Abstract. Many applications require wind gust estimates at very different atmospheric height levels. For example, the renewable energy sector is interested in wind and gust predictions at the hub height of a wind power plant. However, numerical weather prediction models typically only derive estimates for wind gusts at the standard measurement height of 10 m above the land surface. Here, we present a statistical post-processing method to derive a conditional distribution for hourly peak wind speed as a function of height. The conditioning variables are taken from the COSMO-REA6 regional reanalysis. The post-processing method was trained using peak wind speed observations at five vertical levels between 10 and 250 m from the Hamburg Weather Mast. The statistical post-processing method is based on a censored generalized extreme value (cGEV) distribution with non-homogeneous parameters. We use a least absolute shrinkage and selection operator to select the most informative variables. Vertical variations of the cGEV parameters are approximated using Legendre polynomials, such that predictions may be derived at any desired vertical height. Further, the Pickands dependence function is used to assess dependencies between gusts at different heights. The most important predictors are the 10 m gust diagnostic, the barotropic and the baroclinic mode of absolute horizontal wind speed, the mean absolute horizontal wind at 700 hPa , the surface pressure tendency, and the lifted index. Proper scores show improvements of up to 60 % with respect to climatology, especially at higher vertical levels. The post-processing model with a Legendre approximation is able to provide reliable predictions of gusts' statistics at non-observed intermediate levels. The strength of dependency between gusts at different levels is non-homogeneous and strongly modulated by the vertical stability of the atmosphere.

中文翻译：

---

使用多元极值理论的区域再分析的阵风统计垂直剖面

摘要。许多应用需要在非常不同的大气高度水平上估算阵风。例如，可再生能源部门对风力发电厂轮毂高度的风和阵风预测感兴趣。但是，数值天气预报模型通常仅得出地表上方 10 m 标准测量高度处的阵风估计值。在这里，我们提出了一种统计后处理方法，以推导出每小时峰值风速作为高度函数的条件分布。条件变量取自 COSMO-REA6 区域再分析。使用距离汉堡气象桅杆 10 至 250 m 之间五个垂直高度的峰值风速观测值训练后处理方法。统计后处理方法基于具有非齐次参数的删失广义极值 (cGEV) 分布。我们使用最小绝对收缩和选择运算符来选择信息量最大的变量。使用勒让德多项式近似 cGEV 参数的垂直变化，从而可以在任何所需的垂直高度导出预测。此外，Pickands 相关函数用于评估不同高度的阵风之间的相关性。最重要的预测因子是 10 m 阵风诊断、绝对水平风速的正压和斜压模式、700 hPa 的平均绝对水平风、表面压力趋势和抬升指数。适当的分数表明气候学方面的改进高达 60%，尤其是在更高的垂直水平上。具有勒让德近似的后处理模型能够在未观察到的中间级别提供对阵风统计数据的可靠预测。不同级别阵风之间的依赖强度是不均匀的，并且受到大气垂直稳定性的强烈调制。