A Particle Filter-based Adaptive Inflation Scheme for the Ensemble Kalman Filter

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An adaptive covariance inflation scheme is proposed for the ensemble Kalman filter (EnKF) to mitigate for the loss of ensemble variance. Adaptive inflation methods are mostly based on a Bayesian approach, which considers the inflation factor as a random variable with a given prior probability distribution, and then combines it with the inflation likelihood through Bayes’ rule to obtain its posterior distribution. In this work, we introduce a numerical implementation of this generic Bayesian approach that uses a particle filter (PF) to compute a Monte Carlo approximation of the inflation posterior distribution. To alleviate the sample attrition issue, the proposed PF employs an artificial dynamical model for the inflation factor based on the well-known smoothing-kernel West and Liu model. The positivity constraint on the inflation factor is further imposed through an inverse-Gamma transition density, whose parameters suggest analytical expressions. The resulting PF-EnKF scheme is straightforward to implement, and can use different number of particles in its EnKF and PF components. Numerical experiments are conducted with the Lorenz-96 model to demonstrate the effectiveness of the proposed method under various experimental scenarios.
Original languageEnglish (US)
JournalQuarterly Journal of the Royal Meteorological Society
StatePublished - Jan 24 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-04-23
Acknowledgements: Research reported in this publication was supported by KingAbdullah University of Science and Technology (KAUST). The authors would like to thank the anonymous reviewers and Franc ̧oisDesbouvries for the fruitful discussions about the resampling in the particle filter.


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