Model‐Space Localization in Serial Ensemble Filters

Abstract

Abstract Ensemble‐based data assimilation systems typically use 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 nonlocal observations, such as satellite radiances, model‐space localization can be superior. This paper demonstrates a new method for performing model‐space localization in serial ensemble filters using the linearized observation operators (or Jacobians). 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 Ensemble Variational system (without outer loops or extra balance constraints) while increasing the runtime compared to the filter with observation‐space localization by a factor between 2 and 8, depending on how sparse the Jacobian matrices are. The results are also similar to another approach to model‐space localization in ensemble filters: ensemble Kalman filter with modulated ensembles.

Keywords

• ensemble data assimilation
• localization
• background error covariances
• EnKF