地球科学进展 ›› 2023, Vol. 38 ›› Issue (2): 111 -124. doi: 10.11867/j.issn.1001-8166.2023.003

综述与评述    下一篇

上海强对流智能预报业务新技术研究进展
马雷鸣 1 , 2( ), 林红 1 , 2, 储海 1 , 2, 尹春光 2, 张军平 3, 陈磊 1, 王海宾 1, 徐康 1, 范旭亮 1   
  1. 1.上海中心气象台,上海 200030
    2.上海市气象局,上海 200030
    3.复旦大学,上海 200438
  • 收稿日期:2022-09-23 修回日期:2022-12-28 出版日期:2023-02-10
  • 基金资助:
    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”(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(18DZ1200400)

Research Progress of Shanghai Operational Intelligent Forecast Technologies on Severe Convection

Leiming MA 1 , 2( ), Hong LIN 1 , 2, Hai CHU 1 , 2, Chunguang YIN 2, Junping ZHANG 3, Lei CHEN 1, Haibin WANG 1, Kang XU 1, Xuliang FAN 1   

  1. 1.Shanghai Central Meteorological Observatory, Shanghai 200030, China
    2.Shanghai Meteorological Service, Shanghai 200030, China
    3.Fudan University, Shanghai 200438, China
  • Received:2022-09-23 Revised:2022-12-28 Online:2023-02-10 Published:2023-03-02
  • About author:MA Leiming (1975-), male, Shihezi City, Xinjiang Uygur Autonomous Region, Professor. Research areas include typhoon, numerical prediction, artificial intelligence technology. E-mail: malm@typhoon.org.cn
  • Supported by:
    the National Natural Science Foundation of China “Study on the mechanism of atmospheric processes at the top of the boundary layer affecting the intensity of deep convection and parameterized closure”(41975069);Shanghai Municipal Commission of Science and Technology “Research and application of key technologies of digital simulator for sudden disastrous weather (strong convection)”(18DZ1200400)

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.

中图分类号: 

1 RINEHART R E, GARVEY E T. Three-dimensional storm motion detection by conventional weather radar[J]. Nature, 1978, 273(5 660): 287-289.
2 WANG Gaili, WONG Waikin, LIU Liping, et al. Application of multi-scale tracking radar echoes scheme in quantitative precipitation nowcasting[J]. Advances in Atmospheric Sciences, 2013, 30(2): 448-460.
3 GIBSON J J. The perception of the visual world[M]. Boston: Houghton Mifflin, 1950.
4 YU Xiaoding, ZHOU Xiaogang, WANG Xiuming. The advances in the nowcasting techniques on thunderstorms and severe convection[J]. Acta Meteorologica Sinica, 2012, 70(3): 311-337.
俞小鼎, 周小刚, 王秀明. 雷暴与强对流临近天气预报技术进展[J]. 气象学报, 2012, 70(3): 311-337.
5 ZHENG Yongguang, ZHANG Xiaoling, ZHOU Qingliang, et al. Review on severe convective weather short-term forecasting and nowcasting[J]. Meteorological Monthly, 2010, 36(7): 33-42.
郑永光, 张小玲, 周庆亮, 等. 强对流天气短时临近预报业务技术进展与挑战[J]. 气象, 2010, 36(7): 33-42.
6 WILSON J W, FENG Y R, CHEN M, et al. Nowcasting challenges during the Beijing olympics: successes, failures, and implications for future nowcasting systems[J]. Weather and Forecasting, 2010, 25(6): 1 691-1 714.
7 GOLDING B W. Nimrod: a system for generating automated very short range forecasts[J]. Meteorological Applications, 1998, 5(1): 1-16.
8 YAO Zuqing. Analysis and study on environmental cloud field of mesoscale severe convective rainstorms in Shanghai [J]. Journal of Nanjing Institute of Meteorology 1989, 12(3):16-18.
姚祖庆. 上海地区中尺度强对流暴雨的环境云场分析研究[J].南京气象学院学报,1989,12(3):16-18.
9 YAO Zuqing, HUANG Yan. The working flow of short-term forecast for severe convection in Shanghai[J]. Meteorological Monthly, 2000, 26(9): 15-18, 23.
姚祖庆, 黄炎. 上海地区强对流短时预报工作流程及其应用[J]. 气象, 2000, 26(9): 15-18, 23.
10 DU Bingyu, GUAN Li, YAO Zuqing, et al. Nowcasting system of severe convective weather in Shanghai[J]. Journal of Nanjing Institute of Meteorology, 2000, 23(2): 242-250.
杜秉玉, 官莉, 姚祖庆, 等. 上海地区强对流天气短时预报系统[J]. 南京气象学院学报, 2000, 23(2): 242-250.
11 CHEN Lei, DAI Jianhua, TAO Lan. Application of an improved TREC algorithm(COTREC)for precipitation nowcast[J]. Journal of Tropical Meteorology, 2009, 25(1): 117-122.
陈雷, 戴建华, 陶岚. 一种改进后的交叉相关法(COTREC)在降水临近预报中的应用[J]. 热带气象学报, 2009, 25(1): 117-122.
12 SUN Min, DAI Jianhua, YUAN Zhaohong, et al. An analysis of a back-propogating thunderstorm using the three-dimensional wind fields retrieved by the dual-Doppler radar data[J]. Acta Meteorologica Sinica, 2015, 73(2): 247-262.
孙敏, 戴建华, 袁招洪, 等. 双多普勒雷达风场反演对一次后向传播雷暴过程的分析[J]. 气象学报, 2015, 73(2): 247-262.
13 TAO Lan, GUAN Li, SUN Min, et al. Evolution analysis of a hail-producing supercell using dual polarization Doppler radar[J]. Journal of the Meteorological Sciences, 2019, 39(5): 685-697.
陶岚, 管理, 孙敏, 等. 双线偏振多普勒雷达对一次降雹超级单体发展减弱阶段的演变分析[J]. 气象科学, 2019, 39(5): 685-697.
14 ZHANG Delin, MA Leiming. Mesoscale observation and simulation on 30 July 2005 severe convective case in Shanghai[J]. Meteorological Monthly, 2010, 36(3): 62-69.
张德林, 马雷鸣. “0730”上海强对流天气个例的中尺度观测分析及数值模拟[J]. 气象, 2010, 36(3): 62-69.
15 MA Leiming. Development of artificial intelligence technology in weather forecast[J]. Advances in Earth Science, 2020, 35(6): 551-560.
马雷鸣. 天气预报中的人工智能技术进展[J]. 地球科学进展, 2020, 35(6): 551-560.
16 SHI X J, CHEN Z R, WANG H, et al. Convolutional LSTM Network: a machine learning approach for precipitation nowcasting[C]//Proceedings of the 28th international conference on neural information processing systems-volume 1. New York: ACM, 2015: 802-810.
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