Can We Optimize the Assimilation Order in the Serial Ensemble Kalman Filter? A Study with the Lorenz-96 Model

Can We Optimize the Assimilation Order in the Serial Ensemble Kalman Filter? A Study with the... AbstractWith the serial treatment of observations in the ensemble Kalman filter (EnKF), the assimilation order of observations is usually assumed to have no significant impact on analysis accuracy. However, Nerger derived that analyses with different assimilation orders are different if covariance localization is applied in the observation space. This study explores whether the assimilation order can be optimized to systematically improve the filter estimates. A mathematical demonstration of a simple two-dimensional case indicates that different assimilation orders can cause different analyses, although the differences are two orders of magnitude smaller than the analysis increments if two identical observation error variances are the same size as the two identical state error variances. Numerical experiments using the Lorenz-96 40-variable model show that the small difference due to different assimilation orders could eventually result in a significant difference in analysis accuracy. Several ordering rules are tested, and the results show that an ordering rule that gives a better forecast relative to future observations improves the analysis accuracy. In addition, the analysis is improved significantly by ordering observations from worse to better impacts using the ensemble forecast sensitivity to observations (EFSO), which estimates how much each observation reduces or increases the forecast error. With the EFSO ordering rule, the change in error during the serial assimilation process is similar to that obtained by the experimentally found best sampled assimilation order. The ordering has more impact when the ensemble size is smaller relative to the degrees of freedom of the dynamical system. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Monthly Weather Review American Meteorological Society

Can We Optimize the Assimilation Order in the Serial Ensemble Kalman Filter? A Study with the Lorenz-96 Model

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Publisher
American Meteorological Society
Copyright
Copyright © American Meteorological Society
ISSN
1520-0493
D.O.I.
10.1175/MWR-D-17-0094.1
Publisher site
See Article on Publisher Site

Abstract

AbstractWith the serial treatment of observations in the ensemble Kalman filter (EnKF), the assimilation order of observations is usually assumed to have no significant impact on analysis accuracy. However, Nerger derived that analyses with different assimilation orders are different if covariance localization is applied in the observation space. This study explores whether the assimilation order can be optimized to systematically improve the filter estimates. A mathematical demonstration of a simple two-dimensional case indicates that different assimilation orders can cause different analyses, although the differences are two orders of magnitude smaller than the analysis increments if two identical observation error variances are the same size as the two identical state error variances. Numerical experiments using the Lorenz-96 40-variable model show that the small difference due to different assimilation orders could eventually result in a significant difference in analysis accuracy. Several ordering rules are tested, and the results show that an ordering rule that gives a better forecast relative to future observations improves the analysis accuracy. In addition, the analysis is improved significantly by ordering observations from worse to better impacts using the ensemble forecast sensitivity to observations (EFSO), which estimates how much each observation reduces or increases the forecast error. With the EFSO ordering rule, the change in error during the serial assimilation process is similar to that obtained by the experimentally found best sampled assimilation order. The ordering has more impact when the ensemble size is smaller relative to the degrees of freedom of the dynamical system.

Journal

Monthly Weather ReviewAmerican Meteorological Society

Published: Dec 11, 2017

References

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