Predicting the intensity and amount of sunlight as a function of location and time is an essential component in identifying promising locations for economical solar farming. Although weather models and irradiance data are relatively abundant, these have yet, to our knowledge, been hybridized on a continental scale. Rather, much of the emphasis in the literature has been on short‐term localized forecasting. This is probably because the amount of data involved in a more global analysis is prohibitive with the canonical toolkit, via the Gaussian process (GP). Here we show how GP surrogate and discrepancy models can be combined to tractably and accurately predict solar irradiance on time‐aggregated and daily scales with measurements at thousands of sites across the continental United States. Our results establish short‐term accuracy of bias‐corrected weather‐based simulation of irradiance, when realizations are available in real space‐time (eg, in future days), and provide accurate surrogates for smoothing in the more common situation where reliable weather data is not available (eg, in future years).

中文翻译：

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太阳辐照度的综合模拟和现场数据

预测日光的强度和数量与位置和时间的函数关系，对于确定经济的太阳能农业有希望的位置至关重要。尽管天气模型和辐照度数据相对丰富，但据我们所知，它们尚未在大陆范围内混合使用。相反，文献中的许多重点都放在短期本地化预测上。这可能是因为规范工具无法通过高斯过程（GP）进行更全面的分析，从而无法进行全面的分析。在这里，我们展示了GP替代模型和差异模型如何结合使用，以精确地预测时间和日尺度上的太阳辐照度，并在美国大陆上千个站点进行了测量。