Particle filters for high-dimensional geoscience applications: a review

TL;DR

This review discusses recent advances in particle filters for high-dimensional geoscience applications, highlighting new methods that improve efficiency and their potential to become mainstream in weather prediction.

Contribution

It provides a comprehensive overview of recent developments, new ideas, and unifications in particle filter methods tailored for nonlinear geoscience state estimation.

Findings

Particle filters are becoming competitive with current weather prediction methods.

Recent techniques like localisation and adaptive resampling improve efficiency.

Hybrid methods combining particle filters with other approaches show promise.

Abstract

Particle filters contain the promise of fully nonlinear data assimilation. They have been applied in numerous science areas, but their application to the geosciences has been limited due to their inefficiency in high-dimensional systems in standard settings. However, huge progress has been made, and this limitation is disappearing fast due to recent developments in proposal densities, the use of ideas from (optimal) transportation, the use of localisation and intelligent adaptive resampling strategies. Furthermore, powerful hybrids between particle filters and ensemble Kalman filters and variational methods have been developed. We present a state of the art discussion of present efforts of developing particle filters for highly nonlinear geoscience state-estimation problems with an emphasis on atmospheric and oceanic applications, including many new ideas, derivations, and unifications, highlighting hidden connections, and generating a valuable tool and guide for the community. Initial experiments show that particle filters can be competitive with present-day methods for numerical weather prediction suggesting that they will become mainstream soon.