Probabilistic post-processing of gridded atmospheric variables and its application to site adaptation of shortwave solar radiation

Abstract Site adaptation refers to procedures for correcting systematic errors in an extended period of gridded modeled data using a short period of ground-based measurements used as unbiased reference. Traditionally, site adaptation leverages a single gridded product and issues point predictions. Currently, remote-sensed and reanalysis data are available from different sources providing multiple versions of estimates of a same atmospheric variable, for any location on Earth. These datasets allow what is called an ensemble prediction. In this regard, this contribution proposes a probabilistic site-adaption framework, and describes how one can use parametric and nonparametric techniques within the framework. On top of the stand-alone probabilistic site-adaption methods, heuristics are optionally used to combine quantiles, to further improve the accuracy of site adaptation. To exemplify the framework, global horizontal irradiance data from 26 sites worldwide with different climate characteristics and weather regimes are used to side-adapt the corresponding predictions from up to 5 satellite-derived databases and 2 reanalyses spanning various periods, collectively. It is found that the proposed site-adaptation methods using multiple gridded products are able to attain, on average, a 5 W/m 2 reduction in continuous ranked probability score than that leveraging just a single product.