A new structure for error covariance matrices and their adaptive estimation in EnKF assimilation

Correct estimation of the forecast and observational error covariance matrices is crucial for the accuracy of a data assimilation algorithm. In this article we propose a new structure for the forecast error covariance matrix to account for limited ensemble size and model error. An adaptive procedure combined with a second-order least squares method is applied to estimate the inflated forecast and adjusted observational error covariance matrices. The proposed estimation methods and new structure for the forecast error covariance matrix are tested on the well-known Lorenz-96 model, which is associated with spatially correlated observational systems. Our experiments show that the new structure for the forecast error covariance matrix and the adaptive estimation procedure lead to improvement of the assimilation results. Copyright (c) 2012 Royal Meteorological Society