Shanghai 200030,China;Fudan University,Shanghai 200438,China)
出处
《地球科学进展》
CAS
CSCD
北大核心
2023年第2期111-124,共14页
Advances in Earth Science
基金
国家自然科学基金项目“边界层顶大气过程影响深对流强度变化机制及参数化闭合研究”(编号:41975069)
上海市科委重点项目“超大城市突发性灾害天气(强对流)数字模拟器关键技术研究及应用”(编号:18DZ1200400)资助
关键词
强对流
雷达
数值预报
机器学习
天气预报
Severe convection
Radar
Numerical weather prediction
Machine learning
Weather forecast
