Please use this identifier to cite or link to this item: https://doi.org/10.1063/1.3452300
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dc.titleDesign of field experiments for adaptive sampling of the ocean with autonomous vehicles
dc.contributor.authorZheng, H.
dc.contributor.authorOoi, B.H.
dc.contributor.authorCho, W.
dc.contributor.authorDao, M.H.
dc.contributor.authorTkalich, P.
dc.contributor.authorPatrikalakis, N.M.
dc.date.accessioned2014-12-12T07:35:53Z
dc.date.available2014-12-12T07:35:53Z
dc.date.issued2010
dc.identifier.citationZheng, H., Ooi, B.H., Cho, W., Dao, M.H., Tkalich, P., Patrikalakis, N.M. (2010). Design of field experiments for adaptive sampling of the ocean with autonomous vehicles. AIP Conference Proceedings 1233 (PART 1) : 905-910. ScholarBank@NUS Repository. https://doi.org/10.1063/1.3452300
dc.identifier.isbn9780735407787
dc.identifier.issn0094243X
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/116060
dc.description.abstractDue 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. © 2010 American Institute of Physics.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1063/1.3452300
dc.sourceScopus
dc.subjectAdaptive Sampling
dc.subjectEnvironmental Sensing
dc.subjectMarine Robotics
dc.subjectOcean Features
dc.subjectPath Planning
dc.typeConference Paper
dc.contributor.departmentTROPICAL MARINE SCIENCE INSTITUTE
dc.description.doi10.1063/1.3452300
dc.description.sourcetitleAIP Conference Proceedings
dc.description.volume1233
dc.description.issuePART 1
dc.description.page905-910
dc.identifier.isiut000283003800155
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