The effect of extreme wind speeds in applications for design is of great interest in a variety of fields such as meteorology and coastal engineering. In these fields a common problem is the scarcity of long datasets. To overcome this limitation, a common approach is to utilize the entire available dataset using the Peak-Over-Threshold (POT) approach. In small samples there may be a limited number of extremes and so re-sampling is often beneficial. However, the re-samples are often affected by dependency and the independence limitations are usually disregarded. To alleviate this effect, the DeCA Uncorrelated (DeCAUn) model is proposed taking into account the correlation effect when re-sampling. This model provides an improvement to the current physical De-Clustering Algorithm (DeCA), by re-sampling the samples of DeCA irregularly in time. The methodology proposed in this assessment is illustrated using wind speed data from a high resolution database over the North Sea, the Atlantic Ocean and the Mediterranean Sea. From this evaluation, the DeCAUn model is proposed as an alternative re-sampling strategy for observations irregularly spaced in time.

极端风速在设计应用中的影响在诸如气象学和海岸工程学等许多领域中引起了极大的兴趣。在这些领域中，一个普遍的问题是缺少长数据集。为了克服此限制，一种常见的方法是使用“阈值上限”（POT）方法来利用整个可用数据集。在小样本中，极限值可能有限，因此重新采样通常是有益的。但是，重采样通常会受到依赖性的影响，并且独立性限制通常会被忽略。为了减轻这种影响，提出了DeCA Uncorrelated（DeCAUn）模型，该模型考虑了重新采样时的相关效应。通过及时对DeCA的样本进行不规则采样，该模型对当前的物理De-Clustering算法（DeCA）进行了改进。使用来自北海，大西洋和地中海的高分辨率数据库中的风速数据说明了此评估中提出的方法。通过此评估，提出了DeCAUn模型，作为对时间上不规则间隔的观测结果的另一种重采样策略。