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Sampling Errors in Ensemble Kalman Filtering. Part II: Application to a Barotropic Model

Sampling Errors in Ensemble Kalman Filtering. Part II: Application to a Barotropic Model In the current study, the authors are concerned with the comparison of the average performance of stochastic versions of the ensemble Kalman filter with and without covariance inflation, as well as the double ensemble Kalman filter. The theoretical results obtained in Part I of this study are confronted with idealized simulations performed with a perfect barotropic quasigeostrophic model. Results obtained are very consistent with the analytic expressions found in Part I. It is also shown that both the double ensemble Kalman filter and covariance inflation techniques can avoid filter divergence. Nevertheless, covariance inflation gives efficient results in terms of accuracy and reliability for a much lower computational cost than the double ensemble Kalman filter and for smaller ensemble sizes. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Monthly Weather Review American Meteorological Society

Sampling Errors in Ensemble Kalman Filtering. Part II: Application to a Barotropic Model

Monthly Weather Review , Volume 137 (5) – Jun 13, 2008

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References (30)

Publisher
American Meteorological Society
Copyright
Copyright © 2008 American Meteorological Society
ISSN
1520-0493
DOI
10.1175/2008MWR2685.1
Publisher site
See Article on Publisher Site

Abstract

In the current study, the authors are concerned with the comparison of the average performance of stochastic versions of the ensemble Kalman filter with and without covariance inflation, as well as the double ensemble Kalman filter. The theoretical results obtained in Part I of this study are confronted with idealized simulations performed with a perfect barotropic quasigeostrophic model. Results obtained are very consistent with the analytic expressions found in Part I. It is also shown that both the double ensemble Kalman filter and covariance inflation techniques can avoid filter divergence. Nevertheless, covariance inflation gives efficient results in terms of accuracy and reliability for a much lower computational cost than the double ensemble Kalman filter and for smaller ensemble sizes.

Journal

Monthly Weather ReviewAmerican Meteorological Society

Published: Jun 13, 2008

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