What is the ensemble {Kalman} filter and how well does it work?

  • Gillijns S
  • Mendoza O
  • Chandrasekar J
 et al. 
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Abstract

In this paper we described the ensemble Kalman filter algorithm. This approach to nonlinear Kalman filtering is a Monte Carlo procedure, which has been widely used in weather forecasting applications. Our goal was to apply the ensemble Kalman filter to representative examples to quantify the tradeoff between estimation accuracy and ensemble size. For all of the linear and nonlinear examples that we considered, the ensemble Kalman filter worked successfully once a threshold ensemble size was reached. In future work we will investigate the factors that determine this threshold value

Author-supplied keywords

  • Covariance matrix
  • Gaussian noise
  • Jacobian matrices
  • Kalman filters
  • Linear systems
  • Monte Carlo
  • Monte Carlo methods
  • Nonlinear dynamical systems
  • Nonlinear systems
  • Particle filters
  • Riccati equations
  • State estimation
  • Weather forecasting
  • ensemble Kalman filter algorithm
  • ensemble size
  • estimation accuracy
  • estimation theory
  • nonlinear Kalman filtering
  • nonlinear filters

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Authors

  • S Gillijns

  • O B Mendoza

  • J Chandrasekar

  • B L R De Moor

  • D S Bernstein

  • A Ridley

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