集合预报最优ETKF初始扰动方法设计及其在暴雨中的应用

Optimal design of ETKF in storm-scale ensemble forecast:A case study

为改进集合转换卡尔曼滤波方法(Ensemble Transform Kalman Filter,ETKF)在初始扰动中离散度偏小的问题,考虑引入物理不确定性。使用初始时刻离散度检验两种ETKF初始扰动方案改进的程度,通过动力和水汽条件分析探求改进机制。利用WRF模式构建更新预报系统,选取2014年5月一次暴雨个例进行集合降水预报试验,通过ETKF方法设计两种不同的初始扰动方案。结果表明:在分析循环中引入多物理扰动的初始扰动方案(multi)相比单一物理过程的初始扰动方案(mono)在初始时刻离散度和模拟动力水汽条件以及降水评分上均有较大改进。初始扰动中multi的离散度相比mono整体更优,显然添加了多物理扰动方案的试验对结果有改进作用;在对两种方案的机理分析中,multi对于降水位置的明显改善主要取决于散度及水汽通量散度模拟能力的提高;在离散度分析中,multi方案在强对流区域的改进效果比在整个区域中的更好,而对各变量的离散度和均方根误差之比相当,说明集合预报系统的合理性;对各量级预报结果评分显示,multi方案均呈现较好表现能力。

In order to improve the lowdispersion of Ensemble Transform Kalman Filter(ETKF)in initial perturbation,we considered the introduction of physical uncertainty.The improvement degree of the two ETKF initial perturbation schemes was tested using the initial dispersion.We explored the improvement mechanism by means of dynamic and water vapor condition analysis.The WRF model was used to construct an updated forecast system.One extreme heavy precipitation which happened in May 2014 was selected for ensemble forecasting test.Two initial perturbation schemes were designed by using ETKF method.The results show that the multi scheme with multiphysical perturbation in the analysis cycle is much better than mono scheme in initial moment dispersion and simulated dynamic and water vapor conditions as well as precipitation ratings.Compared with mono,the initial moment dispersion of multi scheme performs obviously better.It is evident that the experiments of the scheme with multiphysical perturbation can improve the results,and provide more forecast information.In the mechanism analysis of the two schemes,the significant improvement of the precipitation location for the multi scheme depends on the improved simulation of divergence and moisture flux divergence.In the analysis of dispersion,the improvement effect of multi scheme in strong convection area is better than that in whole area,and the ratio of dispersion to rootmean-square error of each variable is roughly equal,which prove the rationality of ensemble forecasting system.The results show that the multi scheme shows better performance.