Efficient data assimilation for spatiotemporal chaos: A local ensemble transform Kalman filter

Brian R. Hunt, Eric Kostelich, Istvan Szunyogh

Research output: Contribution to journalArticle

704 Scopus citations

Abstract

Data assimilation is an iterative approach to the problem of estimating the state of a dynamical system using both current and past observations of the system together with a model for the system's time evolution. Rather than solving the problem from scratch each time new observations become available, one uses the model to "forecast" the current state, using a prior state estimate (which incorporates information from past data) as the initial condition, then uses current data to correct the prior forecast to a current state estimate. This Bayesian approach is most effective when the uncertainty in both the observations and in the state estimate, as it evolves over time, are accurately quantified. In this article, we describe a practical method for data assimilation in large, spatiotemporally chaotic systems. The method is a type of "ensemble Kalman filter", in which the state estimate and its approximate uncertainty are represented at any given time by an ensemble of system states. We discuss both the mathematical basis of this approach and its implementation; our primary emphasis is on ease of use and computational speed rather than improving accuracy over previously published approaches to ensemble Kalman filtering. We include some numerical results demonstrating the efficiency and accuracy of our implementation for assimilating real atmospheric data with the global forecast model used by the US National Weather Service.

Original languageEnglish (US)
Pages (from-to)112-126
Number of pages15
JournalPhysica D: Nonlinear Phenomena
Volume230
Issue number1-2
DOIs
StatePublished - Jun 2007

Keywords

  • Data assimilation
  • Ensemble Kalman filtering
  • Spatiotemporal chaos
  • State estimation

ASJC Scopus subject areas

  • Statistical and Nonlinear Physics
  • Mathematical Physics
  • Condensed Matter Physics
  • Applied Mathematics

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