9:30 AM - 9:45 AM
[MGI28-03] Reconditioning the observation error covariance matrix in the local ensemble transform Kalman filter: experiments with the Lorenz-96 model
Keywords:Data assimilation, Observation error correlation, Condition number, Reconditioning
The condition number of the observation error covariance matrix affects to the convergence efficiency when minimizing the cost function when the observation error correlation is considered in the variational data assimilation methods. However, it is still unknown how the condition number affects in the local ensemble transform Kalman filter (LETKF). In this study, we explore the potential impact of the condition number of the observation error covariance matrix in the LETKF. We performed a series of observing system simulation experiments (OSSEs) to account for the observation error correlations in the LETKF with the simple toy Lorenz-96 model using different observation error covariance matrices of the low and high condition numbers. The results show that the LETKF becomes very unstable when the condition number is large. ‘Reconditioning’ is a method to reduce the condition number of a matrix by slightly modifying the original matrix. The experiments using the ‘reconditioned’ observation error covariance matrix show that the LETKF is significantly stabilized, while the impact on the analysis accuracy is minimal.