中文摘要：

通过设计3组不同的观测误差均方差,对2012年8月1日—29日进行了基于GRAPES-M EPS（Global/Regional Assimilation and Prediction System-Mesoscale Ensemble Prediction System）的集合预报敏感性试验,研究观测误差均方差对集合预报初始扰动场结构、扰动量及垂直扰动总能量发展的影响,评估集合预报结果的差异,并分析了一次典型的江淮流域强降水个例。结果显示,模式变量扰动结构和扰动振幅对观测误差均方差较敏感,较小的观测误差均方差使得温度和风等模式变量的初始扰动量增大,扰动总能量增长更快,降水集合预报效果更优。因此在GRAPES-MEPS中,可以考虑对观测误差均方差进行适当的扰动,以体现观测误差均方差的不确定性对集合预报的影响,提高GRAPES-MEPS的集合预报技巧。

英文摘要：

It is well known that the atmosphere is a nonlinear dynamical system with chaotic characteristics, and small differences in the initial value of the numerical model may lead to completely different results. Ensemble prediction is a new generation of stochastic dynamic forecasting technique.It is based on the analysis of the initial value of the assimilation analysis to generate a set of normal distribution of the initial disturbance,thus it can be used to reflect the uncertainty in the assimilation analysis.The method by which to generate the initial set of dis- turbances is the core of ensemble prediction.The ETKF method is an initial perturbation technique that has been developed over the past 10 years,and has been widely used.Because the number of actual ensemble members is far less than the prediction of the model, the variance of the ensemble prediction model prediction may be underestimated, thus an amplification factor is introduced to adjust the magnitude of the ETKF. Observation mean square error has a major impact on the structure and initial perturbation to the regional Ensemble Prediction System of the China Meteorological Administration Numerical Prediction Center.In this paper we design three different sets of numerical simulations of the sensitivity tests of observed error from August 1 to August 29 2012.We then analyze the impact of the structure and initial perturbation on the initial perturbation field, and assess the difference of the total energy of vertical perturbation and ensemble forecast skill score by means of the GRAPES-MEPS （Global/Regional Assimilation and Prediction System, Mesoscale Ensemble Prediction System） of the China Meteorological Administration Numerical Prediction Center.In addition, we analyze a typical ensemble prediction rainfall in the Yangtze-Huaihe River Basin.The results indicate that with the observation mean square error reduced, the model variable temperature and initial perturbation wind increases, and the ensemble forecasting dispersion grows slightly better