Preprints
https://doi.org/10.5194/egusphere-2025-3947
https://doi.org/10.5194/egusphere-2025-3947
04 Sep 2025
 | 04 Sep 2025
Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

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

Abstract. This study produces wind and solar power generation estimates derived from the US Department of Energy’s Simple Cloud-Resolving Energy Exascale Earth System Model (E3SM) Atmosphere Model (SCREAM) by leveraging regional mesh refinement over California (CARRM) simulations at 3.25 km and 800 m horizontal resolutions, using the Python wrapper of System Advisor Model (PySAM). The resulting wind and solar energy generation estimates are compared to monthly capacity factors from the Energy Information Administration (EIA), the High-Resolution Rapid Refresh (HRRR; 3 km resolution) forecast model, and the E3SM North American Regionally Refined Model (NARRM; 25 km resolution). We systematically assess the impacts of generation modeling assumptions, meteorological models, and horizontal resolution. Results show that resolution plays a dominant role for wind energy: increasing from 25 km to 3.25 km brings qualitative and quantitative improvements, most notably by resolving the phase error in the seasonal cycle found in coarser simulations. However, further refinement to 800 m offers minimal gains. SCREAM’s performance for solar generation surpasses HRRR, likely due to more accurate surface radiation. The sensitivity of PySAM to system configuration, particularly for axis-tracking modeling in photovoltaics, is also highlighted. Overall, SCREAM-RRM shows strong potential for high-resolution energy assessments, with future progress depending on more in situ observations and clearer quantification of generation modeling uncertainties.

Competing interests: At least one of the (co-)authors is a member of the editorial board of Geoscientific Model Development. The authors declare that they have no other competing interests.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.
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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

Status: open (until 30 Oct 2025)

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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://zenodo.org/records/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://zenodo.org/records/16809290

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

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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.
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