川西高原夏半年数值模式降水预报分区分型精细化检验评估OA
Regional and circulation-type based fine verification of numerical model precipitation forecasts over the Western Sichuan Plateau in the warm season
为检验数值模式在复杂地形区的预报性能并提升川西高原夏半年降水预报能力,本文以2022-2024年夏半年(5-10月)川西高原116次强降水过程作为研究对象,在结合REOF分区和降水的主要影响环流分型基础上,采用常规检验、分数技巧评分(FSS)和MODE检验方法,对ECMWF、CMA-GFS、CMA-MESO和SWC-WARR 4类数值模式的降水预报性能开展精细化评估,初步剖析了导致模式偏差的可能原因.结果表明:全球模式ECMWF全域TS和FSS得分最高,在川西北空报率最低,对极端降水漏报显著,强度系统性偏弱;CMA-GFS在高原低涡型中强度预报较准确,两模式在高原低槽型和高原切变型中落区预报较准确;区域模式CMA-MESO命中率高、SWC-WARR形态匹配最佳,二者在川西南复杂地形区对高压影响型等中尺度过程预报更好,但普遍存在范围偏大,湿偏差明显,降水开始时间偏早等系统性偏差,导致空报率高、TS评分偏低.FSS日变化在11:00-14:00(北京时)达到峰值,源于区域模式预报提前与全球模式预报偏晚在午后与实况重叠所致;剖面分析进一步揭示,模式湿偏差源于中低层湿度模拟偏强、地形抬升响应过度、水汽辐合中心偏移及关键系统位置模拟的偏差,且对流酝酿期过早建立辐合是时间偏早主因.业务上大尺度过程优先参考ECMWF/CMA-GFS确定过程范围,中小尺度对流侧重CMA-MESO/SWC-WARR定位强中心,并对区域模式产品进行空间范围收缩和预报起始时间后调2～4 h,以匹配川西高原午后对流触发特征.
To evaluate the performance of numerical weather prediction(NWP)models over complex terrain and improve warm-season(May-October)precipitation forecasting over the Western Sichuan Plateau,this study assesses four models—ECMWF,CMA-GFS,CMA-MESO,and SWC-WARR—using 116 heavy rainfall events during 2022-2024.Based on REOF regionalization and dominant circulation classification,the models are eval-uated with traditional categorical scores,Fractions Skill Score(FSS),and Method for Object-Based Diagnostic Evaluation(MODE).Results show that the global model ECMWF has the highest overall TS and FSS scores and the lowest false alarm rate in northwestern Sichuan,but significantly underestimates extreme rainfall intensity.CMA-GFS performs well in intensity forecasts for plateau vortex-type events and accurately locates rainfall for plateau trough-type and plateau shear-line-type events.Among regional models,CMA-MESO has a higher hit rate,while SWC-WARR shows the best spatial pattern matching.Both perform better in the complex terrain of southwestern Sichuan for mesoscale processes such as high-pressure influence-type and dual-high shear-type events.However,they present obvious systematic biases,including overestimated rainfall areas,significant wet biases,and earlier predicted precipitation onset,leading to higher false alarm rates and lower TS scores.The diur-nal peak of FSS appears at 11:00-14:00(Beijing Time),caused by the overlap of early regional model forecasts and late global model forecasts with observed afternoon convection.Cross-section analyses indicate that wet bias-es result from overestimated mid-low-level humidity,excessive orographic lifting,displaced moisture conver-gence centers,and positional errors of key weather systems.Premature low-level convergence during the convec-tive initiation stage is the main cause of early forecast onset.For operational applications,large-scale processes should refer to ECMWF/CMA-GFS to determine rainfall ranges,while mesoscale convective centers should rely on CMA-MESO/SWC-WARR.It is recommended to shrink the rainfall areas and delay the predicted onset time of regional models by 2～4 hours to fit the observed afternoon convective characteristics over the Western Sichuan Plateau.
武敬峰;徐晓芳;阳春芳;黄楚惠;张雷;张其圣
阿坝州气象局,四川 马尔康 624000||高原与盆地暴雨旱涝灾害四川省重点实验室,四川 成都 610072阿坝州气象局,四川 马尔康 624000阿坝州气象局,四川 马尔康 624000四川省气象台,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阿坝州气象局,四川 马尔康 624000阿坝州气象局,四川 马尔康 624000
天文与地球科学
川西高原环流分型降水预报检验分数技巧评分(FSS)基于对象的评估诊断方法(MODE)系统性偏差
Western Sichuan Plateaucirculation classificationprecipitation forecast verificationFractions Skill Score(FSS)Method for Object-Based Diagnostic Evaluation(MODE)systematic bias
《高原气象》 2026 (4)
1027-1043,17
高原与盆地暴雨旱涝灾害四川省重点实验室科技发展基金项目(省重实验室SCQXKJYJXMS202416)四川省气象局强对流天气短临预报关键技术研究重点创新团队项目(SCQXZDCXTD202401)阿坝州灾害性天气监测预报预警和服务创新团队项目(阿气发[2023]47号)
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