Localizing the Ensemble Kalman Particle Filter

• Main Authors: Sylvain Robert, Hans R. Künsch
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2017-01-01
• Series: Tellus: Series A, Dynamic Meteorology and Oceanography
• Subjects: ensemble Kalman filter; particle filter; data assimilation; non-linear filtering; localization
• Online Access: http://dx.doi.org/10.1080/16000870.2017.1282016

Ensemble methods such as the Ensemble Kalman Filter (EnKF) are widely used for data assimilation in large-scale geophysical applications, as for example in numerical weather prediction. There is a growing interest for physical models with higher and higher resolution, which brings new challenges for data assimilation techniques because of the presence of non-linear and non-Gaussian features that are not adequately treated by the EnKF. We propose two new localized algorithms based on the Ensemble Kalman Particle Filter, a hybrid method combining the EnKF and the Particle Filter (PF) in a way that maintains scalability and sample diversity. Localization is a key element of the success of EnKF in practice, but it is much more challenging to apply to PFs. The algorithms that we introduce in the present paper provide a compromise between the EnKF and the PF while avoiding some of the problems of localization for pure PFs. Numerical experiments with a simplified model of cumulus convection based on a modified shallow water equation show that the proposed algorithms perform better than the local EnKF. In particular, the PF nature of the method allows to capture non-Gaussian characteristics of the estimated fields such as the location of wet and dry areas.