Articles | Volume 11, issue 1
https://doi.org/10.5194/ascmo-11-89-2025
https://doi.org/10.5194/ascmo-11-89-2025
11 Jun 2025
 | 11 Jun 2025

Machine-learning-based probabilistic forecasting of solar irradiance in Chile

Sándor Baran, Julio C. Marín, Omar Cuevas, Mailiu Díaz, Marianna Szabó, Orietta Nicolis, and Mária Lakatos

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Short summary
This paper assesses the skill of probabilistic forecasts of solar irradiance in the northern regions of Chile. Raw ensemble forecast are calibrated using a parametric and a novel non-parametric machine-learning-based method. As the reference approach, the ensemble model output statistics are considered. We verify the superiority of the proposed non-parametric neural-network-based ensemble correction, resulting in more than 50 % improvement in prediction performance compared to the raw forecasts.
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