Accurate wind pattern modelling is crucial for various applications, including renewable energy, agriculture, and climate adaptation. In this paper, we introduce the wrapped Gaussian spatial process (WGSP), as well as the projected Gaussian spatial process (PGSP) custom-tailored for South Africa's intricate wind behaviour. Unlike conventional models struggling with the circular nature of wind direction, the WGSP and PGSP adeptly incorporate circular statistics to address this challenge. Leveraging historical data sourced from meteorological stations throughout South Africa, the WGSP and PGSP significantly increase predictive accuracy while capturing the nuanced spatial dependencies inherent to wind patterns. The superiority of the PGSP model in capturing the structural characteristics of the South African wind data is evident. As opposed to the PGSP, the WGSP model is computationally less demanding, allows for the use of less informative priors, and its parameters are more easily interpretable. The implications of this study are far-reaching, offering potential benefits ranging from the optimisation of renewable energy systems to the informed decision-making in agriculture and climate adaptation strategies. The WGSP and PGSP emerge as robust and invaluable tools, facilitating precise modelling of wind patterns within the dynamic context of South Africa.

翻译：精准的风模式建模对于包括可再生能源、农业和气候适应在内的多种应用至关重要。本文针对南非复杂的风行为，提出了包裹高斯空间过程（WGSP）和投影高斯空间过程（PGSP）。与难以处理风向循环特性的传统模型不同，WGSP和PGSP巧妙地将循环统计学纳入其中以应对这一挑战。利用南非各地气象站的历史数据，WGSP和PGSP在捕捉风模式固有的空间依赖性的同时，显著提升了预测精度。PGSP模型在捕捉南非风数据结构特征方面表现出明显的优越性。与PGSP相反，WGSP模型计算需求较低，允许使用信息量较弱的先验，且其参数更易于解释。本研究的影响深远，为优化可再生能源系统、农业及气候适应策略中的知情的决策提供了潜在收益。WGSP和PGSP成为稳健且宝贵的工具，能够在南非的动态背景下精确建模风模式。