集合转换卡尔曼滤波同化的一种分析方案

Analysis programme in assimilation of ensemble transform Kalman filter

集合转换卡尔曼滤波在处理非线性观测资料的同化时,通常对非线性观测算子做简单的线性化处理,这会带来较大的舍入误差,降低同化效果。通过对状态变量和观测变量的转换,将观测算子视为预报算子的一部分,对传统的同化算法进行改进,减小了同化中的误差。以典型的Lorenz-96预报模型来验证算法,对于较大模型误差或较强非线性观测算子的情形,结果好于传统的方法。

The 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 simulation results of Lorenz-96 experiments show that 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.