• Journal of Ocean Engineering and Technology
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• v.33 no.3
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• pp.222-228
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• 2019

An extreme value analysis (EVA) is essential to obtain a design value for highly nonlinear variables such as long-term environmental data for wind and waves, and slamming or sloshing impact pressures. According to the extreme value theory (EVT), the extreme value distribution is derived by multiplying the initial cumulative distribution functions for independent and identically distributed (IID) random variables. However, in the position mooring of DNVGL, the sampled global maxima of the mooring line tension are assumed to be IID stochastic variables without checking their independence. The ITTC Recommended Procedures and Guidelines for Sloshing Model Tests never deal with the independence of the sampling data. Hence, a design value estimated without the IID check would be under- or over-estimated because of considering observations far away from a Weibull or generalized Pareto distribution (GPD) as outliers. In this study, the IID sampling data are first checked in an EVA. With no IID random variables, an automatic resampling scheme is recommended using the block maxima approach for a generalized extreme value (GEV) distribution and peaks-over-threshold (POT) approach for a GPD. A partial autocorrelation function (PACF) is used to check the IID variables. In this study, only one 5 h sample of sloshing test results was used for a feasibility study of the resampling IID variables approach. Based on this study, the resampling IID variables may reduce the number of outliers, and the statistically more appropriate design value could be achieved with independent samples.

• Journal of the Korean earth science society
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• v.42 no.5
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• pp.524-535
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• 2021

Rapid climate change and oceanic warming have increased the variability of oceanic wave heights over the past several decades. In addition, the extreme wave heights, such as the upper 1% (or 5%) wave heights, have increased more than the heights of the normal waves. This is true for waves both in global oceans as well as in local seas. Satellite altimeters have consistently observed significant wave heights (SWHs) since 1991, and sufficient SWH data have been accumulated to investigate 100-year return period SWH values based on statistical approaches. Satellite altimeter data were used to estimate the extreme SWHs at the Ieodo Ocean Research Station (IORS) for the period from 2005 to 2016. Two representative extreme value analysis (EVA) methods, the Initial Distribution Method (IDM) and Peak over Threshold (PoT) analysis, were applied for SWH measurements from satellite altimeter data and compared with the in situ measurements observed at the IORS. The 100-year return period SWH values estimated by IDM and PoT analysis using IORS measurements were 8.17 and 14.11 m, respectively, and those using satellite altimeter data were 9.21 and 16.49 m, respectively. When compared with the maximum value, the IDM method tended to underestimate the extreme SWH. This result suggests that the extreme SWHs could be reasonably estimated by the PoT method better than by the IDM method. The superiority of the PoT method was supported by the results of the in situ measurements at the IORS, which is affected by typhoons with extreme SWH events. It was also confirmed that the stability of the extreme SWH estimated using the PoT method may decline with a decrease in the quantity of the altimeter data used. Furthermore, this study discusses potential limitations in estimating extreme SWHs using satellite altimeter data, and emphasizes the importance of SWH measurements from the IORS as reference data in the East China Sea to verify satellite altimeter data.