Estimation of Wave Directional Spectra from Pitch‐Roll Buoy Data

Abstract

Estimates of the directional wave spectrum are generally extracted from data obtained by a heave‐pitch‐roll buoy. Many different spectra estimators are now available, and there is a question as to which one is the more reliable in different environmental conditions. Routine data analysis and wave‐spectra calculation require a computationally efficient and robust estimator. Five different directional spectrum estimates are compared for both synthetic and field data. Synthetic data consists of reconstructed cross‐spectral density matrices with and without noise, and the field data come from the Atlantic Ocean via the Surface Wave Dynamics Experiment (SWADE). Directional spreading and higher‐order statistics of the wave field are compared for each technique. Test results indicate that the maximum likelihood method (MLM) estimator, although not considered an “optimal” estimator, gives the most consistent and predictable results, and should therefore be favored over more traditionally used direct Fourier transform methods. Other spectral estimators, such as the maximum entropy method, iterative MLM, and eigen vector MLM, outperform the MLM in many cases, but have unwanted characteristics, such as wavenumber dependence, high sensitivity to noise, and numerical instability. Results outline the fact that none of the directional spectra estimators performs very well in every case and that care should be taken in data interpretation for engineering applications.