A three-dimensional variational data assimilation scheme in support of coastal ocean observing systems

Zhijin Li; Yi Chao; John D. Farrara; Xiaochun Wang; James C. McWilliams; Kayo Ide

doi:10.1117/12.740681

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13 September 2007 A three-dimensional variational data assimilation in support of coastal ocean observing systems

Zhijin Li, Yi Chao, John D. Farrara, Xiaochun Wang, James C. McWilliams, Kayo Ide

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Proceedings Volume 6685, Assimilation of Remote Sensing and In Situ Data in Modern Numerical Weather and Environmental Prediction Models; 66850B (2007) https://doi.org/10.1117/12.740681
Event: Optical Engineering + Applications, 2007, San Diego, California, United States

Abstract

A three-dimensional variational data assimilation (3DVAR) system (ROMS3DAR) has been developed in the framework of the Regional Ocean Modeling System (ROMS). This system enables the capability of predicting meso- to small-scale variations with temporal scales from hours to days in the coastal oceans. To cope with the particular difficulties that result from complex coastlines and bottom topography, unbalanced flows and sparse observations, ROMS3DVAR utilizes several novel strategies. These strategies include the implementation of three-dimensional anisotropic and inhomogeneous error correlations, application of particular weak dynamic contraints, and implementation of efficient and reliable algorithms for minimizing the cost function. ROMS3DVAR has been implemented in a quasi-real-time fashion in support of both the Southern and Central California Coastal Ocean Observing System (SCCOOS and CenCOOS). ROMS3DVAR assimilates a variety of observations, including satellite sea surface temperatures and sea surface heights, High Frequency (HF) radar velocities, ship reports and other available temperature and salinity profiles. The evaluation showed useful forecast skills.

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Zhijin Li, Yi Chao, John D. Farrara, Xiaochun Wang, James C. McWilliams, and Kayo Ide "A three-dimensional variational data assimilation in support of coastal ocean observing systems", Proc. SPIE 6685, Assimilation of Remote Sensing and In Situ Data in Modern Numerical Weather and Environmental Prediction Models, 66850B (13 September 2007); https://doi.org/10.1117/12.740681

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