Well-posedness and accuracy of the ensemble Kalman filter in discrete and continuous time

By David Kelly, Kody J. H. Law, Andrew M. Stuart. 2013

142 citations recorded by the source corpus.

The ensemble Kalman filter (EnKF) is a method for combining a dynamical model with data in a sequential fashion. Despite its widespread use, there has been little analysis of its theoretical properties. Many of the algorithmic innovations associated with the filter, which are required to make a useable algorithm in practice, are derived in an ad hoc fashion. The aim of this paper is to initiate the development of a systematic analysis of the EnKF, in particular to do so for small ensemble size. The perspective is to view the method as a state estimator, and not as an algorithm which approximates the true filtering distribution. The perturbed observation version of the algorithm is studied, without and with variance inflation. Without variance inflation well-posedness of the filter is established; with variance inflation accuracy of the filter, with respect to the true signal underlying the data, is established. The algorithm is considered in discrete time, and also for a continuous time limit arising when observations are frequent and subject to large noise. The underlying dynamical model, and assumptions about it, is sufficiently general to include the Lorenz '63 and '96 models, together with the incompressible Navier–Stokes equation on a two-dimensional torus. The analysis is limited to the case of complete observation of the signal with additive white noise. Numerical results are presented for the Navier–Stokes equation on a two-dimensional torus for both complete and partial observations of the signal with additive white noise.

Subfields: Artificial Intelligence, Atmospheric Science, Oceanography

View Paper: https://www.semanticscholar.org/paper/4b1deffbbb6dd53e69874e21a304cf5e2c5dc784

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In Artificial Intelligence

  1. Kody J. H. Law
  2. Konstantinos C. Zygalakis
  3. Andrew M. Stuart
  4. Masoumeh Dashti
  5. Mark A. Girolami
  6. G. Em Karniadakis
  7. Sari Lasanen
  8. Liang Yan

In Atmospheric Science

  1. Andrew M. Stuart
  2. Konstantinos C. Zygalakis
  3. Kody J. H. Law
  4. Heng Xiao

In Oceanography

  1. Konstantinos C. Zygalakis
  2. Kody J. H. Law
  3. Andrew M. Stuart

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