Recent statistical postprocessing methods for wind speed forecasts have incorporated linear models and neural networks to produce more skillful probabilistic forecasts in the low-to-medium wind speed range. At the same time, these methods struggle in the high-to-extreme wind speed range. In this work, we aim to increase the performance in this range by training using a weighted version of the continuous ranked probability score (wCRPS). We develop an approach using shifted Gaussian cdf weight functions, whose parameters are tuned using a multi-objective hyperparameter tuning algorithm that balances performance on low and high wind speed ranges. We explore this approach for both linear models and convolutional neural network models combined with various parametric distributions, namely the truncated normal, log-normal, and generalized extreme value distributions, as well as adaptive mixtures. We apply these methods to forecasts from KNMI's deterministic Harmonie-Arome numerical weather prediction model to obtain probabilistic wind speed forecasts in the Netherlands for 48 hours ahead. For linear models we observe that even with a tuned weight function, training using the wCRPS produces a strong body-tail trade-off, where increased performance on extremes comes at the price of lower performance for the bulk of the distribution. For the best models using convolutional neural networks, we find that using a tuned weight function the performance on extremes can be increased without a significant deterioration in performance on the bulk. The best-performing weight function is shown to be model-specific. Finally, the choice of distribution has no significant impact on the performance of our models.

翻译：近期针对风速预报的统计后处理方法结合了线性模型与神经网络，在中低风速范围内已能生成更具技巧性的概率预报。然而，这些方法在高速至极端风速范围内仍存在明显不足。本研究旨在通过采用加权连续分级概率评分（wCRPS）进行模型训练，以提升该极端区间的预报性能。我们提出了一种基于平移高斯累积分布函数权重函数的优化方法，其参数通过多目标超参数调优算法进行校准，以平衡模型在低风速与高风速区间的性能表现。在线性模型与卷积神经网络模型中，我们分别将此方法与多种参数化分布（包括截断正态分布、对数正态分布、广义极值分布）以及自适应混合分布相结合进行探索。我们将这些方法应用于荷兰皇家气象研究所（KNMI）的确定性Harmonie-Arome数值天气预报模式，以获得荷兰地区未来48小时的概率风速预报。对于线性模型，研究发现即使采用调优后的权重函数，使用wCRPS进行训练仍会引发显著的“主体-尾部”权衡效应——即极端风速预报性能的提升会以分布主体部分性能下降为代价。对于采用卷积神经网络的最佳模型，我们发现通过调优权重函数可以在不显著降低主体分布性能的前提下提升极端风速的预报性能。研究同时表明，最优权重函数具有模型特异性。最后，分布类型的选择对本研究模型的性能未产生显著影响。