• CN 62-1175/P
• ISSN 1006-7639
• 双月刊
• 中国科技核心期刊
• 中国学术期刊综合评价数据库统计源期刊
• 中文科技期刊数据库收录期刊

• 技术报告 •

### 基于CMA-MESO的分级短时强降水概率预报方法研究

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

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.

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