Towards a leadtime, scale and dynamical feature dependent postprocessing for wind gusts (C05)

针对阵风的交付周期、规模和动态特征相关的后处理 (C05)

• 批准号: 280704579
• 金额: --
• 依托单位: Department Troposphärenforschung (IMK-TRO)
• 依托单位国家: 德国
• 项目类别: CRC/Transregios
• 资助国家: 德国
• 项目状态: 未结题
• 来源: https://gepris.dfg.de/gepris/projekt/280704579?language=en
• 关键词: Towards; leadtime; scale; dynamical; feature

Wind gusts associated with wintertime low-pressure systems are amongst the most significant natural haz-ards in central Europe. Here we will adapt spatial verification methods originally developed for precipitation to ensemble weather predictions in order to separate contributions to forecast error and uncertainty from spatial (or temporal) shifts of the low-pressure system and the associated smaller-scale wind features, as well as size and intensity differences. Remaining errors will then mainly be due to local conditions, turbu-lence and convection, which can be statistically corrected training modern machine learning algorithms.

与冬季低压系统相关的阵风是中欧最重要的自然灾害之一。在这里，我们将适应空间验证方法最初开发的降水集合天气预报，以分离的贡献预测误差和不确定性的空间（或时间）的变化的低压系统和相关的小尺度风的功能，以及大小和强度的差异。剩余的误差将主要归因于局部条件、湍流和对流，这可以通过训练现代机器学习算法进行统计校正。