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In the design process of marine structures like offshore wind turbines thelong-term distribution of significant wave height needs to be modelled toestimate loads. This is typically done by fitting a translated Weibulldistribution to wave data. However, the translated Weibull distribution oftenfits well at typical values, but poorly at high wave heights such that extremeloads are underestimated. Here, we analyzed wave datasets from six locationssuitable for offshore wind turbines. We found that the exponentiated Weibulldistribution provides better overall fit to these wave data than the translatedWeibull distribution. However, when the exponentiated Weibull distribution wasfitted using maximum likelihood estimation, model fit at the upper tail wassometimes still poor. Thus, to ensure good model fit at the tail, we estimatedthe distribution's parameters by prioritizing observations of high wave heightusing weighted least squares estimation. Then, the distribution fitted well atthe bulks of the six datasets (mean absolute error in the order of 0.1 m) andat the tails (mean absolute error in the order of 0.5 m). The proposed methodalso estimated the wave height's 1-year return value accurately and could beused to calculate design loads for offshore wind turbines.

Predicting wave heights for marine design by prioritizing extreme events in a global model