Previously, in the study by Yang [J. Renewable Sustainable Energy 12, 016102 (2020)], probabilistic site adaptation was demonstrated for the first time. This technique leverages the ensemble model output statistics (EMOS), post-processes the empirical distribution formed by m gridded solar irradiance estimates from different satellite-derived and reanalysis databases, and thus obtains a final predictive distribution of the site-adapted irradiance, which has a normal density. That said, three questions were later thought of: (1) can post-processing the clear-sky index, instead of irradiance, lead to better site-adaptation performance; (2) will the parameter estimation strategy substantially affect model performance; and (3) how does the normality assumption hold in reality? In this paper, I revisit the probabilistic site-adaptation problem and aim to address these questions. In summary, it is found that (1) building EMOS models on irradiance and on the clear-sky index leads to similar model performance; (2) the choice of minimizing the continuous ranked probability score and the ignorance score needs to be tailored to the problem at hand; and (3) using a truncated normal predictive distribution in EMOS does not seem to possess an advantage over using a normal predictive distribution.

中文翻译：

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集成模型输出统计作为太阳辐照度的概率站点适应工具：重新审视

此前，在杨[J. Renewable Sustainable Energy 12, 016102 (2020)]，首次展示了概率站点适应。该技术利用集合模型输出统计数据 (EMOS)，对来自不同卫星衍生和再分析数据库的 m 个网格太阳辐照度估计形成的经验分布进行后处理，从而获得站点适应辐照度的最终预测分布，该分布具有正常密度。也就是说，后来想到了三个问题：（1）是否可以对晴天指数进行后处理，而不是辐照度，从而获得更好的场地适应性能；(2) 参数估计策略是否会实质性地影响模型性能；(3) 正态性假设在现实中如何成立？在本文中，我重新审视了概率站点适应问题，旨在解决这些问题。综上所述，发现 (1) 在辐照度和晴空指数上建立 EMOS 模型导致相似的模型性能；(2) 最小化连续排序概率分数和无知分数的选择需要根据手头的问题量身定做；(3) 在 EMOS 中使用截断的正态预测分布似乎并不比使用正态预测分布具有优势。