Estimating correlated observation error statistics using an ensemble transform Kalman filter

Waller, J. A., Dance, S. L. ORCID: https://orcid.org/0000-0003-1690-3338, Lawless, A. S. ORCID: https://orcid.org/0000-0002-3016-6568 and Nichols, N. K. ORCID: https://orcid.org/0000-0003-1133-5220 (2014) Estimating correlated observation error statistics using an ensemble transform Kalman filter. Tellus A, 66. 23294. ISSN 1600-0870 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
URI	https://reading-clone.eprints-hosting.org/id/eprint/37547
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Mathematics and Statistics Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Co-Action Publishing
Download/View statistics	View download statistics for this item