An iterative ensemble Kalman filter in presence of additive model error

TL;DR

This paper introduces the IEnKF-Q, an extension of the iterative ensemble Kalman filter that accounts for additive model errors, demonstrating improved performance over existing methods in nonlinear systems.

Contribution

The paper generalizes the IEnKF to handle additive model errors using a Gauss-Newton minimisation in ensemble space, enhancing its applicability and accuracy.

Findings

IEnKF-Q outperforms EnKF and naive IEnKF with additive noise.

The method is effective in nonlinear Lorenz-96 model experiments.

It provides a more accurate state estimation in the presence of model errors.

Abstract

The iterative ensemble Kalman filter (IEnKF) in a deterministic framework was introduced in Sakov et al. (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 generalise the IEnKF to the case of imperfect model with additive model error. The new method called IEnKF-Q conducts a Gauss-Newton minimisation 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.