A Hybrid Background Error Covariance Model for Assimilating Glider Data into a Coastal Ocean Model

Max Yaremchuk Naval Research Laboratory, Stennis Space Center, Mississippi

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Dmitri Nechaev Department of Marine Science, University of Southern Mississippi, Stennis Space Center, Mississippi

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Chudong Pan Department of Marine Science, University of Southern Mississippi, Stennis Space Center, Mississippi

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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 with the heuristic Gaussian-shaped covariances 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 with information criterion based on the innovation error statistics, (iii) restriction of the heuristic covariance operator to the null space of , and (iv) definition of the BEC magnitudes through separate analyses of the innovation error statistics in the state space and the null space of .

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.

Corresponding author address: Max Yaremchuk, Naval Research Laboratory, Code 7321, Bldg. 1009, Stennis Space Center, MS 39529. E-mail: max.yaremchuk@nrlssc.navy.mil

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 with the heuristic Gaussian-shaped covariances 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 with information criterion based on the innovation error statistics, (iii) restriction of the heuristic covariance operator to the null space of , and (iv) definition of the BEC magnitudes through separate analyses of the innovation error statistics in the state space and the null space of .

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.

Corresponding author address: Max Yaremchuk, Naval Research Laboratory, Code 7321, Bldg. 1009, Stennis Space Center, MS 39529. E-mail: max.yaremchuk@nrlssc.navy.mil
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