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The hydrodynamic circulation in estuaries is primarily driven by tides, river inflows and surface winds. While tidal and river data can be quite easily obtained for input to hydrodynamic models, sourcing accurate surface wind data is problematic. Inaccurate wind data can lead to inaccuracies in the surface currents computed by three-dimensional hydrodynamic models. In this research, a high-resolution wind model was coupled with a three-dimensional hydrodynamic model of Galway Bay, a semi-enclosed estuary on the west coast of Ireland, to investigate the effect of wind forcing on model accuracy. Two wind-forcing conditions were investigated: (1) using wind data measured onshore on the NUI Galway campus (NUIG) and (2) using offshore wind data provided by a high resolution wind model (HR). A scenario with no wind forcing (NW) was also assessed. The onshore wind data varied with time but the speed and direction were applied across the full model domain. The modeled offshore wind fields varied with both time and space. The effect of wind forcing on modeled hydrodynamics was assessed via comparison of modeled surface currents with surface current measurements obtained from a High-Frequency (HF) radar Coastal Ocean Dynamics Applications Radar (CODAR) observation system. Results indicated that winds were most significant in simulating the north-south surface velocity component. The model using high resolution temporally- and spatially-varying wind data achieved better agreement with the CODAR surface currents than the model using the onshore wind measurements and the model without any wind forcing.

In this paper, the Self-Organizing Map (SOM) method was applied to the surface currents data obtained between February and November 2008 by a network of high-frequency (HF) radars in the northern Adriatic. The sensitivity of the derived SOM solutions was tested in respect to the change of coordinate system of the data introduced to the SOM. In one experiment the original radial data measurements were used, and in the other experiment the Cartesian (total) current vectors derived from original radar data were analyzed. Although the computation of SOM solutions was not a demanding task, comparing both neural lattices yielded the nondeterministic polynomial time (NP) problem for which is difficult to propose a solution that will be globally optimal. Thus, we suggested utilizing the greedy algorithm with underlying assumption of 1-to-1 mapping between lattices. The results suggested that such solution could be local, but not global optimum and that the latter assumption could lower the obtained correlations between the patterns. However, without the assumption of 1-to-1 mapping between lattices, correlation between the derived SOM patterns was quite high, indicating that SOM mapping introduced to the radial current vectors and subsequent transformation into Cartesian coordinate system does not significantly affect obtained patterns in comparison to the SOM mapping done on the derived Cartesian current vectors. The documented similarity corroborates the use of total current vectors in various oceanographic studies, as being representative derivative of original radial measurements. We analyzed high-frequency radar data in the northern Adriatic.Self-Organizing Map method has been applied to radial and Cartesian current vectors.Transformation to Cartesian vectors do not significantly affect the SOM patterns.