Localizing the Ensemble Kalman Particle Filter

TL;DR

This paper introduces two localized algorithms based on the Ensemble Kalman Particle Filter to improve data assimilation in high-resolution, non-linear, and non-Gaussian geophysical models, maintaining scalability and diversity.

Contribution

The paper presents novel localized algorithms that combine EnKF and PF, addressing localization challenges in non-Gaussian, high-resolution models.

Findings

Algorithms outperform local EnKF in experiments

Capture non-Gaussian features like wet/dry area locations

Maintain scalability and sample diversity

Abstract

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 (NWP). 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 (EnKPF), 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 EnKFs 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.