首页|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

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

Method of Object-Based Diagnostic Evaluation for Numerical Heavy-Precipitation Prediction Based on Subjective and Objective Circulation Classification:Application and Testing over Northeast China during the Warm Season of 2019

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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)、副热带高压影响型(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;副热带高压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.
Based on subjective and objective circulation classification and the MODE(Method of Object-based Diagnostic Evaluation),a evaluation method framework is developed for numerical heavy-rainfall prediction.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 during this period can be classified into a trough pattern(P1),western Pacific subtropical high pattern(P2),jet pattern(P3),western Northeast China Cold Vortex(NCCV)pattern(P4),and eastern NCCV pattern(P5).Among these five synoptic patterns,P1 and P3 are dominated by regional heavy rainfall,and the numerical model has high predictability for the occurrence of heavy rainfall with high threshold scores(TS).The heavy rainfall of P4 and P5 is localized,and the numerical model has poor predictability with low TS.P2 is also dominated by regional heavy rainfall.However,the forecast deviation for the location,intensity,and area of heavy rainfall is relatively large,and the TS is low.In addition,from the comparison of CMA_MESO and ECMWF results,CMA_MESO's rainfall predictions are generally stronger in intensity and larger in area than the actual rainfall of heavy rains.For heavy rainfall,CMA_MESO results show a generally higher TS and false alarm rate than ECMWF results.CMA_MESO has a less consistent forecast deviation and generally lower predictability.

Subjective and objective circulation classificationNCCV(Northeast China cold vortex)objective identificationHeavy rainfallNumerical weather forecastMODE(Method of Object-based Diagnostic Evaluation)

齐铎、崔晓鹏、陈力强、黄丽君、刘松涛、卜文惠、王承伟

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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,北京 100029

中国科学院大学,北京 100049

黑龙江省气象台,哈尔滨 150030

南京信息工程大学气象灾害预报预警与评估协同创新中心,南京 210044

中国气象局沈阳大气环境研究所,沈阳 110166

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主客观融合环流分型 东北冷涡客观识别 强降水 数值预报 MODE检验

中国科学院战略性先导科技专项(A类)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国家重点研发计划中国气象局创新发展专项黑龙江省自然科学基金联合引导项目黑龙江省气象局智能网格预报及数值模式释用创新团队项目

XDA230901012020SYIAEZD42018YFC1507305CXFZ2021Z034LH2022D021

2024

10.3878/j.issn.1006-9895.2210.22107

大气科学
中国科学院大气物理研究所

大气科学

CSTPCD北大核心
影响因子：2.11
ISSN：1006-9895
年,卷(期)：2024.48(3)
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