Ensemble Kalman filtering with residual nudging

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

Covariance inflation and localisation are two important techniques that are used to improve the performance of the ensemble Kalman filter (EnKF) by (in effect) adjusting the sample covariances of the estimates in the state space. In this work, an additional auxiliary technique, called residual nudging, is proposed to monitor and, if necessary, adjust the residual norms of state estimates in the observation space. In an EnKF with residual nudging, if the residual norm of an analysis is larger than a pre-specified value, then the analysis is replaced by a new one whose residual norm is no larger than a pre-specified value. Otherwise, the analysis is considered as a reasonable estimate and no change is made. A rule for choosing the pre-specified value is suggested. Based on this rule, the corresponding new state estimates are explicitly derived in case of linear observations. Numerical experiments in the 40-dimensional Lorenz 96 model show that introducing residual nudging to an EnKF may improve its accuracy and/or enhance its stability against filter divergence, especially in the small ensemble scenario.
Original languageEnglish (US)
Pages (from-to)17130
JournalTellus A
Volume64
Issue number1
DOIs
StatePublished - Oct 3 2012

ASJC Scopus subject areas

  • Oceanography
  • Atmospheric Science

Fingerprint Dive into the research topics of 'Ensemble Kalman filtering with residual nudging'. Together they form a unique fingerprint.

Cite this