doi:  10.3878/j.issn.1006-9895.1903.18228
降水邻域集合概率方法尺度敏感性试验

Scale Sensitivity Experiments of Precipitation Neighborhood Ensemble Probability Method
摘要点击 74  全文点击 36  投稿时间:2018-09-10  
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基金:  科技部科技支撑项目2015BAC03B01,国家重点研发计划项目2018YFC1507405,中国气象局公益性行业(气象)科研专项GYHY201506005
中文关键词:  集合预报  降水概率预报  邻域法  权重修正  邻域半径
英文关键词:  Ensemble forecast  Precipitation probability forecast  Neighborhood method  Weight correction  Radius of neighborhood
                 
作者中文名作者英文名单位
刘雪晴LIU Xueqing成都信息工程大学, 成都 610225
陈静CHEN Jing中国气象局数值预报中心, 北京 100081
陈法敬CHEN Fajing中国气象局数值预报中心, 北京 100081
夏宇XIA Yu南京信息工程大学, 南京 210044
范宇恩FAN Yuen成都信息工程大学, 成都 610225
徐致真XU Zhizhen中国气象科学研究院, 北京 100081
引用:刘雪晴,陈静,陈法敬,夏宇,范宇恩,徐致真.2020.降水邻域集合概率方法尺度敏感性试验[J].大气科学,44(2):282-296,doi:10.3878/j.issn.1006-9895.1903.18228.
Citation:LIU Xueqing,CHEN Jing,CHEN Fajing,XIA Yu,FAN Yuen,XU Zhizhen.2020.Scale Sensitivity Experiments of Precipitation Neighborhood Ensemble Probability Method[J].Chinese Journal of Atmospheric Sciences (in Chinese),44(2):282-296,doi:10.3878/j.issn.1006-9895.1903.18228.
中文摘要:
      降水邻域集合概率法是处理高分辨率降水集合预报不确定性的一种新方法。利用2017年5~7月GRAPES(Global and Regional Assimilation and Prediction Enhanced System)区域集合预报系统24 h降水预报资料,进行GRAPES降水邻域集合概率方法试验,并针对邻域概率法的等权重和邻域尺度问题,设计了邻域格点权重修正邻域方案以及二分类权重修正邻域方案,进行降水的集合概率法、等权重邻域集合概率方法、权重修正邻域集合概率方法和二分类权重修正邻域集合概率方法等四种方法的格点相关及敏感性试验,并利用多种概率预报检验评分评估上述四种方法的预报效果。试验结果表明:(1)尽管采用邻域计算方案的三种邻域集合概率方法的降水概率预报评分各有优劣,如等权重邻域集合概率法的相对作用特征曲线面积评分略优,而权重修正邻域集合概率法和二分类权重修正邻域集合概率法的降水概率预报可靠性更高,但采用了邻域计算方案的降水概率预报评分均优于传统的集合概率方法;(2)降水邻域集合概率方法的预报技巧对邻域尺度很敏感,统计评分最优的邻域半径为5~8倍模式水平格距;(3)引入了权重修正的两个邻域集合概率预报方法在24 h降水量超过10 mm时改进较明显,能够提供更加客观的概率预报结果。总体上看,降水邻域集合概率方法具有较好的应用前景,恰当的邻域概率方法及邻域半径可以获得更合理的降水概率预报结果。
Abstract:
      The precipitation neighborhood ensemble probability method is a new method to deal with the uncertainty of high-resolution ensemble forecast. Based on the 24 h accumulated precipitation data of May to July 2017 of GRAPES (Global and Regional Assimilation and Prediction Enhanced System) regional ensemble forecast system, experiments of precipitation neighborhood ensemble probability method were carried out. Moreover, aiming at the equal weight and neighborhood scale problems of the neighborhood probabilistic method, two kinds of weight correction schemes (weight correction neighborhood scheme and binary weight correction neighborhood scheme) were designed. Meanwhile, the grids correlation and sensitivity experiments of four groups of precipitation probability forecasts were implemented using the ensemble probability forecast, equal weight neighborhood ensemble probability method, weight correction neighborhood ensemble probability method, and binary weight correction neighborhood ensemble probability method. The results of precipitation probability prediction were verified by multiple probability scores, which showed that: (1) The precipitation probability scores of the neighborhood calculation scheme are superior to the original ensemble probability forecast method. The precipitation probability scores of the three neighborhood ensemble probability methods have their own advantages and disadvantages. For example, the relative operating characteristic area (AROC) score of the equal weight neighborhood ensemble probability method is slightly better; however, the higher reliability of precipitation probability prediction is determined by the weight correction neighborhood ensemble probability method and the binary weight correction neighborhood ensemble probability method. (2) The forecast skill of the precipitation neighborhood ensemble probability methods is very sensitive to the neighborhood scale. The optimal neighborhood radius is 5-8 times the horizontal grid scale of the model. (3) The two neighborhood ensemble probability methods combined with weight correction largely improved the forecast skill of the threshold by over 10 mm in 24 hours and provided more objective probability forecast results. Generally, the precipitation neighborhood ensemble probability method has good application values. By selecting the appropriate neighborhood probability method and the neighborhood radius, more objective prediction results can be obtained.
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