In recent years, ensemble weather forecasting has become a routine at all major weather prediction centers. These forecasts are obtained from multiple runs of numerical weather prediction models with different initial conditions or model parametrizations. However, ensemble forecasts can often be underdispersive and also biased, so some kind of postprocessing is needed to account for these deficiencies. One of the most popular state of the art statistical postprocessing techniques is the ensemble model output statistics (EMOS), which provides a full predictive distribution of the studied weather quantity. We propose a novel EMOS model for calibrating wind speed ensemble forecasts, where the predictive distribution is a generalized extreme value (GEV) distribution left truncated at zero (TGEV). The truncation corrects the disadvantage of the GEV distribution-based EMOS models of occasionally predicting negative wind speed values, without affecting its favorable properties. The new model is tested on four datasets of wind speed ensemble forecasts provided by three different ensemble prediction systems, covering various geographical domains and time periods. The forecast skill of the TGEV EMOS model is compared with the predictive performance of the truncated normal, log-normal and GEV methods and the raw and climatological forecasts as well. The results verify the advantageous properties of the novel TGEV EMOS approach.

中文翻译：

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基于截断广义极值分布的集合模型输出统计模型，用于风速集合预报标定

近年来，集合天气预报已成为各大天气预报中心的常态。这些预报是从具有不同初始条件或模型参数化的数值天气预报模型的多次运行中获得的。然而，集合预测通常可能是分散不足且有偏差的，因此需要某种后处理来解决这些缺陷。最流行的最先进的统计后处理技术之一是集合模型输出统计 (EMOS)，它提供了所研究天气量的完整预测分布。我们提出了一种新的 EMOS 模型，用于校准风速集合预测，其中预测分布是广义极值 (GEV) 分布，左截断为零 (TGEV)。截断纠正了基于 GEV 分布的 EMOS 模型偶尔预测负风速值的缺点，而不影响其有利特性。新模型在三个不同的集合预测系统提供的四个风速集合预测数据集上进行了测试，涵盖了不同的地理域和时间段。TGEV EMOS 模型的预测技巧与截断正态、对数正态和 GEV 方法以及原始和气候预测的预测性能进行了比较。结果验证了新型 TGEV EMOS 方法的有利特性。新模型在三个不同的集合预测系统提供的四个风速集合预测数据集上进行了测试，涵盖了不同的地理域和时间段。TGEV EMOS 模型的预测技巧与截断正态、对数正态和 GEV 方法以及原始和气候预测的预测性能进行了比较。结果验证了新型 TGEV EMOS 方法的有利特性。新模型在三个不同的集合预测系统提供的四个风速集合预测数据集上进行了测试，涵盖了不同的地理域和时间段。TGEV EMOS 模型的预测技巧与截断正态、对数正态和 GEV 方法以及原始和气候预测的预测性能进行了比较。结果验证了新型 TGEV EMOS 方法的有利特性。