Accurate estimates of wind speeds at wind turbine hub heights are crucial for both wind resource assessment and day-to-day management of electricity grids with high renewable penetration. In the absence of direct measurements, parametric models are commonly used to extrapolate wind speeds from observed heights to turbine heights. Recent literature has proposed extensions to allow for spatially or temporally varying vertical wind gradients, that is, the rate at which wind speed changes with height. However, these approaches typically assume that reference height and hub height measurements are available at the same locations, which limits their applicability in operational settings where meteorological stations and wind farms are spatially separated. In this paper, we develop a two-step spatio-temporal framework to estimate turbine height wind speeds using only open-access observations from sparse meteorological stations. First, a non-parametric generalized additive model is trained on reanalysis data to perform vertical height extrapolation. Second, a spatial Gaussian process model interpolates these hub-height estimates to wind farm locations while explicitly propagating uncertainty from the height extrapolation stage. The proposed framework enables the construction of high-resolution, sub-hourly turbine-height wind speed time series and spatial wind maps using data available in real time, capabilities not provided by existing reanalysis products. We further provide calibrated uncertainty estimates that account for both vertical extrapolation and spatial interpolation errors. The approach is validated using hub-height measurements from seven operational wind farms in Ireland, demonstrating improved accuracy relative to ERA5 reanalysis while relying solely on real-time, open-access data.

翻译：准确估算风力发电机轮毂高度风速对于风资源评估和高可再生能源渗透电网的日常管理至关重要。在缺乏直接测量的情况下，通常采用参数化模型将观测高度的风速外推至风机高度。近期研究提出了允许垂直风梯度（即风速随高度变化的速率）在空间或时间上变化的扩展方法。然而，这些方法通常假设参考高度与轮毂高度测量数据位于相同位置，这限制了其在气象站与风电场空间分离的实际运行场景中的应用。本文开发了一种两步时空框架，仅利用稀疏气象站点的开放观测数据来估算风机高度风速。首先，基于再分析数据训练非参数广义可加模型以执行垂直高度外推。其次，通过空间高斯过程模型将这些轮毂高度估算值插值到风电场位置，同时显式传递高度外推阶段的不确定性。所提框架能够利用实时可用数据构建高分辨率、亚小时级的轮毂高度风速时间序列与空间风场图，这是现有再分析产品所不具备的能力。我们进一步提供了经过校准的不确定性估计，同时考虑了垂直外推与空间插值误差。该方法通过爱尔兰七个运行风电场的轮毂高度实测数据进行了验证，结果表明在仅依赖实时开放数据的前提下，其精度相较于ERA5再分析数据有显著提升。