Application Of Ensemble Kalman Filter In Data Assimilation

The accuracy of modem numerical prediction mainly depends on the exact model that can simulate the synoptic course and the initial field that can describe the initial time. Recently the development of numerical models has run to perfection, so it is most important to obtain the exact initial field. The Four-Dimensional variational data assimilation (4DVAR) has demonstrated the remarkable superiority in meteorological data assimilation. 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 method has an important scientific and practical meaning.The main virtue of the EnKF is that its background error covariance is flow-dependent. The EnKF theory and the realization were in detail introduced in this paper. And in this context conventional and non-conventional are assimilated by EnKF assimilation system based on MM5 model. The experiment has been performed with the heavy rain cases occurred on July 9th, 2005 and June 14th ,2002.The effect of EnKF assimilation system was researched. At last the qualitative and quota comparision were carried on between EnKF and 4DVAR. It is found that the result of using EnKF is better than using 4DVAR in the choosing case at the same condition. For the non-conventional there was not obviously diffirence between EnKF and 4DVAR because the cloud-derived wind data itself.