Supplementary Materials for "Reliability of extreme wind speeds predicted by extreme-value analysis", Meteorology, 2023, 2(3), 344-367.

Supplementary data to accompany "Reliability of extreme wind speeds predicted by extreme-value analysis" published in Meteorology, 2023, 2, which examines the reliability of extreme wind speed predictions at large mean recurrence intervals (MRI) by bootstrapping samples from representative known distributions. The classical asymptotic generalized extreme value distribution (GEV) and the generalized Pareto (GPD) distribution are compared with a contemporary sub-asymptotic Gumbel distribution that accounts for incomplete convergence to the correct asymptote. The mean bias error is shown to be minimal in all cases, so that the variability expressed by the standard error becomes the principal reliability metric. Peak-over-threshold (POT) methods are shown to always be more reliable than epoch methods due to the additional sub-epoch data. The generalized asymptotic methods are shown to always be less reliable than the sub-asymptotic methods by a factor that increases with MRI. This study reinforces the previously published theory-based arguments that GEV and GPD are unsuitable models for extreme wind speeds by showing that they also provide the least reliable predictions in practice. A new two-step Weibull-XIMIS hybrid method is shown to have superior reliability.本辅助数据集配套发表于《Meteorology》2023年第2卷的《基于极值分析的极端风速预测可靠性》一文。该研究通过从典型已知分布中抽取自助样本，探究了大平均重现间隔（mean recurrence interval, MRI）下极端风速预测的可靠性。研究将经典渐近广义极值分布（generalized extreme value distribution, GEV）与广义帕累托分布（generalized Pareto distribution, GPD），与一种考虑了未完全收敛至正确渐近形式的现代次渐近耿贝尔分布（Gumbel distribution）进行了对比。所有场景下的平均偏差误差均被证实极小，因此由标准误差体现的变异性成为核心可靠性评价指标。由于可利用额外的次时段数据，超阈值峰值法（peak-over-threshold, POT）始终比时段极值法具备更高的可靠性。广义渐近类方法的可靠性始终低于次渐近类方法，且二者的差距随平均重现间隔增大而扩大。本研究通过证实GEV与GPD在实际应用中预测可靠性最差，进一步支撑了此前基于理论提出的观点：二者不适用于极端风速建模。一种新型两步式威布尔-XIMIS混合方法被证实具备更优异的可靠性。