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Data assimilation has been developed into an effective technology that can utilize a large number of multisource unconventional data. It cannot only provide the initial field for the ocean numerical prediction model, but also construct the ocean reanalysis datasets and provide the design basis for the ocean observation plan. In data assimilation, the estimation of the observation error is of paramount importance, because the quality of the analysis depends on it. In general, the observation error covariance matrix is diagonal or assumed to be diagonal, which means that the observation errors are independent from one another. However, there are indeed correlations in the observation errors. A diagnostic method has been developed, which can estimate a correlated and more accurate observation error covariance matrix. The proposed method combines an ensemble squareroot Kalman filter with the diagnostic method, providing an estimation of the observation error covariance matrix. In order to test the performance of the method, the numerical experiments are performed with the Lorenz 96 model and a Shallow water model. The more accurate observation error covariance matrix can be obtained to use in ensemble square-root Kalman filter by using the new method. We could find using the estimated correlated observation error in the data assimilation improves the analysis. AbstrAct Shaodong Zang1, Jichao Wang1*

Considering the impact of observation error correlation in ensemble square-root Kalman filter