Estimating the state of large spatio-temporally chaotic systems

E. Ott, B. R. Hunt, I. Szunyogh, A. V. Zimin, Eric Kostelich, M. Corazza, E. Kalnay, D. J. Patil, J. A. Yorke

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


We consider the estimation of the state of a large spatio-temporally chaotic system from noisy observations and knowledge of a system model. Standard state estimation techniques using the Kalman filter approach are not computationally feasible for systems with very many effective degrees of freedom. We present and test a new technique (called a Local Ensemble Kalman Filter), generally applicable to large spatio-temporally chaotic systems for which correlations between system variables evaluated at different points become small at large separation between the points.

Original languageEnglish (US)
Pages (from-to)365-370
Number of pages6
JournalPhysics Letters, Section A: General, Atomic and Solid State Physics
Issue number5
StatePublished - Sep 27 2004


  • Chaos
  • Kalman filter
  • Spatio-temporal chaos
  • Weather forecasting

ASJC Scopus subject areas

  • Physics and Astronomy(all)


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