首页|四个数值预报模式对四川强降水过程预报能力评估

四个数值预报模式对四川强降水过程预报能力评估

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为衡量数值模式对强降水过程的预报能力,选取欧洲中期天气预报中心(the European Centre for Medium-Range Weather Forecasts,ECMWF)数值预报产品、国家气象中心区域中尺度数值预报产品(China Meteorological Administration Mesoscale Model,CMA-MESO 3KM)、西南区域中心中尺度模式系统(Southwest Center WRF ADAS Real-time Modeling System,SWC)、国家气象中心全球数值预报系统(China Meteorological Administration for Global Forecast System,CMA-GFS)4个模式预报产品,利用目标对象检验法,对四川2018-2020年共93次强降水过程(≥25 mm·d-1)从降水位置、降水面积、降水强度等方面进行检验,在此基础上重点讨论36 h预报时效模式的预报能力.结果表明:(1)随着预报时效越临近,各模式预报平均水平越高,且整体对雨带位置把握较好,更具有参考性.(2)各模式对锋面降水过程预报能力较强,对暖区降水过程预报能力较差.(3)暖区强降水过程可在大尺度模式基础上结合本地中尺度模式进行订正;锋面降水过程则以ECMWF模式预报为基础,参考CMA-MESO 3KM模式对大雨及以上量级降水落区和量级进行调整.
Evaluation of forecasting ability of four numerical models for heavy precipitation processes in Sichuan Province
In order to measure the ability of numerical models to forecast heavy precipitation processes,the four prediction products of the European Centre for Medium-Range Weather Forecasts(ECMWF),China Meteorological Administration Mesoscale Model(CMA-MESO 3KM),Southwest Center WRF ADAS Real-time Modeling System(SWC),China Meteorological Administration for Global Fore-cast System(CMA-GFS)are selected,and 93 heavy precipitation processes(≥25 mm·d-1)in Sichuan Province from 2018 to 2020 are tested from the aspects of precipitation location,precipitation area and precipitation intensity using the target object test method,and on this basis,the prediction ability of models for 36 h forecast aging is discussed.The results are as follows:(1)With the approaching of forecasting time,the average forecast level of each model is higher,and the position of rain belt is better grasped.(2)The prediction ability of each model for frontal precipitation process is better,while for warm-region precipitation process it is poor.(3)The heavy warm-region precipitation process can be corrected based on the large-scale model forecasts combined with the local mesoscale model products.For frontal precipitation process,on the basis of forecast of ECMWF model,the precipitation area and magnitude of heavy rain and above are adjusted according to the location of rain belt in CMA-MESO 3KM model.

Sichuan Provincemulti-modelobject-based verificationheavy precipitation processtest

王彬雁、王佳津、肖递祥、龙柯吉

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四川省气象台,四川 成都 610072

高原与盆地暴雨旱涝灾害四川省重点实验室,四川 成都 610072

四川 多模式 目标对象 强降水过程 检验

四川省科技计划重点研发项目中国气象局创新发展专项中国气象局创新发展专项中国气象局创新发展专项四川省自然科学基金面上项目四川省气象局智能网格预报创新团队、国家重点研发计划"典型灾害天气公里级滚动预报关键技术研究与示范应用"项目2024年中国气象局复盘总结专项

2022YFS0542CXFZ2021J027CXFZ2023J016CXFZ2024J01323NSFSC01892021YFC3000900FPZJ2024-111

2024

10.11755/j.issn.1006-7639(2024)-02-0315

干旱气象
中国气象局兰州干旱气象研究所 中国气象学会干旱气象学委员会

干旱气象

CSTPCD
影响因子：1.9
ISSN：1006-7639
年,卷(期)：2024.42(2)
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