Examination of Situation-Dependent Background Error Covariances at the Convective Scale in the Context of the Ensemble Kalman Filter

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 b...

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Bibliographic Details
Published in:Monthly weather review 2013-10, Vol.141 (10), p.3369-3387
Main Authors: CHUNG, Kao-Shen, WEIGUANG CHANG, FILLION, Luc, TANGUAY, Monique
Format: Article
Language:English
Subjects:
Algorithms
Climatology
Correlation
Correlation analysis
Data assimilation
Dynamical systems
Dynamics
Earth, ocean, space
Errors
Exact sciences and technology
External geophysics
Kalman filters
Localization
Meteorology
Precipitation
Radar
Radar data
Structures
Studies
Weather
Weather forecasting
Wind
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Summary: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 the HREnKF system in assimilating high-density observations such as radar data.
ISSN:0027-0644
1520-0493
DOI:10.1175/MWR-D-12-00353.1