The Wave Period Parameterization of Ocean Waves and Its Application to Ocean Wave Simulations

The wave period is a wave parameter that is significantly influenced by factors such as wind speed and bottom topography. Previous research on wave period parameterization has primarily focused on wind-dominated sea areas and may not be applicable to certain regions, such as the equatorial calm or c...

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Veröffentlicht in:Remote sensing (Basel, Switzerland) Switzerland), 2023-11, Vol.15 (22), p.5279
Hauptverfasser: Lv, Jialei, Zhang, Wenjing, Shi, Jian, Wu, Jie, Wang, Hanshi, Cao, Xuhui, Wang, Qianhui, Zhao, Zeqi
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Sprache:eng
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Zusammenfassung:The wave period is a wave parameter that is significantly influenced by factors such as wind speed and bottom topography. Previous research on wave period parameterization has primarily focused on wind-dominated sea areas and may not be applicable to certain regions, such as the equatorial calm or coastal areas dominated by swell waves. To address this limitation, this paper utilizes the third-generation wave numerical model SWAN to perform wave numerical simulations for a portion of the Northwest Pacific Ocean. The simulation incorporates observational data from nearshore stations, buoys, and satellite altimeters for error analysis. To develop a new wave parameterization scheme (WS-23), we employ extensive NDBC buoy data and incorporate the exponential rate and wave age characteristics that were previously established by predecessors. Our scheme introduces a judgement mechanism to distinguish between wind waves, swell waves, and mixed waves. The resulting ocean wave factor enhances the mean wave period values calculated using the model and other parameterization schemes. The experimental results demonstrate that our new parameterization scheme effectively improves the abnormal peak of the fitting data. Comparing the output values of the mean wave period element output of the SWAN model with our new parameterization scheme, we observe a reduction in the mean values of Ea, Ec, and RMSE by 0.231, 1.94%, and 0.162, respectively, while increasing the average r by 0.05.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15225279