Study Of Ensemble Kalman Filter

"The blending of the existing noisy observation irregularly distributed in space and time into numerical models based on the physical laws that govern atmospheric flows became known as model assimilation of the data or data assimilation". It has become more and more important in the meteorology and oceanography because of the increasing observation and computation ability. In the recent years, a new data assimilation method called ensemble Kalman filter (EnKF) has aroused people's attention. The available result indicates that EnKF owns the potential ability of becoming an operational data assimilation method. This article simply reviews the developmental history and study actuality of this advanced data assimilation method.The main virtue of the EnKF is that its background error covariance is flow-dependent. But the flow-dependent background error covariance is not easy to obtain in the operational variation analysis at present. But the EnKF aIso has drawbacks, for example, filter divergence, unbalance between the analysis variables et al. A serial experiment using the shallow water equation and the real atmosphere model (MM5) were carried out in order to study the EnKF theory, including the flow-dependent background error covariance in EnKF, the ensemble number's effect on the EnKF system and the comparison between EnKF and 3D-VAR. From the experiments it is seen that: the EnKF is superior to the 3D-VAR and its error is convergent. The main reason is that the 3D-VAR can't update its background error variance but the EnKF's background error covariance is flow-dependent. As the number of ensemble increasing the EnKF's analysis quality is improved because the increasing ensemble numbers will reduce the spurious error correlations.