首页|多模式对四川盆地强降水过程的预报性能检验

多模式对四川盆地强降水过程的预报性能检验

扫码查看
原文链接
  • NETL
  • NSTL
  • 万方数据
为进一步认识当前数值预报模式的预报能力,选取2018-2020年发生在四川盆地的47次强降水过程进行分型,再基于多源降水融合产品和地面观测资料,通过TS评分、时空滑动等方法对欧洲中期天气预报中心(European Centre for Medium-Range Weather Forecasts,ECMWF)数值预报模式、国家气象中心区域中尺度数值预报模式(China Meteorological Administration Mesoscale Model,CMA_MESO)和西南区域数值预报系统(Southwest Center WRF ADAS Real-time Modeling System,SWC_WARMS)在强降水过程范围、强度、极值、时间和位移偏差等方面的预报能力进行检验评估.结果表明,各模式08:00(北京时,下同)预报优于20:00预报,ECMWF对中雨和大雨预报更优,SWC_WARMS的暴雨量级评分更高.各模式对中雨的预报范围普遍较实况偏大,随着降水量级增大,逐渐转为低估,其中SWC_WARMS更接近实况.对于降水强度,ECMWF和CMA_MESO的平均降水量和极值普遍较实况偏小,SWC_WARMS更接近实况.3种模式时间偏差不明显,仅个别起报时次有-6～3 h的时间偏差;ECMWF的位移偏差最小,纬向上ECMWF和SWC_WARMS以偏北为主,经向上ECMWF以偏西为主,CMA_MESO和SWC_WARMS以偏东为主.
Performance verification of multi-model heavy rainfall processes prediction in the Sichuan Basin
In order to further understand the prediction ability of current numerical prediction models,this paper selects 47 heavy rain-fall processes that occurred in the Sichuan Basin from 2018 to 2020 and classifies them,then based on merged precipitation products and ground observation data,the prediction ability of European Centre for Medium-Range Weather Forecasts(ECMWF),China Meteo-rological Administration Mesoscale Model(CMA_MESO)and Southwest Center WRF ADAS Real-time Modeling System(SWC_WARMS)models in the range,intensity,extreme value,time and displacement deviation of heavy rainfall processes is validated and assessed by using threat score,space-time sliding and other methods.The results show that the 08:00(UTC+08:00)prediction of each model is bet-ter than the 20:00(UTC+08:00)prediction,the ECMWF is better in moderate rain and heavy rain prediction,the SWC_WARMS has a higher score in the rainstorm prediction.The prediction range of moderate rain by various models is generally larger than the actual,and gradually turns to underestimate with the increase of magnitude,in which SWC_WARMS is closer to the actual.For rainfall inten-sity,the average precipitation and extreme value of ECMWF and CMA_MESO are generally smaller than the actual,and the prediction of SWC_WARMS is closer to the actual.The time deviation of predictions of three models is not obvious,only a few initial forecast times have a time deviation of-6 to 3 h,the displacement deviation of ECMWF products is the smallest,the prediction of ECMWF and SWC_WARMS are mainly northerly in latitudinal direction,while in meridional direction,the prediction of ECMWF is mainly to the west,and the predictions of CMA_MESO and SWC_WARMS are mainly to the east.

validation and assessmenttime slidingspace slidingthree-source merged precipitationprocess classification

龙柯吉、杨康权、康岚

展开 >

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

检验评估 时间滑动 空间滑动 三源融合降水 过程分型

四川省科技计划重点研发项目四川省科技厅中央引导地方科技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智能网格预报创新团队、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(重大专项)四川智能网格预报创新团队、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(研究型业务重点专项)四川智能网格预报创新团队、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(重点专项)

2022YFS05422023ZYD0147CXFZ2021J027CXFZ2021Z007SCQXKJZD202101SCQXKJYJXZD202201SCQXKJZD2020002

2024

10.11755/j.issn.1006-7639(2024)-03-0473

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(3)