Ensemble model output statistics as a probabilistic site-adaptation tool for solar irradiance: A revisit

Abstract

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.