Abstract:Ensemble-based data assimilation systems typically use background error covariance localization to dampen spurious correlations associated with sampling error while increasing the rank of the covariance estimate. Variational methods use model-space localization, in which localization is applied to ensemble estimates of covariances between model variables and is based on distances between those variables, while ensemble filters apply observation-space localization to estimates of model-observation covariances, based on distances between model variables and observations. It has been shown that for non-local observations, such as satellite radiances, model-space localization can be superior.
In this talk we will present a method for performing model-space localization in serial ensemble filters using the linearized observation operators. Results of radiance-only assimilation in a global forecast system show the benefit of using model-space localization relative to observation-space localization. The serial ensemble square root filter with vertical model-space localization gives results similar to those of the EnVar system (without outer loops or extra balance constraints), and to ensemble Kalman filter using modulated ensembles to emulate model-space covariance localization.
