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Abstract
The reliable monitoring of sea state parameters is a key factor for weather forecasting, as well as for ensuring the safety and navigation of ships. In the current analysis, two spectrum estimation techniques, based on the Welch and Thomson methods, were applied to a set of random wave signals generated from a theoretical wave spectrum obtained by combining wind sea and swell components with the same prevailing direction but different combinations of significant wave heights, peak periods, and peak enhancement factors. A wide benchmark study was performed to systematically apply and compare the two spectrum estimation methods. In this respect, different combinations of wind sea spectra, corresponding to four grades of the Douglas Scale, were combined with three swell spectra corresponding to different swell categories. The main aim of the benchmark study was to systematically investigate the effectiveness of the Welch and Thomson methods in terms of spectrum restitution and the assessment of sea state parameters. The spectrum estimation methods were applied to random wave signals with different durations, namely 600 s (short) and 3600 s (long), to investigate how the record length affected the assembled sea state parameters, which, in turn, were assessed by the nonlinear least square method. Finally, based on the main outcomes of the benchmark study, some suggestions are provided to select the most suitable spectrum reconstruction method and increase the effectiveness of the assembled sea state parameters.
Highlights
The assessment of wave spectra from the analysis of random wave elevations has been a widely investigated topic since the works of Mansard and Funke [1,2] and Battjes and val Vledder [3] because it is a key factor to detect sea state conditions and ensure the safety and navigation of ships [4,5,6]
The paper focused on the application of the Thomson and Welch spectrum estimation methods to assess the main parameters of bimodal wave spectra obtained by the superposition of wind wave and swell components
10-min durations, were generated based on a set of theoretical bimodal spectra obtained by several combinations of wind sea and swell components that were characterized by different significant wave heights, wave peak periods, and peak enhancement factors
Summary
The assessment of wave spectra from the analysis of random wave elevations has been a widely investigated topic since the works of Mansard and Funke [1,2] and Battjes and val Vledder [3] because it is a key factor to detect sea state conditions and ensure the safety and navigation of ships [4,5,6]. The assessment of wave spectrum parameters, namely significant wave height, wave peak period, and peak enhancement factor, has been revealed to be a quite-challenging issue since some aspects, such as the selection of a proper spectrum estimation technique, the minimum duration of the wave time signal, and the trade-off between spectral resolution and variance of the spectral estimator represent critical issues of the entire data processing procedure. Spectrum estimation is a key data processing tool for dynamic measurement, and, in the case of sea waves, it constitutes the first step for estimating sea-state parameters. The seminal idea under the first group is the “periodogram,” which is the square of the Fourier transform of the signal, divided by the observation duration, originally proposed by Schuster to identify periodicity in noisy signals [17]. One of the most effective was formulated by Welch [18] and basically consists of dividing a signal into segments, tapering each segment by a smoothing window, calculating the periodogram of each pre-treated segment, and averaging the periodograms
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