Collection Of The Kalman Filter Data Assimilation Program Design And Research

""The blending of the existing noisy observations 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 observations 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. So studying the EnKF theory has an important scientific and practical meaning.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 also 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 (GRAPES) 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 et al. the unbalance in traditional EnKF was found in experiments and the importance of physical law constraint in EnKF was presented. The conception of full constraint EnKF and the semi-constrained EnKF (SCKF) scheme was introduced. The SCKF's feasibility and validity has been tested using the barotropic model.The following are the main conclusions and results:(1) The result of the linear shallow water equation indicates that the EnKF is superior to the 3D-var and its error is convergent. The main reason is that the 3dvar can't update its background error covariance but the EnKF's background error covariance is flow-dependent.(2) As the number of ensemble increasing the EnKF's analysis quality is improved because theincreasing ensemble numbers will reduce the spurious error correlations. And the experiment indicates that as the ensemble numbers increasing EnKF's error correlation convergence to the KF's one(3) When the ensemble number is small, using a cut-off radius can improve the assimilation quality and super cut-off radius exists.(4) Using the established EnKF system combined with the real atmosphere model GRAPES studies the EnKF's background error covariance. Two points were choice in the experiment near each other but in the different flow. The experiment indicates that the EnKF's error correlations are flow-dependent. And the error variance's moving direction also indicate flow-dependent(5) The comparison between GRAPES 3dvar and EnKF's single point experiment indicates that the 3D-VAR's analysis in flow-independent because its assumption of the first guess error covariance is isotropic and homogeneous. But the EnKF's analysis is flow-dependent for the flow-dependent background error covariance.(6) Aiming at the analysis variable's unbalance problem, the conception of full constraint EnKF was introduced. Based on this theory, the semi-constraint EnKF scheme was designed. The data assimilation ability of uni-variable EnKF (UVKF), the traditional multi-variable EnKF (MVKF) and the semi-constraint EnKF (SCKF) was compared using the barotropic model. The experiment showed both MVKF and SCKF can improve the wind analysis in the condition of only height observation utility. But large spurious convergence and divergence was brought by MVKF and SCKF can avoid it...