Accurate forecasts of extreme wind speeds are of high importance for many applications. Such forecasts are usually generated by ensembles of numerical weather prediction (NWP) models, which however can be biased and have errors in dispersion, thus necessitating the application of statistical post-processing techniques. In this work we aim to improve statistical post-processing models for probabilistic predictions of extreme wind speeds. We do this by adjusting the training procedure used to fit ensemble model output statistics (EMOS) models - a commonly applied post-processing technique - and propose estimating parameters using the so-called threshold-weighted continuous ranked probability score (twCRPS), a proper scoring rule that places special emphasis on predictions over a threshold. We show that training using the twCRPS leads to improved extreme event performance of post-processing models for a variety of thresholds. We find a distribution body-tail trade-off where improved performance for probabilistic predictions of extreme events comes with worse performance for predictions of the distribution body. However, we introduce strategies to mitigate this trade-off based on weighted training and linear pooling. Finally, we consider some synthetic experiments to explain the training impact of the twCRPS and derive closed-form expressions of the twCRPS for a number of distributions, giving the first such collection in the literature. The results will enable researchers and practitioners alike to improve the performance of probabilistic forecasting models for extremes and other events of interest.

翻译：极端风速的精确预报对许多应用至关重要。此类预报通常由数值天气预报（NWP）模型集合生成，但这些模型可能存在偏差和离散度误差，因此需要应用统计后处理技术。本研究旨在改进极端风速概率预测的统计后处理模型。我们通过调整用于拟合集合模型输出统计（EMOS）模型（一种常用后处理技术）的训练过程来实现这一目标，并提出使用阈值加权连续分级概率评分（twCRPS）进行参数估计——这是一种强调阈值以上预测的严格评分规则。我们证明，使用twCRPS进行训练可提升后处理模型在不同阈值下对极端事件的预测性能。我们发现存在分布主体与尾部的权衡：极端事件概率预测性能的提升会伴随分布主体预测性能的下降。但我们提出了基于加权训练和线性池化的策略来缓解这种权衡。最后，我们通过合成实验解释twCRPS对训练的影响，并为多种分布推导了twCRPS的闭式表达式，这在文献中尚属首次。研究结果将使研究人员和实践者能够改进极端事件及其他关注事件的概率预报模型性能。