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Entropy, Closures and Subgrid Modeling
Open AccessArticle

Comparison of Statistical Dynamical, Square Root and Ensemble Kalman Filters

1
Centre for Australian Climate & Weather Research, Bureau of Meteorology, Docklands, Australia
2
Centre for Australian Climate & Weather Research, CSIRO Marine & Atmospheric Research, Aspendale, Australia
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Author to whom correspondence should be addressed.
Entropy 2008, 10(4), 684-721; https://doi.org/10.3390/e10040684
Received: 30 May 2008 / Accepted: 31 October 2008 / Published: 20 November 2008
We present a statistical dynamical Kalman filter and compare its performance to deterministic ensemble square root and stochastic ensemble Kalman filters for error covariance modeling with applications to data assimilation. Our studies compare assimilation and error growth in barotropic flows during a period in 1979 in which several large scale atmospheric blocking regime transitions occurred in the Northern Hemisphere. We examine the role of sampling error and its effect on estimating the flow dependent growing error structures and the associated effects on the respective Kalman gains. We also introduce a Shannon entropy reduction measure and relate it to the spectra of the Kalman gain. View Full-Text
Keywords: Data assimilation; Entropy; Turbulence closures Data assimilation; Entropy; Turbulence closures
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O’Kane, T.J.; Frederiksen, J.S. Comparison of Statistical Dynamical, Square Root and Ensemble Kalman Filters. Entropy 2008, 10, 684-721.

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