Combining quantiles of calibrated solar forecasts from ensemble numerical weather prediction

This work is concerned with optimally combining quantiles of several post-processed versions of ensemble solar forecasts, which is new in this field. Numerical weather prediction (NWP) serves grid integration of solar energy by issuing dynamical ensemble irradiance forecasts. However, these ensemble members often suffer from under-dispersion, which motivates statistical calibration via quantile regression (QR) or ensemble model output statistics (EMOS). Given the numerous variants of QR and EMOS, it is generally unclear which variant offers the best performance under what situation, which further motivates combining quantile forecasts. A framework for combining solar forecasts in the form of quantiles is proposed, and a constrained quantile regression averaging scheme is used to exemplify the framework. Using the strictly proper pinball loss, ensemble irradiance forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System are first post-processed using five QR variants and five EMOS variants, and then combined through a linear program. It is found that combining quantiles is an effective strategy that can further improve the calibrated ECMWF forecasts across all locations herein considered.