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