An iterative ensemble Kalman filter in the presence of additive model error. (1st February 2018)

• Record Type: Journal Article
• Authors: Sakov, Pavel; Haussaire, Jean‐Matthieu; Bocquet, Marc
• Is Part Of: Quarterly journal of the Royal Meteorological Society. Volume 144:Number 713(2018)
• Page Start: 1297
• Page End: 1309
• Publication Date: 2018-02-01
• Subjects: ensemble Kalman filter -- model error -- Gauss–Newton minimization -- iterative ensemble Kalman filter; Meteorology -- Periodicals; 551.5
• DOI: 10.1002/qj.3213
• Languages: English
• ISSNs: 0035-9009

Abstract

Abstract : The iterative ensemble Kalman filter (IEnKF) in a deterministic framework was introduced in Sakov et al. [Mon. Wea. Rev. 140: 1988–2004 (2012 )] 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. The new method called IEnKF‐Q conducts a Gauss–Newton minimization in ensemble space. It combines the propagated analysed ensemble anomalies from the previous cycle and model noise ensemble anomalies into a single ensemble of anomalies, and by doing so takes an algebraic form similar to that of the IEnKF. The performance of the IEnKF‐Q is tested in a number of experiments with the Lorenz'96 model, which show that the method consistently outperforms both the EnKF and the IEnKF naively modified to accommodate additive model noise. Abstract : The iterative ensemble Kalman filter (IEnKF) intended for perfect‐model chaotic systems is extended to the IEnKF‐Q which additionally handles imperfect models. The IEnKF‐Q is tested with the Lorenz'96 model in various noise and nonlinearity conditions and compared with the ensemble Kalmanfilter (EnKF) and straightforward but naive extensions of the IEnKF to noisy models. In line with the theory, the IEnKF‐Q is shown to systematically outperform the EnKF and IEnKF variants.