This article is
- freely available
Comparison of Statistical Dynamical, Square Root and Ensemble Kalman Filters
Centre for Australian Climate & Weather Research, Bureau of Meteorology, Docklands, Australia
Centre for Australian Climate & Weather Research, CSIRO Marine & Atmospheric Research, Aspendale, Australia
* Author to whom correspondence should be addressed.
Received: 30 May 2008 / Accepted: 31 October 2008 / Published: 20 November 2008
Abstract: 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.
Keywords: Data assimilation; Entropy; Turbulence closures
Article StatisticsClick here to load and display the download statistics.
Notes: Multiple requests from the same IP address are counted as one view.
Cite This Article
MDPI and ACS Style
O’Kane, T.J.; Frederiksen, J.S. Comparison of Statistical Dynamical, Square Root and Ensemble Kalman Filters. Entropy 2008, 10, 684-721.
O’Kane TJ, Frederiksen JS. Comparison of Statistical Dynamical, Square Root and Ensemble Kalman Filters. Entropy. 2008; 10(4):684-721.
O’Kane, Terence J.; Frederiksen, Jorgen S. 2008. "Comparison of Statistical Dynamical, Square Root and Ensemble Kalman Filters." Entropy 10, no. 4: 684-721.