Suppose a location is within the spatial coverage of m gridded solar irradiance products, there is very little reason to rely on a single product, even if that product is known to be superior to its peers. In this paper, I discuss the ensemble performance of gridded irradiance estimates. First, I show the optimal convex combination of gridded irradiance estimates from different products almost always outperforms the best individual estimate under squared loss. Then, I extend the problem to the probability space and demonstrate how to construct predictive distributions for gridded irradiance estimates. Since the sample ensemble variances are often over- or under-dispersed, depending on the location, an ensemble model output statistics (EMOS) technique is used to correct such behaviors. The EMOS technique aims at minimizing the ignorance score, which is equivalent to maximizing the log-likelihood function of the underlying statistical model. In the language of solar engineers, EMOS is a probabilistic site-adaptation technique. At this point, this is the first work that (1) performs probabilistic site adaptation, (2) uses ensemble approaches for site adaptation, and (3) demonstrates formal probabilistic verification on site-adaptation problems.Suppose a location is within the spatial coverage of m gridded solar irradiance products, there is very little reason to rely on a single product, even if that product is known to be superior to its peers. In this paper, I discuss the ensemble performance of gridded irradiance estimates. First, I show the optimal convex combination of gridded irradiance estimates from different products almost always outperforms the best individual estimate under squared loss. Then, I extend the problem to the probability space and demonstrate how to construct predictive distributions for gridded irradiance estimates. Since the sample ensemble variances are often over- or under-dispersed, depending on the location, an ensemble model output statistics (EMOS) technique is used to correct such behaviors. The EMOS technique aims at minimizing the ignorance score, which is equivalent to maximizing the log-likelihood function of the underlying statistical model. In the language of solar engineers, EMOS is a probabilistic site-a...

中文翻译：

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集合模型输出统计作为卫星衍生和再分析太阳辐照度的概率站点适应工具

假设一个位置在 m 个网格太阳辐照度产品的空间覆盖范围内，则几乎没有理由依赖单一产品，即使该产品已知优于同类产品。在本文中，我讨论了网格辐照度估计的整体性能。首先，我展示了来自不同产品的网格辐照度估计的最佳凸组合几乎总是优于平方损失下的最佳个体估计。然后，我将问题扩展到概率空间，并演示如何构建网格辐照度估计的预测分布。由于样本集合方差通常过度分散或分散不足，具体取决于位置，因此使用集合模型输出统计 (EMOS) 技术来纠正此类行为。EMOS 技术旨在最小化无知分数，这相当于最大化底层统计模型的对数似然函数。用太阳能工程师的话来说，EMOS 是一种概率站点适应技术。在这一点上，这是 (1) 执行概率站点自适应，(2) 使用集成方法进行站点自适应，以及 (3) 演示对站点自适应问题的正式概率验证的第一项工作。假设一个位置在空间覆盖范围内在 m 个网格化太阳辐照度产品中，几乎没有理由依赖单一产品，即使该产品众所周知优于同行。在本文中，我讨论了网格辐照度估计的整体性能。第一的，我展示了来自不同产品的网格辐照度估计的最佳凸组合几乎总是优于平方损失下的最佳个体估计。然后，我将问题扩展到概率空间，并演示如何构建网格辐照度估计的预测分布。由于样本集合方差通常过度分散或分散不足，具体取决于位置，因此使用集合模型输出统计 (EMOS) 技术来纠正此类行为。EMOS 技术旨在最小化无知分数，这相当于最大化底层统计模型的对数似然函数。用太阳能工程师的话来说，EMOS 是一个概率站点——一个... 我将问题扩展到概率空间，并演示如何构建网格辐照度估计的预测分布。由于样本集合方差通常过度分散或分散不足，具体取决于位置，因此使用集合模型输出统计 (EMOS) 技术来纠正此类行为。EMOS 技术旨在最小化无知分数，这相当于最大化底层统计模型的对数似然函数。用太阳能工程师的话来说，EMOS 是一个概率站点——一个... 我将问题扩展到概率空间，并演示如何构建网格辐照度估计的预测分布。由于样本集合方差通常过度分散或分散不足，具体取决于位置，因此使用集合模型输出统计 (EMOS) 技术来纠正此类行为。EMOS 技术旨在最小化无知分数，这相当于最大化底层统计模型的对数似然函数。用太阳能工程师的话来说，EMOS 是一个概率站点——一个... 这相当于最大化底层统计模型的对数似然函数。用太阳能工程师的话来说，EMOS 是一个概率站点——一个... 这相当于最大化底层统计模型的对数似然函数。用太阳能工程师的话来说，EMOS 是一个概率站点——一个...