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.本补充数据配套2023年发表于《Meteorology》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，探究了大平均重现期（mean recurrence intervals, MRI）下极端风速的预测可靠性。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），与一种可刻画收敛至正确渐近线过程不完全性的当代亚渐近耿贝尔分布进行对比。结果表明，所有场景下的平均偏差误差均极小，因此由标准误差表征的离散程度成为核心可靠性指标。由于可获得额外的亚时段数据，超阈值峰值法（peak-over-threshold, POT）始终比时段法更可靠。广义渐近类方法的可靠性始终低于亚渐近类方法，且二者的差距随平均重现期增大而扩大。本研究通过证实GEV与GPD在实际应用中预测可靠性最差，进一步支撑了此前已发表的基于理论推导的结论——即GEV与GPD不适用于极端风速建模。一种新型两步式威布尔-XIMIS混合方法展现出更优异的可靠性。