Environmental and Ecological Statistics

, Volume 15, Issue 1, pp 101–110

A two-stage ensemble Kalman filter for smooth data assimilation

Article

DOI: 10.1007/s10651-007-0033-0

Cite this article as:
Johns, C.J. & Mandel, J. Environ Ecol Stat (2008) 15: 101. doi:10.1007/s10651-007-0033-0

Abstract

The ensemble Kalman Filter (EnKF) applied to a simple fire propagation model by a nonlinear convection-diffusion-reaction partial differential equation breaks down because the EnKF creates nonphysical ensemble members with large gradients. A modification of the EnKF is proposed by adding a regularization term that penalizes large gradients. The method is implemented by applying the EnKF formulas twice, with the regularization term as another observation. The regularization step is also interpreted as a shrinkage of the prior distribution. Numerical results are given to illustrate success of the new method.

Keywords

Data assimilationEnsemble Kalman filterState-space modelPenaltyTikhonov regularizationWildfireConvection-reaction-diffusionShrinkageBayesian

Copyright information

© Springer Science+Business Media, LLC 2007

Authors and Affiliations

  1. 1.Milliman, Inc.DenverUSA
  2. 2.Department of Mathematical SciencesUniversity of Colorado at Denver and Health Sciences CenterDenverUSA