集合转换卡尔曼滤波同化的一种改进

he assimilation of Ensemble Transform Kalman Filter (ETKF) in processing nonlinear observation data has to simply linearize the nonlinear observations, which results in large error and low accuracy. The nonlinear observation operator is treated as a part of the forecast operator through the conversion of the state vector and observation vector. Such improvement to conventional assimilation calculation effectively reduces the assimilative error. The assimilation results of Lorenz-96 experiments show that the analysis error is reduced in the new scheme and the assimilation accuracy is improved, especially for the cases of the forecast model with large error or strongly nonlinear observation operator.