Comparing calibrated analog and dynamical ensemble solar forecasts

Ensemble modeling is a chief strategy for probabilistic forecasting. In weather forecasting, analog ensemble, which operates under the principle that weather patterns often repeat, and dynamical ensemble, which generates equally likely trajectories of future weather by perturbing the initial and boundary conditions, constitute the two most common approaches to making ensembles. That said, in the field of solar forecasting, nor is there any head-to-head comparison made thus far in regard to understanding the relative performance of these two competing approaches; this work seeks to fill the gap. Four years (2017–2020) of operational forecasts, at seven locations, from the European Centre for Medium-Range Weather Forecasts (ECMWF) are used, and both the raw and post-processed versions of the ensemble irradiance forecasts are verified in a fair and thorough fashion. Three classical post-processing methods, namely, Bayesian model averaging, nonhomogeneous Gaussian regression, and quantile regression, are applied to both the analog ensemble forecasts derived from ECMWF’s high-resolution control forecasts and dynamical ensemble forecasts from ECMWF’s Ensemble Prediction System. It is found that analog ensemble before post-processing possesses some advantage in terms of calibration over dynamical ensemble; their average reliability values are 0.6 W/m${}^2$ and 8.2 W/m${}^2$, respectively. However, dynamical ensemble after post-processing becomes generally more attractive, obtaining an average continuous ranked probability score of 49.0 W/m${}^2$, against 51.7 W/m${}^2$ for AnEn.