Saudi Arabia has recently established its renewable energy targets as part of its “Vision 2030” proposal, which represents a roadmap for reducing the country's dependence on oil over the next decade. This study provides a foundational assessment of the wind resource in Saudi Arabia that serves as a guide for the development of the outlined wind energy component. The assessment is based on a new high‐resolution weather simulation of the region generated with the Weather Research and Forecasting (WRF) model. Furthermore, we propose a spatiotemporal stochastic generator of daily wind speeds that assists in characterizing the uncertainty of the energy estimates. The stochastic generator considers a vector autoregressive structure in time, with innovations from a novel biresolution model based on a skew‐t distribution with a low‐dimensional latent structure. Estimation of the spatial model parameters is performed using a Monte Carlo expectation‐maximization (EM) algorithm, which achieves inference over approximately 184 million points and enables to capture the spatial patterns of the higher order moments that typically characterize high‐resolution wind fields. Our results identify regions along the western mountain ranges and central escarpments that are suitable for the deployment of wind energy infrastructure. According to the assessment, between 30 and 70% of the national electricity demand could be met by wind energy.

中文翻译：

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用于评估沙特阿拉伯风能资源的高分辨率双层偏斜随机发生器

沙特阿拉伯最近将其可再生能源目标作为其“2030 年愿景”提案的一部分，该提案代表了该国未来十年减少对石油依赖的路线图。本研究提供了对沙特阿拉伯风资源的基础评估，可作为概述风能组件开发的指南。该评估基于天气研究和预测 (WRF) 模型生成的该地区新的高分辨率天气模拟。此外，我们提出了一种每日风速的时空随机发生器，有助于表征能量估计的不确定性。随机生成器及时考虑向量自回归结构，基于具有低维潜在结构的 skew-t 分布的新型双分辨率模型的创新。空间模型参数的估计是使用蒙特卡罗期望最大化 (EM) 算法进行的，该算法实现了大约 1.84 亿个点的推理，并能够捕获通常表征高分辨率风场的高阶矩的空间模式。我们的结果确定了适合部署风能基础设施的西部山脉和中部悬崖地区。根据评估，风能可以满足全国30%至70%的电力需求。它实现了大约 1.84 亿个点的推理，并能够捕获通常表征高分辨率风场的高阶矩的空间模式。我们的结果确定了适合部署风能基础设施的西部山脉和中部悬崖地区。根据评估，风能可以满足全国30%至70%的电力需求。它实现了大约 1.84 亿个点的推理，并能够捕获通常表征高分辨率风场的高阶矩的空间模式。我们的结果确定了适合部署风能基础设施的西部山脉和中部悬崖地区。根据评估，风能可以满足全国30%至70%的电力需求。