Ensemble model output statistics as a probabilistic site-adaptation tool for satellite-derived and reanalysis solar irradiance.

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. [ABSTRACT FROM AUTHOR]