Investigations into assimilating high-frequency radar data into a three-dimensional hydrodynamic model of a wind-dominated coastal water body

Accurate 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.

Funder

National University of Ireland Galway
China Scholarship Council (CSC)

Rights

Attribution-NonCommercial-NoDerivs 3.0 Ireland

cbn

Collections

University of Galway Theses (PhD Theses)