Stochastic parametrization: An alternative to inflation in ensemble Kalman filters

Abstract

AbstractWe investigate the application of a stochastic dynamical model in ensemble Kalman filter methods. Ensemble Kalman filters are very popular in data assimilation because of their ability to handle the filtering of high‐dimensional systems with reasonably small ensembles (especially when they are accompanied with so‐called localization techniques). The stochastic framework presented here relies on location uncertainty principles that model the effects of the model errors on the large‐scale flow components. The experiments carried out on the surface quasi‐geostrophic model with the localized square‐root filter demonstrate two significant improvements compared with the deterministic framework. First, as the uncertainty is a priori built into the model through the stochastic parametrization, there is no need for ad hoc variance inflation or perturbation of the initial condition. Second, it yields better mean‐square‐error results than the deterministic ones.