论文部分内容阅读
在集合卡尔曼滤波方法中,根据预报集合的统计特性提供的预报误差协方差矩阵对资料同化起决定性作用.但协方差矩阵低估会引起资料同化滤波发散问题.通过将集合转换卡尔曼滤波方法和时间局地化的H∞滤波方法相结合,提出一种基于鲁棒集合滤波思想的资料同化方法,放大转移矩阵的特征值,改善估计效果.主要思路是在集合滤波的框架下,按照鲁棒滤波的最小最大准则,实现同化系统性能的改进.利用非线性Lorenz-96混沌系统,考察集合时间局地化的H∞滤波在系统参数变化时,对同化系统鲁棒性的影响.结果表明:集合时间局地化的H∞滤波对系统参数变化具有很好的鲁棒性;与传统的滤波方法相比,鲁棒滤波方法提高了同化的效果.“,”The background error covariance matrix based on properties of the ensemble prediction statistics play an important role in the ensemble Kalman filter data assimilation.However,data assimilation divergence occurs from the inaccurate estimate of the covariance matrix and the limited ensembles.In this study,based on an ensemble time-local H-infinity filter which inflates the eigenvalues of the analysis error covariance matrix,a new data assimilation filter method is proposed,referred to as the inflation transform matrix eigenvalues algorithm,in order to improve properties of the estimation.The properties of data assimilation is improved in the framework of ensemble filters according to the min-max criterion of robust filtering theory.Using the nonlinear Lorenz-96 chaos system,we investigate how the ensemble time-local H-infinity filter methods impacts the robustness of the assimilation systems under the selected change conditions,such as initial background conditions,force parameters,and performance level coefficients.It is show that the ensemble time-local H∞ filter has good robustness to the change of above parameters.Compared with traditional filter methods,robust filter methods can improve the assimilation effect.