Multi-layer model simulation and data assimilation in the Serangoon Harbor of Singapore

In June of 2009, a sea trial was carried out around Singapore to study and monitor physical, biological and chemical oceanographic parameters. Temperature, salinity and velocities were collected from multiple vehicles. The extensive data set collected in the Serangoon Harbour provides an opportunity...

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Bibliographic Details
Main Authors: Wei, Jun (Contributor), Zheng, Haining (Contributor), Chen, Haoliang (Contributor), Ooi, Boon Hooi (Author), Dao, M. H. (Author), Cho, Wonjoon (Contributor), Tkalich, P. (Author), Patrikalakis, Nicholas M. (Contributor), Rizzoli, Paola M (Author)
Other Authors: Joint Program in Oceanography/Applied Ocean Science and Engineering (Contributor), Massachusetts Institute of Technology. Center for Ocean Engineering (Contributor), Massachusetts Institute of Technology. Department of Earth, Atmospheric, and Planetary Sciences (Contributor), Massachusetts Institute of Technology. Department of Mechanical Engineering (Contributor), Singapore-MIT Alliance in Research and Technology (SMART) (Contributor), Woods Hole Oceanographic Institution (Contributor), Rizzoli, Paola M. (Contributor)
Format: Article
Language:English
Published: International Society of Offshore and Polar Engineers, 2011-06-16T19:07:08Z.
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Summary:In June of 2009, a sea trial was carried out around Singapore to study and monitor physical, biological and chemical oceanographic parameters. Temperature, salinity and velocities were collected from multiple vehicles. The extensive data set collected in the Serangoon Harbour provides an opportunity to study barotropic and baroclinic circulation in the harbour and to apply data assimilation methods in the estuarine area. In this study, a three-dimensional, primitive equation coastal ocean model (FVCOM) with a number of vertical layers is used to simulate barotropic and baroclinic flows and reconstruct the vertical velocity structures. The model results are validated with in situ ADCP observations to assess the realism of the model simulations. EnKF data assimilation method is successively implemented to assimilate all the available ADCP data, and thus correct for the model forecast deficiencies.
Singapore. National Research Foundation
Singapore-MIT Alliance
Singapore-MIT Alliance. Center for Environmental Sensing and Monitoring