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

doi:10.1016/j.physleta.2004.08.004

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

Mathematical and Statistical Sciences, School of (SoMSS)

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

Abstract

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 language	English (US)
Pages (from-to)	365-370
Number of pages	6
Journal	Physics Letters, Section A: General, Atomic and Solid State Physics
Volume	330
Issue number	5
DOIs	https://doi.org/10.1016/j.physleta.2004.08.004
State	Published - Sep 27 2004

Keywords

Chaos
Kalman filter
Spatio-temporal chaos
Weather forecasting

ASJC Scopus subject areas

Physics and Astronomy(all)

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10.1016/j.physleta.2004.08.004

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title = "Estimating the state of large spatio-temporally chaotic systems",

abstract = "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.",

keywords = "Chaos, Kalman filter, Spatio-temporal chaos, Weather forecasting",

author = "E. Ott and Hunt, {B. R.} and I. Szunyogh and Zimin, {A. V.} and Eric Kostelich and M. Corazza and E. Kalnay and Patil, {D. J.} and Yorke, {J. A.}",

note = "Funding Information: This work was supported by the W.M. Keck Foundation, by a James S. McDonnell 21st Century Research Award, by the Office of Naval Research (Physics), by the Army Research Office, and by the National Science Foundation (Grant 0104087 and Grant PHYS 0098632).",

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language = "English (US)",

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T1 - Estimating the state of large spatio-temporally chaotic systems

AU - Ott, E.

AU - Hunt, B. R.

AU - Szunyogh, I.

AU - Zimin, A. V.

AU - Kostelich, Eric

AU - Corazza, M.

AU - Kalnay, E.

AU - Patil, D. J.

AU - Yorke, J. A.

N1 - Funding Information: This work was supported by the W.M. Keck Foundation, by a James S. McDonnell 21st Century Research Award, by the Office of Naval Research (Physics), by the Army Research Office, and by the National Science Foundation (Grant 0104087 and Grant PHYS 0098632).

PY - 2004/9/27

Y1 - 2004/9/27

N2 - 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.

AB - 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.

KW - Chaos

KW - Kalman filter

KW - Spatio-temporal chaos

KW - Weather forecasting

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VL - 330

SP - 365

EP - 370

JO - Physics Letters, Section A: General, Atomic and Solid State Physics

JF - Physics Letters, Section A: General, Atomic and Solid State Physics

IS - 5

ER -

Estimating the state of large spatio-temporally chaotic systems

Abstract

Keywords

ASJC Scopus subject areas

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