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arXiv · 1801.08474

Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures

Patrick N. Raanes·Marc Bocquet·Alberto Carrassi
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Abstract

This paper studies multiplicative inflation: the complementary scaling of the state covariance in the ensemble Kalman filter (EnKF). Firstly, error sources in the EnKF are catalogued and discussed in relation to inflation; nonlinearity is given particular attention as a source of sampling error. In response, the "finite-size" refinement known as the EnKF-N is re-derived via a Gaussian scale mixture, again demonstrating how it yields adaptive inflation. Existing methods for adaptive inflation estimation are reviewed, and several insights are gained from a comparative analysis. One such adaptive inflation method is selected to complement the EnKF-N to make a hybrid that is suitable for contexts where model error is present and imperfectly parameterized. Benchmarks are obtained from experiments with the two-scale Lorenz model and its slow-scale truncation. The proposed hybrid EnKF-N method of adaptive inflation is found to yield systematic accuracy improvements in comparison with the existing methods, albeit to a moderate degree.

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Patrick N. Raanes, Marc Bocquet, Alberto Carrassi. 2018-01-25. Adaptive covariance inflation in the ensemble Kalman filter by Gaussian scale mixtures. https://doi.org/10.1002/qj.3386

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