Estimating model-error covariances for application to atmospheric data assimilation

Abstract

The Kalman filter theory assumes that the model error is additive white (in time) noise, which permits the separation of the model and predictability error. Progress can be made by assuming that the model-error statistics are homogeneous and stationary, an assumption that is more justifiable for the model-error statistics than for the forecast-error statsitics. A methodology for estimating the homogeneous, stationary component of the model-error covariance is discussed and tested in a simple data-assimilation system. -from Author