Regridding uncertainty for statistical downscaling of solar radiation

Bailey, Maggie D.; Nychka, Douglas; Sengupta, Manajit; Habte, Aron; Xie, Yu; Bandyopadhyay, Soutir

doi:https://doi.org/10.5194/ascmo-9-103-2023

Articles | Volume 9, issue 2

https://doi.org/10.5194/ascmo-9-103-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/ascmo-9-103-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 9, issue 2

04 Dec 2023

| 04 Dec 2023

Regridding uncertainty for statistical downscaling of solar radiation

Maggie D. Bailey, Douglas Nychka, Manajit Sengupta, Aron Habte, Yu Xie, and Soutir Bandyopadhyay

Data sets

The NA-CORDEX dataset, version 1.0 L. O. Mearns https://doi.org/10.5065/D6SJ1JCH

Model code and software

Regridding Uncertainty for Statistical Downscaling of Solar Radiation. In Advances in Statistical Climatology, Meteorology, and Oceanography M. Bailey https://doi.org/10.5281/zenodo.10054998

Short summary

To ensure photovoltaic (PV) plants last, we need to understand how climate change affects solar radiation. Climate models help predict future radiation for PV plants, but there is often uncertainty. We explore this uncertainty and its impact on building PV plants. We highlight the importance of considering uncertainties for accurate planning and management. A California case study shows a practical application for the solar industry.