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CN 11-1768/O4

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降水邻域集合概率方法尺度敏感性试验
作者:
作者单位:

1.成都信息工程大学;2.中国气象局数值预报中心

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基金项目:

科技部科技支撑项目(批准号2015BAC03B01),中国气象局公益性行业(气象)科研专项(GYHY201506005),


The Scale Sensitivity experiments of Precipitation Neighborhood Ensemble Probability method
Author:
Affiliation:

1.Numerical Weather Prediction Centre,CMA;2.Nanjing University of Information Science Technology;3.Chengdu University of information technology

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    摘要:

    降水邻域集合概率法是处理高分辨率集合预报降水预报不确定性的一种新方法。利用2017年5-7月GRAPES区域集合预报系统24h降水预报资料,进行GRAPES(Global and Regional Assimilation and Prediction Enhanced System)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降水阈值为10mm/24h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 from May to July 2017 of GRAPES(Global and Regional Assimilation and Prediction Enhanced System)regional ensemble forecast system. The experiments of precipitation neighborhood ensemble probability method were carried out. At the same time, aiming at the equal weight and neighborhood scale problems of neighborhood probabilistic method, two kinds of weight correction schemes are designed, which are weight correction neighborhood scheme and binary weight correction neighborhood scheme respectively. Meanwhile, the grids correlation and sensitivity experiments of four groups of precipitation probability forecasts were implemented by using 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 show that :(1) the precipitation probability scores of neighborhood calculation scheme are superior to the original ensemble probability forecast method. Although the precipitation probability scores of the three neighborhood set probability methods have their own advantages and disadvantages. For example, the relative working characteristic area (AROC) score of the equal weight neighborhood ensemble probability method is slightly better. However, the reliability of precipitation probability prediction is determined by weighted correction neighborhood ensemble probability method and binary weighted correction neighborhood ensemble probability method. (2)The forecast skill of the precipitation neighbourhood 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 have largely improved the forecast skill of the threshold more than 10mm/24h and provided more objective probability forecast results. Generally speaking, the precipitation neighborhood ensemble probability method has a well application values. By selecting the appropriate neighborhood probability method and neighborhood radius, more objective prediction results can be obtained.

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历史
  • 收稿日期:2018-09-10
  • 最后修改日期:2018-12-28
  • 录用日期:2019-04-17
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