Sensitivity analysis of wind input parametrizations in the WAVEWATCH III spectral wave model using the ST6 source term package for Ireland

Fernández, Leandro; Calvino, Clément; Dias, Frédéric

doi:10.1016/j.apor.2021.102826

Cited by 7 publications

(1 citation statement)

References 25 publications

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“…However, performing numerical tests on the sensitivity of all parameters related to forecasting performance is inefficient. Therefore, sensitivity analysis should be preceded for physical parameters closely related to offshore wind-wave development and nonlinear wave interactions, which are known to have the most significant impact on forecasting performance, and then gradually examine the interaction between parameters [38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis of Forecasting Performance for ST6 Parameterization in High-Resolution Wave Model Based on WAVEWATCH III

Roh

Chang

et al. 2023

JMSE

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A regional wave forecasting system in East Asia, including the Korean Peninsula, was built based on WAVEWATCH III using offshore wind forecast data from the Global Data Assimilation Prediction System. The numerical simulations were performed on the sensitivity of the interaction between input wind and wave development. The forecasts for each condition were compared and verified with the observational data of marine meteorological buoys from 1 August to 30 September 2020. The sensitivity conditions were configured to have a specific range of variables related to the directional distribution of input winds (SINA0) and variables indicating the development of input wind–wave (CDFAC) in the ST6. The results were presented by calculating the mean error and root mean square error for all observation points. Overall, as the CDFAC increased, the mean error tended to decrease according to the forecast time and the root mean square error increased. Although the effect of SINA0 at the same CDFAC was insignificant, when SINA0 increased in sections where the significant wave height decreased rapidly, the significant wave height tended to decrease. In addition, the main variables that affect the physical process of wind–wave interaction should be considered to improve wave model forecasting performance and accuracy.

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Section: Introductionmentioning

confidence: 99%