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Estimating correlated observation error statistics using an ensemble transform Kalman filter

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Waller, J. A., Dance, S. L., Lawless, A. S. and Nichols, N. K. (2014) Estimating correlated observation error statistics using an ensemble transform Kalman filter. Tellus A, 66. 23294. ISSN 1600-0870

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To link to this article DOI: 10.3402/tellusa.v66.23294

Abstract/Summary

For certain observing types, such as those that are remotely sensed, the observation errors are correlated and these correlations are state- and time-dependent. In this work, we develop a method for diagnosing and incorporating spatially correlated and time-dependent observation error in an ensemble data assimilation system. The method combines an ensemble transform Kalman filter with a method that uses statistical averages of background and analysis innovations to provide an estimate of the observation error covariance matrix. To evaluate the performance of the method, we perform identical twin experiments using the Lorenz ’96 and Kuramoto-Sivashinsky models. Using our approach, a good approximation to the true observation error covariance can be recovered in cases where the initial estimate of the error covariance is incorrect. Spatial observation error covariances where the length scale of the true covariance changes slowly in time can also be captured. We find that using the estimated correlated observation error in the assimilation improves the analysis.

Item Type:Article
Refereed:Yes
Divisions:Faculty of Science > School of Mathematical and Physical Sciences > Department of Mathematics and Statistics
Faculty of Science > School of Mathematical and Physical Sciences > Department of Meteorology
ID Code:37547
Publisher:Co-Action Publishing

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