集合数据同化方法的发展与应用概述

The developments and applications of ensemble-based data assimilation methods

集合数据同化方法具有简洁概念化的公式和应用起来相对容易等优点,因此,它们获得了普及性的应用;近10年来集合数据同化方法已经得到了快速的发展。综述了包括集合卡尔曼滤波(EnKF,Ensemble Kalman Filter)、集合卡尔曼平滑(EnKS,Ensemble Kalman Smoother)、集合方均根滤波(EnSRF,Ensemble Square-Root Filter)和减秩卡尔曼滤波(SEEK,Singular Evolutive Extended Kalman Filter)等集合数据同化方法的研究进展状况。通过与其它数据同化方法的对比,总结出了这些方法的特点,探讨了我国在集合数据同化方法研究中存在的问题并展望了该方法的研究和应用前景。

Ensemble-based data assimilation methods have been developing rapidly for a decade. They have been applied in wide field due to their simple conceptual formulation and ease implementation. The advancement in ensemble-based data assimilation methods are reviewed, including Ensemble Kalman Filter （EnKF）, Ensemble Kalman Smoother （EnKS）, Ensemble Square-Root Filter （EnSRF）, Singular Evolutive Extended Kalman Filter （SEEK）. The main characteristics are outlined though comparing them with other data assimilation methods. Finally, the further developments are discussed.