Accounting for the Horizontal Observation Error Correlations in the Local Ensemble Transform Kalman Filter: Observing System Simulation Experiments

It is natural that the observation errors are correlated if measured with the same instrument, such as radiosondes, radars, and satellite sensors. Radiosonde observations would have the error correlations in the vertical. Satellite radiances would have the horizontal and inter-channel error correlations. However, in the operational data assimilation systems, the observation errors are usually assumed to be uncorrelated for simplicity and computational efficiency.

 In this study, we explore the impact of considering spatially-correlated observation errors in the local ensemble transform Kalman filter (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 (L96) and the realistic Nonhydrostatic ICosahedral Atmospheric Model (NICAM). For each of L96 and NICAM experiments, we performed 4 experiments to compare: (1) assuming diagonal observation error covariance matrix R, (2) using inflated diagonal R, (3) using diagonal R with thinned observations, and (4) using full R with non-zero off-diagonal terms. The results showed that the analysis was improved by taking into account the observation error correlations in both systems. Following the OSSEs, we extend to the real-world observation data. We first estimate the full R matrix using the Desroziers diagnostic equations. We will present our latest results at the time of the conference.