Wind-speed processes exhibit substantial temporal variability and spatial dependence, yet volatility dynamics across monitoring networks remain relatively unexplored. This study investigates the spatiotemporal behaviour of wind-speed volatility using daily observations from 141 stations in Northern Italy over 2016--2021, with measurements at 10 m and 100 m enabling the analysis of spatial and vertical dependence. We adopt a parsimonious spatiotemporal volatility framework based on GARCH-type dynamics, in which conditional variance depends on past local shocks and spatially aggregated information from neighbouring stations. The approach combines a spatial mean specification with structured volatility models using distance-based and directionally informed weight matrices. Results show that properly modelling spatial dependence in the mean is essential for well-behaved residuals and reliable inference. Forecast performance is strongly driven by the mean specification: flexible structures perform better when residual spatial dependence remains, while parsimonious distance-based models yield robust out-of-sample forecasts once spatial interactions are captured. Persistence increases with height, and a multivariate extension reveals cross-height dependence.

翻译：风速过程表现出显著的时间变异性和空间依赖性，然而监测网络中波动率动态的研究仍相对匮乏。本研究基于2016—2021年意大利北部141个气象站的逐日观测数据，结合10米与100米高度的测量值，探讨了风速波动的时空行为，并分析了空间与垂直依赖性。我们采用基于GARCH型动态的简约时空波动率框架，其中条件方差取决于过去的局部冲击及邻站的空间聚合信息。该方法将空间均值设定与结构化波动率模型相结合，利用基于距离和方向信息的权重矩阵。结果表明：恰当建模均值中的空间依赖性，是获取良好残差与可靠推断的关键。预测性能受均值设定主导：当残差空间依赖性未完全消除时，灵活结构表现更优；而一旦捕获空间交互作用，简约的距离型模型能产生稳健的样本外预测。波动持续性随高度增加而增强，多变量扩展揭示了跨高度依赖性。