引用本文:陈良吕, 陈法敬, 夏宇.基于SEEPS方法的重庆地区降水数值预报性能分析[J].西南大学学报(自然科学版),2019,41(7):116~124
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基于SEEPS方法的重庆地区降水数值预报性能分析
陈良吕, 陈法敬, 夏宇1,2,3
1. 重庆市气象科学研究所, 重庆 401147;2. 中国气象局数值预报中心, 北京 100081;3. 南京信息工程大学 大气科学学院, 南京 210044
摘要:
本研究简要介绍了SEEPS方法的具体计算方案,将该方法应用到重庆地区的降水数值预报检验中,对重庆地区常用的3个业务数值模式2017年全年的预报结果进行了检验评估,并对比分析了3个模式降水预报性能的总体差异及时空分布特征.结果表明,综合各个预报时效2017年全年区域平均SEEPS技巧评分的结果,EC模式的降水预报性能最优,其次是SWC-WARMS,CQMFS最差;综合各个预报时效2017年1-12月逐月区域平均的SEEPS技巧评分的结果,SWC-WARMS各月的预报性能均优于CQMFS.SWC-WARMS和CQMFS的降水预报性能在7月和8月总体而言优于EC模式,其余各月均差于EC模式;对于同一区域全年平均的降水数值预报性能,EC模式最优,其次是SWC-WARMS,CQMFS最差.各个模式的SEEPS技巧评分在四川盆地东部偏东地区均存在大值中心.EC模式总体表现出在重庆的东北部偏东地区和中西部偏北地区的SEEPS技巧评分优于重庆的其他地区.SWC-WARMS总体表现出在重庆东南部地区的SEEPS技巧评分优于重庆的其他地区.CQMFS总体表现出在重庆的东南部地区和重庆的中西部偏北地区的SEEPS技巧评分优于其他地区.
关键词:  降水预报  检验方法  SEEPS方法  概率空间
DOI:10.13718/j.cnki.xdzk.2019.07.016
分类号:P426.6
基金项目:重庆市气象局青年基金项目(QNJJ-201905);重庆市气象局数值模式应用技术攻关团队项目(YWGGTD-201715);中国气象局公益性行业科研专项项目(GYHY201506005).
An SEEPS-Based Analysis of Numerical Prediction Performance in Chongqing Area
CHEN Liang-lv, CHEN Fa-jing, XIA Yu1,2,3
1. Chongqing Institute of Meteorological Sciences, Chongqing 401147, China;2. NWP Center of China Meteorological Administration, Beijing 100081, China;3. School of Atmospheric Sciences, Nanjing University of Information Technology, Nanjing 210044, China
Abstract:
This paper gives a brief account of the specific calculation schemes of the SEEPS (stable equitable error in probability space) method, which is applied to the numerical prediction performance analysis of precipitation in Chongqing area. The annual forecast results of three models, which were operationally implemented and commonly used in Chongqing area in 2017, were tested and evaluated, and the overall difference and temporal and spatial characteristics of the three models were compared and analyzed. The results showed that, in general, based on the results of the regional average SEEPS skill score in 2017, the prediction performance of EC model was the best, followed in sequence by SWC-WARMS and CQMFS; and based on the results of the monthly mean SEEPS skill score in 2017, the prediction performance of SWC-WARMS in each month was better than that of CQMFS. The precipitation forecast performance SWC-WARMS and CQMFS in July and August was, as a whole, better than that of the EC model, but was inferior to that of EC in other months. For the average annual precipitation prediction performance of the same region, the EC model was the best, followed in order by SWC-WARMS and CQMFS. The SEEPS skill score of each model had a large-value center in the eastern part of the Sichuan basin. The EC model showed that the SEEPS skill score was generally higher in the northeast-by-east and mid-west-by-north parts of Chongqing than in the other areas of the city. The SWC-WARMS overall showed that the SEEPS skill score in the southeast of Chongqing was higher than in the other areas. The CQMFS overall showed that the SEEPS skills score in the southeast and mid-west-by-north regions of Chongqing was higher than that in the other regions.
Key words:  precipitation forecast  verification method  SEEPS method  probability space
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