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dc.contributor.advisorHartnett, Michael
dc.contributor.authorRen, Lei
dc.date.accessioned2015-11-06T15:47:48Z
dc.date.issued2015-10
dc.identifier.urihttp://hdl.handle.net/10379/5320
dc.description.abstractAccurate forecasting of surface currents in coastal waters is of great importance for operations such as search and rescue operations and marine energy extraction. Numerical models and measurements are two powerful conventional approaches to study coastal currents. However, accurate initial and boundary conditions are not easily defined in numerical models. Nonetheless, development of the High Frequency (HF) radar system makes it possible to monitor the surface water in coastal areas in a fine spatial resolution over a short temporal interval. In order to produce accurate information of surface currents in coastal areas, a combination technique, named data assimilation, has been applied to make the best use of radar data into models to enhance modelling performance. Radar data measured by CODAR (Coastal ocean dynamics applications radar) had relatively good quality cross-validated with ADCP (Acoustic Doppler Current Profile) data and wave buoy measurements. Before assimilating the radar data into models, the best “free run” (without data assimilation) of EFDC (Environmental Fluid Dynamics Code) model was set up by sequentially examining wind data, wind stress coefficient, vertical layer thickness structure and bottom roughness height. Assimilation tests using pseudo data investigated model’s stability and influences of data assimilation cycle lengths on forecasting. Radar currents were separately combined into the model using four kinds of sequential data assimilation algorithms: Direct Insertion, Optimal Interpolation, nudging and indirect data assimilation via correcting wind stress. Assimilation parameters in each algorithm were optimized based on generating good patterns of surface currents over hindcasting periods. To extend forecasting improvements, sensitivity tests were performed using the temporally interpolated radar data to determine the best data assimilation cycle length for each algorithm. To assess degrees of implementation complexity and improvement success in each algorithm, modelled results were intercompared with the radar data in qualitative methods and quantitative methods. According to the model forecasting performance, the best developed data assimilation model for simulating surface currents in Galway Bay was to combine the radar currents at each model timestep using nudging data assimilation algorithm.en_IE
dc.subjectData assimilationen_IE
dc.subjectSurface currentsen_IE
dc.subjectModellingen_IE
dc.subjectForecastingen_IE
dc.subjectHigh-frequency radaren_IE
dc.subjectCivil engineeringen_IE
dc.subjectDepartment of Civil Engineeringen_IE
dc.subjectCollege of Engineering and Informaticsen_IE
dc.titleInvestigations into assimilating high-frequency radar data into a three-dimensional hydrodynamic model of a wind-dominated coastal water bodyen_IE
dc.typeThesisen_IE
dc.contributor.funderNational University of Ireland Galwayen_IE
dc.contributor.funderChina Scholarship Council (CSC)en_IE
dc.local.noteMy PhD research focused on assimilating High-Frequency radar currents into a three-dimensional model to improve forecasting, four types of sequential data assimilation approaches had be applied and intercompared. The best assimilation model for Galway Bay is using nudging data assimilation approach to update the model background states at each model timestep.en_IE
dc.description.embargo2019-10-23
dc.local.finalYesen_IE
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