Model-space localization in serial ensemble filters

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.