Spatial solar forecast verification with the neighborhood method and automatic threshold segmentation

Numerical weather prediction (NWP) has hitherto been the default tool for providing day-ahead forecasting services to the solar energy industry. Rapid advancements in solar forecasting using NWP call for more appropriate forecast verification procedures. Current solar forecast verification is almost always carried out through ground-based radiometric data collected at point locations. Consequently, spatial features embedded in the gridded NWP forecasts cannot be verified. This study presents the spatial verification of solar irradiance forecasts using the neighborhood method, with the main goal of emphasizing the importance of such verification procedures. By applying spatial smoothing one establishes a way to directly compare the observed and forecast fields, and concurrently, mitigate verification errors that may arise from small-scale spatial displacements. Within this framework, two variants of the neighborhood-based verification, namely, the fraction-field method and the upscaling method, are examined with respect to two reanalysis products, namely, ERA5 and MERRA-2. The results suggest that, in comparison to the upscaling method, the fraction-field method can better quantify forecast performance by providing fractions skill scores. On top of the traditional neighborhood approach, which involves the subjective selection of threshold for dichotomization, an automatic threshold segmentation method based on the three-component skew-normal mixture model is proposed to resolve the issue, which can also lead to substantial time savings in data processing. Given the spatio-temporal attributes and benefits of visualization, spatial verification is anticipated to serve as a complementary practice to the current mainstream point-location forecast verification.