Abstract: 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.

Key words: singular vectors, ensemble prediction, GRAPES model, southwest vortex, heavy rain

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。

关键词: 奇异向量, 集合预报, GRAPES模式, 西南涡, 暴雨