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Particle filters for high-dimensional geoscience applications: A review.

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Area of Science:

  • Geosciences
  • Atmospheric Science
  • Oceanic Science

Background:

  • Particle filters offer potential for fully nonlinear data assimilation.
  • Application in high-dimensional geoscience systems was limited by inefficiency.
  • Recent advancements address these limitations, improving applicability.

Purpose of the Study:

  • To present a state-of-the-art discussion on developing particle filters for high-dimensional nonlinear geoscience state-estimation.
  • To emphasize atmospheric and oceanic applications.
  • To provide a guide with new ideas, derivations, and unifications.

Main Methods:

  • Developments in proposal densities.
  • Application of optimal transportation concepts.
  • Implementation of localization and adaptive resampling strategies.
  • Hybridization with ensemble Kalman filters and variational methods.

Main Results:

  • Significant progress in particle filter efficiency for high-dimensional systems.
  • Demonstrated competitiveness with current numerical weather prediction methods.
  • Successful unification of various particle filter developments.

Conclusions:

  • Particle filters are rapidly overcoming previous limitations in high-dimensional geoscience.
  • They are becoming a viable and competitive tool for state estimation.
  • Particle filters are poised for mainstream adoption in fields like numerical weather prediction.