Journal of Arid Meteorology ›› 2025, Vol. 43 ›› Issue (1): 143-152.DOI: 10.11755/j.issn.1006-7639-2025-01-0143

• Technical Reports • Previous Articles     Next Articles

Improvement of hourly short-term heavy rain forecasting method in Hubei Province based on multi-model integration

WAN Yu(), XU Guanyu(), ZHONG Min, LIU Ruixue, LIU Wenting   

  1. Hubei Meteorological Observatory,Wuhan 430074,China
  • Received:2024-04-11 Revised:2024-07-10 Online:2025-02-28 Published:2025-03-15

基于多模式融合的湖北逐时短时强降水预报方法改进

万羽(), 许冠宇(), 钟敏, 刘瑞雪, 刘文婷   

  1. 湖北省气象台,湖北 武汉 430074
  • 通讯作者: 许冠宇(1988—),女,高级工程师,主要从事天气预报和强天气诊断研究。E-mail:xiaoyu_xy219@163.com。
  • 作者简介:万羽(1997—),男,工程师,主要从事天气预报工作和研究。E-mail:wanyu97@163.com
  • 基金资助:
    中国气象局创新发展专项(CXFZ2024J015);湖北省自然科学基金气象创新发展联合基金项目(2022CFD011);中国气象局复盘总结专项(FPZJ2023-081)

Abstract:

Extreme heavy rainfall events are occurring with heightened frequency due to intensified global climate warming, posing growing risks to public safety and social development. It is of great significance for disaster prevention and reduction to study the short-term heavy rain. Based on the precipitation data from regional automatic stations in Hubei Province, short-term heavy rain probability forecast products, and mesoscale high-resolution numerical model data, this study adopts neighborhood optimal probability and multi-model integration methods for the short-term heavy rainfall location forecasting in Hubei Province with a lead time of up to 12 h. The results show that the neighborhood method obviously improves the prediction accuracy of the mesoscale numerical model for short-term heavy rain, with the area neighborhood method outperforming the single-point neighborhood method. The optimal area probability of CMA-MESO, CMA-SH9 and WH-RUC modes are all 5%, and the optimal neighborhood radius is 50, 60 and 60 km respectively. The multi-mode integration method shows significant improvement compared to the single-point neighborhood method with one model. The threat scores for all lead times indicate positive forecast skill, improving by 0.014 and 0.020 from April to September in 2023 and 2024, respectively. The improved multi-model integration method shows a substantial increase in probability of detection, especially in accuracy of various severe convection prediction in Hubei Province on August 7, 2023 and June 28, 2024.

Key words: short-term heavy rain, high-resolution model, neighborhood method, multi-model integration, threat score

摘要: 随着全球气候变暖的加剧,极端强降水事件发生频率明显增加,对经济社会发展及人民生命财产安全构成重大威胁。开展短时强降水的预报研究对于防灾减灾具有重要意义。基于湖北省区域自动站降水资料、短时强降水概率预报产品和中尺度高分辨率数值模式资料,采用邻域最优概率法和多模式融合技术对湖北省1~12 h短时强降水的落区进行预报与检验评估。结果表明,邻域法明显提高了中尺度数值模式对短时强降水的预报能力,其中面积邻域法的效果优于单点邻域法,CMA-MESO、CMA-SH9和WH-RUC模式的最优面积概率均为5%,最优邻域半径分别为50、60、60 km;多模式融合预报方法较单模式单点邻域法表现出明显优势,2023年、2024年4—9月短时强降水的1~12 h TS评分均表现为正技巧,分别提高0.014、0.020;改进后的多模式融合方法对短时强降水的命中率有大幅提升,尤其是在湖北省2023年8月7日和2024年6月28日的多次强对流过程预报中均有提前精准预报。

关键词: 短时强降水, 高分辨率模式, 邻域法, 多模式融合, TS评分

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