Environmental contour correction using Bayesian inference for areas with limited metocean data

Wind speed–wave height contours are crucial for evaluating the extreme metocean conditions of offshore wind structures. To construct reliable contours, long-term buoy data are essential. However, in Korea, the limited observation period of simultaneous wind and wave data poses a challenge, resulting in low reliability in estimating extreme environmental loads. Therefore, in this study, we proposed a method for refining the distribution of metocean data using Bayesian inference. Our comparison of extreme metocean conditions based on different observation periods revealed significant variations in the estimated conditions over short observation periods. To address this issue, the wave buoy data distribution was defined as a prior distribution, and the wave height distribution for the target region was corrected using a Bayesian inference approach. In addition, the wind speed distribution was improved by considering the correlation between wind speed and wave height. Subsequently, extreme metocean conditions were evaluated using the environmental contour approach based on the IFORM method. The results confirmed that the distribution of metocean data was improved, allowing for the derivation of more reliable extreme environmental loads than with conventional environmental contours. Therefore, the methodology presented in this study can be applied for constructing reasonable and reliable environmental contours, even when observation periods are limited.