Surface winds can vary substantially from one minute to the next, so there is scope for studying its variation on this fine time scale. Restricting to the month of June to minimize seasonality, this work develops a range of machine learning models for generating realistic time series of surface wind vectors at a site in Lamont, Oklahoma based on more than 30 years of high quality measurements at the minute time scale. Such a generator could be used as an input into models from a range of disciplines, notably for wind energy, but also wildfire spread and aviation, among others. The data show complex diurnal structures in both wind speed and direction that would be challenging to capture with standard time series models, so we consider a number of machine learning approaches to producing a stochastic wind generator based on time vector-quantized variational autoencoders. We consider generating a day's worth of data at a time and generating a day of wind vectors conditional on the previous day's winds. We also study methods for incorporating a discrete weather state variable in the generator. We evaluate the generators using a wide range of formal and informal methods. The best of these generators can capture many but not all of the complex features present in the observational data. In particular, the best of our approaches accurately mimic diurnal changes in wind volatility but struggle to match the observed distribution of extreme wind speeds.

翻译：地面风速可能在分钟尺度上发生显著变化，因此有必要研究其在该精细时间尺度上的演变。为最小化季节性影响，本研究仅关注六月，基于位于俄克拉荷马州拉蒙特的站点超过30年的分钟尺度高质量测量数据，开发了一系列用于生成真实地表风矢量时间序列的机器学习模型。此类生成器可作为多学科模型的输入，尤其在风能领域，同时也可应用于野火蔓延、航空等方向。数据显示风速和风向均存在复杂的日变化结构，传统时间序列模型难以捕捉这些特征，因此我们采用多种机器学习方法，基于时间向量量化变分自编码器构建随机风生成器。我们考虑了逐日生成全天数据的方法，以及基于前一日风况条件生成次日风矢量的方案，并研究了在生成器中融入离散天气状态变量的技术。通过广泛的定性与定量评估，最优生成器能够捕捉观测数据中大部分而非全部复杂特征。具体而言，我们的最佳方法能准确模拟风速变化性的日循环模式，但在匹配极端风速的观测分布方面仍存在不足。