Statistical spatiotemporal environmental data analysis is rarely straightforward, with one having to face challenges relating to big data, non‐Gaussianity, nonstationarity, multiple scales of behavior, deterministic (numerical) model output, and more. One often has to rely heavily on good statistical parallel computing skills and sound knowledge of the application domain. The work of Tagle et al. (2020) overcomes all of these challenges, and is an excellent example of the tangible contributions spatiotemporal modeling and distribution theory can make to the environmental sciences at the policy level. In this discussion piece I focus on a few high‐level concepts in the paper of Tagle et al. (2020) that are relevant to related application domains. I also provide some technical suggestions that could be used to facilitate inference.

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评论：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

统计时空环境数据分析很少是直截了当的，必须面临与大数据、非高斯性、非平稳性、多尺度行为、确定性（数值）模型输出等相关的挑战。人们常常不得不严重依赖良好的统计并行计算技能和应用领域的扎实知识。Tagle 等人的工作。(2020) 克服了所有这些挑战，是时空建模和分布理论可以在政策层面对环境科学做出切实贡献的一个很好的例子。在这篇讨论文章中，我关注 Tagle 等人的论文中的一些高级概念。(2020) 与相关应用领域相关。我还提供了一些可用于促进推理的技术建议。