干旱气象 ›› 2022, Vol. 40 ›› Issue (4): 700-709.DOI: 10.11755/j.issn.1006-7639(2022)-04-0700

• 技术报告 • 上一篇    下一篇

基于CMA-MESO的分级短时强降水概率预报方法研究

钟敏1(), 肖安2(), 许冠宇1   

  1. 1. 武汉中心气象台,湖北 武汉 430074
    2. 江西省气象台,江西 南昌 330096
  • 收稿日期:2021-01-18 修回日期:2021-06-10 出版日期:2022-08-31 发布日期:2022-09-22
  • 通讯作者: 肖安
  • 作者简介:钟敏(1980—),女,正研级高级工程师,主要从事暴雨及强对流天气预报研究. E-mail: zhongmin296@163.com
  • 基金资助:
    国家重点研发计划政府间国际科技创新合作重点专项能源与水纽带关系及高效绿色利用关键技术项目(2018YFE0196000);湖北省气象局重点科研项目(2022Z01);中国气象局预报员专项(CMAYBY2020-080);气象预报业务关键技术发展专项共同资助(YBGJXM(2020)3A-08)

Study on probability forecast method about graded short-term heavy rain based on CMA-MESO

ZHONG Min1(), XIAO An2(), XU Guanyu1   

  1. 1. Wuhan Center Meteorological Observatory, Wuhan 430074, China
    2. Jiangxi Meteorological Observatory, Nanchang 330096, China
  • Received:2021-01-18 Revised:2021-06-10 Online:2022-08-31 Published:2022-09-22
  • Contact: XIAO An

摘要:

随着预报服务需求不断增长和预报内容日趋精细化,仅针对20 mm·h-1以上的短时强降水预报已不能完全满足业务需要,开展不同雨强等级的短时强降水预报方法研究显得十分必要。利用2016—2019年6—8月中国南方9省1市的国家及区域气象站共51 355站次短时强降水样本,将雨强R分为4个等级:20≤R<30 mm·h-1、30≤R<50 mm·h-1、50≤R<80 mm·h-1R≥80 mm·h-1(分别对应I、Ⅱ、Ⅲ、IV级)。将各级样本与同时段CMA-MESO(China Meteorological Administration mesoscale model)数值预报模式初始场进行时空匹配,提取22个相关物理量建立数据集并进行百分位值统计;利用XGBoost(extreme gradient boosting)机器学习方法对物理量进行重要性排序以确定权重系数;应用连续概率预报方法,选用升、降半岭函数作为隶属函数,建立不同等级短时强降水概率预报模型。运用该模型在2020年汛期进行实时业务预报,并对湖北省2020年6—8月15次大暴雨过程0~36 h预报时效的逐小时不同等级短时强降水概率预报产品进行检验,结果表明:I级概率预报产品60%阈值的TS评分(0.145)最好,对应命中率为55.7%;Ⅱ级概率预报产品65%阈值的TS评分(0.083)最好,对应命中率为39.1%;Ⅲ级概率预报产品70%阈值的TS评分(0.03)最好,对应命中率为21.7%;IV级概率预报产品80%阈值的TS评分(0.005)最好,对应命中率为5.8%。对不同等级雨强个例对比检验表明,各级概率预报产品对CMA-MESO模式在同时次不同等级短时强降水预报上均有较好的订正作用。对3次强降水过程逐小时预报检验表明,I级概率预报产品命中率为40%~80%,空报率为50%~90%,预报时效达36 h,普遍优于同时次CMA-MESO降水量预报。本研究对不同等级短时强降水分型建模并在实际预报中有较好的参考性,能够对CMA-MESO的降水预报起到订正作用。

关键词: CMA-MESO, 分级降水, 短时强降水, 概率预报

Abstract:

With continuous growth of forecast service demand and increasingly refined forecast content, the forecast of short-term heavy precipitation above 20 mm·h-1 can not meet the forecast service demand fully. It is very necessary to carry out research on forecast methods about short-term heavy precipitation with different rainfall intensity. The 51 355 samples of short-term heavy rainfall from national and regional meteorological stations in nine provinces and one city in southern China from June to August during 2016-2019 were divided into four rainfall grades according to their rainfall intensity (R), namely, I: 20≤R<30 mm·h-1, II: 30≤R<50 mm·h-1, III: 50≤R<80 mm·h-1, and IV: R≥80 mm·h-1. The samples of all rainfall grades were spatiotemporal matched with the initial field of CMA-MESO (China Meteorological Administration mesoscale model) in the same period, and the percentile statistics were applied to 22 physical quantities extracted from these samples. The XGBoost (extreme gradient boosting) machine learning method was used to rank importance of those 22 physical quantities to determine their weight coefficients. Based on the continuous probability prediction method, the ascending and descending half ridge functions were selected as the membership function, the probability prediction models of short-term heavy precipitation with different rainfall grades were established. The real-time operational prediction was carried out in flood season of 2020 using these prediction medels, and the hourly probability prediction products of short-term heavy precipitation with different rainfall grades for 0-36 h prediction time during 15 heavy rainstorm precesses in Hubei Province from June to August 2020 were tested. The results show that for the grade I probability prediction products, the TS score (0.145) using 60% as threshold works best, with a corresponding hit rate of 55.7%; for the grade II probability prediction products, the TS score (0.083) using 65% as threshold works best, with a corresponding hit rate of 39.1%; for the grade III probability prediction products, the TS score (0.03) using 70% as threshold works best, with a corresponding hit rate of 21.7%; for the grade IV probability prediction products, the TS score (0.005) using 80% as threshold works best, with a corresponding hit rate of 5.8%.The results also suggest that probability prediction products help to correct the CMA-MESO model in predicting short-term heavy precipitation with different rainfall grades at the same time. The hourly prediction test of three heavy precipitation processes shows the hit rate of 40%-80%, the false rate of 50%-90%, and 36 h prediction time for the grade I probability forecast products, which are generally better than CMA-MESO precipitation forecast at the same time. A model was established to forecast short-term heavy precipitation with different grades in this study, and it outperforms existing numerical models and can be a good reference for meteorologists to forecast short-term heavy precipitation and correct precipitation forecast biases in CMA-MESO.

Key words: CMA-MESO, graded precipitation, short-term heavy rain, probability forecast

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