Two-dimensional analysis of wave energy evolution using wavelet transforms
Date of Award
Doctor of Philosophy (Ph.D.)
Applied Marine Physics/Ocean Engineering
First Committee Member
Hans C. Graber - Committee Chair
The capability of HF radar systems to sample the distribution of wave energy over wide areas is investigated in different storm scenarios. A methodology that combines the estimation of significant wave height ( Hs) from Doppler spectra and the filtering of the resulting highly scattered time series is established. First, a model introduced elsewhere to estimate Hs was calibrated and tested (to assess bias and uncertainty) with data from seven different buoy/gauge stations collected during three different field experiments. Subsequently, Hs estimates were obtained for all sampling points within the radar effective domain (in all experiments) and a filtering technique based on Wavelet transform characterization and decomposition was applied. The effectiveness of the filtering technique (i.e. accuracy of the filtered radar-derived estimates) was assessed by comparison to results from wave propagation models calibrated by independent buoy measurements. It was found that the treated estimates were able to represent adequately the energy field induced by different storm events as the filtering technique minimized the contribution of unrealistic features introduced by the presence of some defective sampling intrinsic to the radar measuring technique. The proposed methodology proved to be essential for the use of HF radar as a tool to identify wave energy trends and potential zones of wave energy concentration in coastal areas. These findings enhance greatly the sampling capabilities of radar systems since reliable wave energy estimates can be obtained in addition to conventional surface current measurements.
Physical Oceanography; Engineering, Marine and Ocean; Remote Sensing
Ramos-Heredia, Rafael Juda, "Two-dimensional analysis of wave energy evolution using wavelet transforms" (2006). Dissertations from ProQuest. 2420.