Articles | Volume 11, issue 1
https://doi.org/10.5194/ascmo-11-89-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/ascmo-11-89-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
11 Jun 2025
| 11 Jun 2025
Machine-learning-based probabilistic forecasting of solar irradiance in Chile
Faculty of Informatics, University of Debrecen, Debrecen, Hungary
Julio C. Marín
Department of Meteorology, University of Valparaíso, Valparaíso, Chile
Center for Atmospheric Studies and Climate Change (CEACC), University of Valparaíso, Valparaíso, Chile
Omar Cuevas
Center for Atmospheric Studies and Climate Change (CEACC), University of Valparaíso, Valparaíso, Chile
Institute of Physics and Astronomy, University of Valparaíso, Valparaíso, Chile
Mailiu Díaz
Faculty of Engineering, Andrés Bello University, Viña del Mar, Chile
Marianna Szabó
Faculty of Informatics, University of Debrecen, Debrecen, Hungary
Orietta Nicolis
Faculty of Engineering, Andrés Bello University, Viña del Mar, Chile
Mária Lakatos
Faculty of Informatics, University of Debrecen, Debrecen, Hungary
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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.
This paper assesses the skill of probabilistic forecasts of solar irradiance in the northern...
