Document Type
Article
Publication Date
6-1-2011
Department
Marine Science
Abstract
A hybrid background error covariance (BEC) model for three-dimensional variational data assimilation of glider data into the Navy Coastal Ocean Model (NCOM) is introduced. Similar to existing atmospheric hybrid BEC models, the proposed model combines low-rank ensemble covariances B(m) with the heuristic Gaussian-shaped covariances B(0) to estimate forecast error statistics. The distinctive features of the proposed BEC model are the following: (i) formulation in terms of inverse error covariances, (ii) adaptive determination of the rank m of B(m) with information criterion based on the innovation error statistics, (iii) restriction of the heuristic covariance operator B(0) to the null space of B(m), and (iv) definition of the BEC magnitudes through separate analyses of the innovation error statistics in the state space and the null space of B(0). The BEC model is validated by assimilation experiments with simulated and real data obtained during a glider survey of the Monterey Bay in August 2003. It is shown that the proposed hybrid scheme substantially improves the forecast skill of the heuristic covariance model.
Publication Title
Monthly Weather Review
Volume
139
Issue
6
First Page
1879
Last Page
1890
Recommended Citation
Yaremchuk, M.,
Nechaev, D. A.,
Pan, C.
(2011). A Hybrid Background Error Covariance Model for Assimilating Glider Data into a Coastal Ocean Model. Monthly Weather Review, 139(6), 1879-1890.
Available at: https://aquila.usm.edu/fac_pubs/528
Comments
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