What the collapse of the ensemble Kalman filter tells us about particle filters

Abstract

The ensemble Kalman filter (EnKF) is a reliable data assimilation tool for high-dimensional meteorological problems. On the other hand, the EnKF can be interpreted as a particle filter, and particle filters (PF) collapse in high-dimensional problems. We explain that these seemingly contradictory statements offer insights about how PF function in certain high-dimensional problems, and in particular support recent efforts in meteorology to 'localize' particle filters, i.e. to restrict the influence of an observation to its neighbourhood.

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Document Details

Document Type
Technical Report
Publication Date
Mar 08, 2017
Accession Number
AD1119563

Entities

People

  • Chris Snyder
  • Daniel Hodyss
  • Matthias Morzfeld

Organizations

  • United States Naval Research Laboratory

Tags

Communities of Interest

  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Assimilation
  • Computational Complexity
  • Equations
  • Filters
  • Filtration
  • Geophysics
  • Kalman Filters
  • Mathematical Filters
  • Mathematics
  • Meteorology
  • Monte Carlo Method
  • Random Variables
  • Sequential Monte Carlo Methods
  • Square Roots
  • Statistical Algorithms
  • Statistics

Fields of Study

  • Environmental science

Readers

  • Adaptive Control and Estimation with Uncertainty in Dynamic Systems.
  • Atmospheric Science/Meteorology
  • Theoretical Analysis.