Examination of situation-dependent background error covariances at the convective scale in the context of the ensemble kalman filter

Kao Shen Chung, Weiguang Chang, Luc Fillion, Monique Tanguay

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

A high-resolution ensemble Kalman filter (HREnKF) system at the convective scale has been developed based on the Canadian Meteorological Center's operational global ensemble Kalman filter (EnKF) system. This study focuses on the very early stage of transition from purely homogeneous isotropic background error correlations to situation-dependent correlations. It has been found that forecast error structures can develop situation-dependent features in as little as 15 min. Furthermore, the dynamic and thermodynamic variables require different periods of time to build up their own forecast error structures. Differences in these structures between regions with and without precipitation are also investigated. An examination of temperature tendencies revealed that physical processes are as important as dynamical forcing in determining the structure of convective-scale errors structures, and that once physical processes become active, these structures change rapidly before the onset of precipitation. This study is intended to be the basis for a systematic exploration in the near future of the usefulness of theHREnKF systemin assimilating high-density observations such as radar data.

Original languageEnglish
Pages (from-to)3369-3387
Number of pages19
JournalMonthly Weather Review
Volume141
Issue number10
DOIs
StatePublished - 2013

Keywords

  • Data assimilation
  • Ensembles
  • Kalman filters

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