双月刊

ISSN 1006-9895

CN 11-1768/O4

基于主客观环流分型的强降水数值预报MODE检验——方法及其在2019年暖季东北地区的应用
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1.中国科学院大气物理研究所云降水物理与强风暴重点实验室;2.中国气象局沈阳大气环境研究所

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中国科学院战略性先导科技专项(A类) XDA23090101,中国气象局沈阳大气环境研究所基本科研业务费重点项目 2020SYIAEZD4,国家重点研发计划项目 2018YFC1507305,中国气象局创新发展专项项目 CXFZ2021Z034,黑龙江省自然科学基金联合引导项目 LH2022D021,黑龙江省气象局智能网格预报及数值模式释用创新团队


Evaluation of heavy rainfall numerical prediction based on subjective and objective circulation classification as well as method for object-based diagnostic evaluation ——method and its application over Northeast China during the warm season of 2019
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Key Laboratory of Cloud-Precipitation Physics and Severe Storms (LACS), Institute of Atmospheric Physics, Chinese Academy of Sciences

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    摘要:

    本文构建了基于主客观环流分型的强降水数值预报空间检验(MODE)方法框架,并利用该框架对欧洲中期天气预报中心全球模式(ECMWF)和中国气象局区域中尺度数值天气预报模式(CMA_MESO)的2019年暖季东北地区强降水预报进行检验。结果表明:2019年暖季东北地区54个强降水日的环流型可分为:西风槽型(15个)、副高影响型(13个)、急流型(5个)、西部(12个)和东部冷涡型(9个)。其中,西风槽型和急流型以区域性强降水为主,模式对其强降水发生与否的预报能力强,TS评分较高;西部、东部冷涡型强降水的局地性强,模式对其强降水发生与否的预报能力差,TS评分低;副高影响型也以区域性强降水为主,模式对其强降水发生与否的预报能力也比较强,但是对其强降水质心位置、强度、面积等属性预报偏差较大,TS评分也相对较低。另外,从两种模式预报性能对比看,CMA_MESO对强降水强度和面积预报较实况普遍偏强,虽然其预报的TS评分一般高于ECMWF,但其对强降水预报的空报率也都比ECMWF大,对强降水的属性预报偏差一致性一般也低于ECMWF,其预报的可订正性整体上不及ECMWF。

    Abstract:

    Based on Subjective and Objective Circulation Classification, a framework of method for object-based diagnostic evaluation (MODE) is developed for numerical forecast of heavy rainfall, and this framework is used to verify the heavy rainfall forecast by the global forecast model of the European Center for Medium-Range Weather Forecasts (ECMWF) and the regional mesoscale forecast model of the China Meteorological Administration (CMA_MESO) in Northeast China during the warm season of 2019. The results show that 54 heavy rainfall days in Northeast China in the warm season of 2019 can be divided into trough pattern (P1), Western Pacific Subtropical High (WPSH) pattern (P2), jet pattern (P3), western Northeast China Cold Vortex (NCCV) pattern (P4), and Eastern NCCV pattern. Among the 5 synoptic patterns above, P1 and P3 are dominated by regional heavy rainfall, and the numerical model has strong predictability for the occurrence of heavy rainfall with higher Threshold Score (TS). The heavy rainfall in P4 and P5 is locality, and the numerical model has poor predictability with lower TS. The P2 is also dominated by regional heavy rainfall. However, the forecast deviation for the location, intensity, area of the heavy rainfall is relatively larger, and the TS is also lower. In addition, from the comparison of CMA_MESO and ECMWF, CMA_MESO is generally stronger than the actually intensity and area of heavy rainfall. The TS and false alarm rate (FAR) of CMA _ MESO for heavy rainfall are both generally higher than that of ECMWF. The forecast deviation of CMA_MESO has less consistent , and the predictability of CMA_MESO is generally lower.

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  • 收稿日期:2022-06-27
  • 最后修改日期:2022-10-14
  • 录用日期:2022-10-20
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