Simulation of wind and solar energy generation over California with E3SM SCREAM regionally refined models at 3.25&thinsp;km and 800&thinsp;m resolutions

Zhang, Jishi; Golaz, Jean-Christophe; Signorotti, Matthew Vincent; Lee, Hsiang-He; Bogenschutz, Peter; Monteagudo, Minda; Ullrich, Paul Aaron; Arthur, Robert S.; Po-Chedley, Stephen; Cameron-Smith, Philip; Watson, Jean-Paul

doi:10.5194/gmd-19-4513-2026

Articles | Volume 19, issue 10

https://doi.org/10.5194/gmd-19-4513-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

https://doi.org/10.5194/gmd-19-4513-2026

© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 19, issue 10

Model evaluation paper

|

27 May 2026

Model evaluation paper |

| 27 May 2026

Simulation of wind and solar energy generation over California with E3SM SCREAM regionally refined models at 3.25 km and 800 m resolutions

Jishi Zhang, Jean-Christophe Golaz, Matthew Vincent Signorotti, Hsiang-He Lee, Peter Bogenschutz, Minda Monteagudo, Paul Aaron Ullrich, Robert S. Arthur, Stephen Po-Chedley, Philip Cameron-Smith, and Jean-Paul Watson

Data sets

Code, model, and analysis data for simulation of wind and solar energy generation over California with E3SM SCREAM regionally refined models at 3.25 km and 800 m resolutions Jishi Zhang https://doi.org/10.5281/zenodo.16809290

Model code and software

Code, model, and analysis data for simulation of wind and solar energy generation over California with E3SM SCREAM regionally refined models at 3.25 km and 800 m resolutions Jishi Zhang https://doi.org/10.5281/zenodo.16809290

Short summary

We ran a convection-permitting model with regional mesh refinement (3.25 km and 800 m) to simulate present-day wind and solar capacity factors over California, coupling it to an energy generation model. The high-resolution models captured realistic seasonal and diurnal cycles, with wind markedly better than a 25 km model and solar outperforming a 3 km operational forecast. We highlight the critical role of resolution, modeling assumptions, and data reliability in renewable energy assessment.