The Successive Analysis Method in Monte Carlo H Filter and Numerical Experiments

The data assimilation method named "Monte Carlo HiÞ filter" is introduced based on HiÞ filter technique and Monte Carlo method. This method applies to nonlinear systems in condition of lacking the statistical properties of observational errors. For assimilation problem of complicated models with higher dimensions, because there are illusive correlations in the distance due to the statistical properties of correlation for errors are computed by statistical method with finite members of forecasting ensemble, the effective method for improving assimilation qualities is to make a truncation for correlative fields by choosing truncation radius and setting the values of correlative covariance in the distance equal to 0 when members are definite. In addition, these methods can overcome the difficulty of inverse computation about higher-dimensional matrix and reduce computational cost and storage capacity. Therefore, in this paper, the method of successive analysis is used by Monte Carlo HiÞ filter method. It shows the successive analysis is effective and the smallest level factor founded by search method is flow-dependent in Monte Carlo HiÞ filter method.