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Particle filters contain the promise of fully nonlinear data assimilation.They have been applied in numerous science areas, but their application to thegeosciences has been limited due to their inefficiency in high-dimensionalsystems in standard settings. However, huge progress has been made, and thislimitation is disappearing fast due to recent developments in proposaldensities, the use of ideas from (optimal) transportation, the use oflocalisation and intelligent adaptive resampling strategies. Furthermore,powerful hybrids between particle filters and ensemble Kalman filters andvariational methods have been developed. We present a state of the artdiscussion of present efforts of developing particle filters for highlynonlinear geoscience state-estimation problems with an emphasis on atmosphericand oceanic applications, including many new ideas, derivations, andunifications, highlighting hidden connections, and generating a valuable tooland guide for the community. Initial experiments show that particle filters canbe competitive with present-day methods for numerical weather predictionsuggesting that they will become mainstream soon.

Particle filters for high-dimensional geoscience applications: a review