An iterative ensemble Kalman filter in the presence of additive model error

The iterative ensemble Kalman filter (IEnKF) in a deterministic framework was introduced in Sakov et al. [Mon. Wea. Rev. 140: 1988–2004 ( )] to extend the ensemble Kalman filter (EnKF) and improve its performance in mildly up to strongly nonlinear cases. However, the IEnKF assumes that the model is perfect. This assumption simplified the update of the system at a time different from the observation time, which made it natural to apply the IEnKF for smoothing. In this study, we generalize the IEnKF to the case of an imperfect model with additive model error.

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