非线性滤波方法与陆面数据同化

陆面数据同化研究近几年成为地球科学研究的新兴领域，其中以非线性滤波为代表的数据同化方法发展迅速并得到了广泛应用。在贝叶斯理论框架内，从递推贝叶斯估计理论的角度系统地分析了扩展卡尔曼滤波、无迹卡尔曼滤波、集合卡尔曼滤波、SIR粒子滤波等非线性滤波方法的异同；针对应用比较广泛的集合卡尔曼滤波和SIR粒子滤波应用中存在的问题，论述了几种提高滤波性能的实用方法，如协方差矩阵的Localization方法、协方差矩阵的Inflation方法、双集合卡尔曼滤波方法、扰动集合、扰动大气驱动和模型参数、平方根集合卡尔曼滤波以及粒子滤波算法的改进等。最后总结讨论了各种非线性滤波方法应用中的特点、难点以及各种算法在陆面数据同化中的应用前景和发展方向。

Review of the Nonlinear Filters in the Land Data Assimilation

The land data assimilation research has become the emerging domain in the geoscience, the data assimilation algorithm obtained rapid development and widespread application taking the nonlinear filter as representative's. The extended Kalman filter, unscented Kalman filter, the ensemble Kalman filter and the SIR particle filter are discussed in the Bayesian theory framework from the viewpoint of the recursive Bayesian estimation. Towards the problems in the application of the ensemble Kalman filter and the SIR particle filter, some techniques that can improve the filter performances are also reviewed, such as the covariance localization, the covariance inflation, the double ensemble Kalman filter, the perturbations in the ensembles, the model forcing and parameters, the ensemble square root Kalman filter and the improved variants of the particle filters. The advantages and disadvantages of each filter as well as the applied perspective and the future research directions are discussed.WT5HZ]Key words:WT5BZ]