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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

URL to conference page. Scroll down to 2009 conference (June 21-26), click "Paper and session list," and search under Patrikalakis' name., Oceanographic features such as jets and vortices are often found downstream of obstacles and landforms such as islands or peninsulas. Such features have high spatial and temporal variability and are, hence, interesting but difficult to measure and quantify. This paper discusses an experiment to identify and resolve such oceanographic features in Selat Pauh, in the Straits of Singapore. The deployment formation for multiple robotic vehicles (Autonomous Surface Craft - ASC), the measurement instruments, and the algorithms developed in extracting oceanographic field variables are described. These were based on two ocean field predictions from well-known geophysical flow dynamic models. Field experiments were carried out and comparison of the forecasts with measurements was attempted. To investigate an unexpected behaviour of one ASC, hindcasts with wind effects and simulation with vortex feature extraction on a larger domain with more involved bathymetry were also partially carried out., Singapore-MIT Alliance for Research and Technology, Singapore. National Research Foundation (SMART/CENSAM initiative)

Due to the highly non-linear and dynamical nature of oceanic phenomena, the predictive capability of various ocean models depends on the availability of operational data. A practical method to improve the accuracy of the ocean forecast is to use a data assimilation methodology to combine in-situ measured and remotely acquired data with numerical forecast models of the physical environment. Autonomous surface and underwater vehicles with various sensors are economic and efficient tools for exploring and sampling the ocean for data assimilation; however there is an energy limitation to such vehicles, and thus effective resource allocation for adaptive sampling is required to optimize the efficiency of exploration. In this paper, we use physical oceanography forecasts of the coastal zone of Singapore for the design of a set of field experiments to acquire useful data for model calibration and data assimilation. The design process of our experiments relied on the oceanography forecast including the current speed, its gradient, and vorticity in a given region of interest for which permits for field experiments could be obtained and for time intervals that correspond to strong tidal currents. Based on these maps, resources available to our experimental team, including Autonomous Surface Craft (ASC) are allocated so as to capture the oceanic features that result from jets and vortices behind bluff bodies (e.g., islands) in the tidal current. Results are summarized from this resource allocation process and field experiments conducted in January 2009., Singapore. National Research Foundation