GRAPES奇异向量研究及其在暴雨集合预报中的应用  

Research on GRAPES Singular Vectors and Application to Heavy Rain Ensemble Prediction

作　　者：刘永柱[1,2] 杨学胜[2] 王洪庆[1] 

机构地区：[1]北京大学物理学院大气与海洋科学系,北京100871 [2]中国气象科学研究院灾害天气国家重点实验室,北京100081

出　　处：《北京大学学报（自然科学版）》2011年第2期271-277,共7页Acta Scientiarum Naturalium Universitatis Pekinensis

基　　金：国家科技支撑计划(2006BAC02B01;2006BAC03B03);863计划(2006AA01A123)资助

摘　　要：为了产生好的集合预报初始扰动,并能够用有限的集合样本来模拟大气概率密度函数在相空间中的时间演变,把奇异向量法引入非静力GRAPES中尺度模式中,研究了GRAPES奇异向量的基础性问题和基于奇异向量构造集合样本的方法。对2008年7月的一次西南涡移动带来的暴雨过程进行GRAPES SVs求解,并进行集合预报试验。结果表明:前27个GRAPES SVs反映了分析误差的主要信息;要素的集合平均的均方根误差比控制预报具有更好的预报技巧,且它们的集合离散度随时间逐渐增加,反映了预报误差的主要信息;从降水Brier评分和ROC技巧上可以看出该集合预报具有好的概率预报技巧,能为暴雨预报提供一定的指导作用。In order to generate the good initial perturbations of ensemble prediction and use limited ensemble members to simulate the time evolution of the atmosphere probability density function in the phase space, the singular vectors （SVs） method was introduced into non-hydrostatic GRAPES meso-scale model. The basic problem of GRAEPS SVs and the method to construct ensemble members based on SVs were researched. GRAPES SVs were solved for a heavy rainfall case by a southwest China vortex moving on July 2008, and an ensemble simulation experiment was made based on GRAPES SVs. The results indicate that the first 27 GRAPES SVs reflect the key information of analysis error; the root mean square error （RMSE） of ensemble mean about forecast elements have better forecast skills than control forecast, and its ensemble spread is gradually growth with time evolution, reflecting the key information of forecast error; brier score and ROC skill of rainfall show that this ensemble prediction has higher probability skill, and can provide some guidance for the heavy rainfall forecast.

关 键 词：奇异向量 集合预报 GRAPES模式 西南涡 暴雨 

分 类 号：P456.7[天文地球—大气科学及气象学]