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This article reviews the characteristics of two common localization methods in ensemble Kalman filter (EnKF) systems: covariance localization (CL) and local analysis (LA). To obtain better assimilation results, a new data assimilation system coupled with fuzzy control algorithms is proposed, named CF (covariance fuzzy) and FA (fuzzy analysis). Motivated by fuzzy control concepts that have been developed in the control engineering field for years, the proposed methods improve the normal localization method which behaves like a Gaussian function but reaches zero at finite radius. To explore the effects of the two new algorithms on the background error covariance matrix and the gain matrix, numerical experiments are designed using a classical nonlinear model (i.e., the Lorenz-96 model). The experiments show that the new algorithms can eliminate spurious correlation of the background error covariance matrix. Additionally, with an increase of the assimilation intensity, the gain matrix followed by the update of the new algorithm. Finally, the experimental results demonstrate that the new algorithm has more robust performance than the common algorithm.
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