Estimating correlated observation error statistics using an ensemble transform Kalman filter

• Published in: Tellus. Series A, Dynamic meteorology and oceanography Vol. 66; no. 1; pp. 23294 - 15
• Main Authors: Waller, Joanne A., Dance, Sarah L., Lawless, Amos S., Nichols, Nancy K.
• Format: Journal Article
• Language: English
• Published: Stockholm Taylor & Francis 01-01-2014; Ubiquity Press; Stockholm University Press
• Subjects: Colleges & universities; correlated observation errors; Correlation; Covariance; Dance; Data assimilation; Data collection; ensemble transform Kalman filter; Error analysis; Errors; Estimates; Estimating; Full text; Kalman filters; Marine; Meteorology; Statistical methods; Statistics; Transforms; United Kingdom > UK
• ISSN: 1600-0870; 0280-6495; 1600-0870
• DOI: 10.3402/tellusa.v66.23294

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.