中文摘要：

集合卡尔曼滤波资料同化方法,可以用集合样本统计出随天气形势变化的误差协方差,是当前资料同化领域的研究热点。主要介绍了GRAPES集合卡尔曼滤波资料同化系统的设计以及初步的试验结果。针对集合卡尔曼滤波同化实际观测资料难以实施的问题,采用成批观测同化的顺序同化方法进行多变量的集合卡尔曼滤波同化;为了滤除有限集合数造成的误差相关噪音和缓解求逆矩阵不满秩的问题,在水平和垂直方向都采用了Schur滤波;建立了与GRAPES预报模式的垂直坐标和预报变量一致的模式面集合卡尔曼滤波系统;集合样本的生成考虑了模式变量的空间相关和模式变量之间的相关,通过利用三维变分分析中的控制变量变换得到模式变量扰动场。通过比较GRAPES集合卡尔曼滤波资料同化系统和GRAPES区域三维变分资料同化系统的单点观测资料同化分析结果,对比背景误差相关系数的分布,验证了GRAPES集合卡尔曼滤波系统的正确性。此外,同化区域探空观测资料试验结果表明,GRAPES集合卡尔曼滤波资料同化系统能够得到合理的分析,并且具有实际运行能力。对分析结果进行12h预报表明,GRAPES集合卡尔曼滤波资料同化系统的分析协调性不如三维变分资料同化系统。

英文摘要：

The ensemble Kalman filter（EnKF） is able to obtain the flow-dependent background error covariance based on the statistics of the ensemble samples so that it is becoming a research focus in the current data assimilation field.In this paper,a practical GRAPES ensemble Kalman filter data assimilation system is established and its tentative experiment is carried out.In view of the difficulty of assimilating real observations,the observations are organized into batches that assimilated sequentially in this paper.The Schur product is employed in the horizontal and vertical direction for filtering the error correlation noise and alleviating the matrix singularity problem.The ensemble Kalman filter system consistent with the GRAPES model vertical coordinate and forecast variables has been also established.The generation of ensemble samples considers the spacial correlation of model variables and the correlation among the model variables.The initial perturbation field can be obtained by perturbing the control variable in the 3D-Var system.Through the ideal and actual observation experiments,the EnKF system has been verified.Furthermore,the experiment results of assimilating regional radiosonds show that the reasonable analysis of the GRAPES EnKF is obtained and the GRAPES EnKF system can be practically applied to the operation forecast.The 12 h forecast experiment using the GRAPES EnKF analysis indicates that the harmony of the GRAPES EnKF system is not as good as the GRAPES 3D-Var assimilation system.