摘要
以三源融合网格实况降水分析资料CMPAS为参照,基于二分法经典检验、预报评分综合图和面向对象MODE检验等方法,对比分析2021年智能网格预报SCMOC以及ECMWF全球、CMA-Meso中尺度模式在秦岭及周边地区的降水预报表现,主要结论如下:1)ECMWF能够很好地刻画日平均降水量、日降水量标准差以及地形影响下降水量、降水频次的空间分布特征,但对于0.1 mm以上量级的降水预报频次远高于观测,暴雨预报频次低于观测,SCMOC、CMA-Meso日降水量大于等于0.1 mm的降水频次和暴雨频次预报更好;SCMOC不足在于降水的空间精细分布特征描述能力相对较弱。2)ECMWF预报的大于等于0.1 mm降水频次日峰值出现时间整体较观测偏早3 h左右,CMA-Meso、SCMOC与观测总体吻合较好。3)三种产品24 h降水量大于等于0.1 mm的TS(Threat Score)评分数值上基本一致,但降水预报表现的特征显著不同,SCMOC成功率高、命中率低,漏报多、空报少,ECMWF、CMA-Meso则相反;24 h、3 h大雨以上量级降水SCMOC的TS评分、成功率、命中率一致优于其他两种产品。4)MODE暴雨检验,SCMOC大面积降水对象与观测相似度最高,预报能力优于ECMWF,但分散性小面积暴雨对象漏报风险大。SCMOC、ECMWF纬向距离偏差大于经向,位置偏西比例高于偏东。
Based on the three-source fusion grid precipitation analysis data from CMPAS and using the dichotomy classical verification method,a comprehensive map of precipitation forecast score,and the Method for Object-Based Diagnostic Evaluation(MODE),we compare and analyzed the precipitation forecast performance of the fine-gridded SCMOC,ECMWF global,and CMA-Meso models in Qinling and its surrounding areas in 2021,and observe the following:1)The ECMWF model can well describe the spatial distribution characteristics of daily average precipitation,daily precipitation standard deviation,and daily precipitation frequency under the influence of terrain.However,the precipitation frequency of more than 0.1 mm is much higher than the observation,and the torrential rain frequency is lower than the observation.SCMOC and CMA-Meso have better forecasts of precipitation of different grades.The deficiency of SCMOC is that its ability to describe the fine spatial distribution characteristics of precipitation is relatively weak.2)The occurrence time of the daily peak of precipitation frequency greater than 0.1mm in the ECMWF model is about 3 hours earlier than the observation,and CMA-Meso and SCMOC are more consistent with the observation.3)The TS scores of the three products with 24-hour precipitation greater than or equal to 0.1 mm are basically the same,but the characteristics of the precipitation forecast are significantly different.SCMOC has a high success rate,a low hit rate,more missed hits,and fewer false alarms than ECMWF and CMA-Meso models which are the opposite of SCMOC.SCMOC s TS score,success rate,and hit rate for 24 h,3 h heavy rain,and above are better than the other two products.4)The verification results of the MODE method show that SCMOC has the highest similarity between the forecast and observation of large-area precipitation objects,and its forecast ability is better than ECMWF and CMA-Meso.However,there is a high risk of missing a hit for scattered,small-area torrential rain objects.The east-west distance deviation of SCMOC and ECMWF is greater than that of the north-south direction,and the proportion of the west position is higher than that of the east position.
作者
潘留杰
张宏芳
刘静
刘嘉慧敏
梁绵
祁春娟
戴昌明
李培荣
PAN Liujie;ZHANG Hongfang;LIU Jing;LIU Jiahuimin;LIANG Mian;QI Chunjuan;DAI Changming;LI Peirong(Shaanxi Meteorological Observatory,Xi’an 710014,China;Key Laboratory of Eco-Environment and Meteorology for the Qinling Mountains and Loess Plateau,Xi’an 710014,China;Shaanxi Meteorological Service Centre,Xi’an 710014,China;Shengyang Meteorological Observatory,Shengyang 110166,China)
出处
《大气科学学报》
CSCD
北大核心
2023年第2期217-229,共13页
Transactions of Atmospheric Sciences
基金
中国气象局创新发展专项(CXFZ2022J023)
中国气象局复盘总结专项(FPZJ2023-129)
陕西省社会发展关键领域项目(2022SF-360)
陕西省自然科学基金资助项目(2022JQ-249)。
