In this study, we have proposed an automated classification approach to identify meaningful patterns in wind field data. Utilizing an extensive simulated wind database, we have demonstrated that the proposed approach can identify low‐level jets, near‐uniform profiles, and other patterns in a reliable manner. We have studied the dependence of these wind profile patterns on locations (eg, offshore vs onshore), seasons, and diurnal cycles. Furthermore, we have found that the probability distributions of some of the patterns depend on the underlying planetary boundary layer schemes in a significant way. The future potential of the proposed approach in wind resource assessment and, more generally, in mesoscale model parameterization improvement is touched upon in this paper.

中文翻译：

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根据多物理场集合预报对风场模式进行自动分类

在这项研究中，我们提出了一种自动分类方法来识别风场数据中有意义的模式。利用广泛的模拟风数据库，我们证明了该方法可以可靠地识别低空急流，近乎均匀的剖面和其他模式。我们研究了这些风廓线模式对位置（例如，海上与陆上），季节和昼夜周期的依赖性。此外，我们发现某些模式的概率分布在很大程度上取决于基础行星边界层方案。本文探讨了该方法在风资源评估以及更广泛的中尺度模型参数化改进方面的未来潜力。