The background error covariance matrix based on properties of the ensemble prediction statistics play an important role in the ensemble Kalman filter data assimilation. However, data assimilation divergence occurs from the inaccurate estimate of the covariance matrix and the limited ensembles. In this study, based on an ensemble time-local H-infinity filter which inflates the eigenvalues of the analysis error covariance matrix, a new data assimilation filter method is proposed, referred to as the inflation transform matrix eigenvalues algorithm, in order to improve properties of the estimation. The properties of data assimilation is improved in the framework of ensemble filters according to the min-max criterion of robust filtering theory. Using the nonlinear Lorenz-96 chaos system, we investigate how the ensemble time-local H-infinity filter methods impacts the robustness of the assimilation systems under the selected change conditions, such as initial background conditions, force parameters, and performance level coefficients. It is show that the ensemble time-local H∞ filter has good robustness to the change of above parameters. Compared with traditional filter methods, robust filter methods can improve the assimilation effect.
BAI Yulong
,
ZHANG Zhuanhua
,
YOU Yuanhong
,
LIU Yingjuan
. A New Data Assimilation Method Based on Robust Ensemble Filter[J]. Plateau Meteorology, 2017
, 36(4)
: 1052
-1059
.
DOI: 10.7522/j.issn.1000-0534.2016.00072
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