Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC)
The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under various climate change scenarios.
Sup3rCC is downscaled Global Climate Model (GCM) data. The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data (linked below as "Sup3r GitHub Repo"). The data includes both historical and future weather years, although the historical years represent the historical climate, not the actual historical weather that we experienced. You cannot use Sup3rCC data to study historical weather events, although other sup3r datasets may be intended for this.
The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar capacity. Please note that all climate change data is only a representation of the possible future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty.
Citation Formats
National Renewable Energy Laboratory (NREL). (2023). Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) [data set]. Retrieved from https://dx.doi.org/10.25984/1970814.
Buster, Grant, Benton, Brandon, Glaws, Andrew, and King, Ryan. Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC). United States: N.p., 19 Apr, 2023. Web. doi: 10.25984/1970814.
Buster, Grant, Benton, Brandon, Glaws, Andrew, & King, Ryan. Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC). United States. https://dx.doi.org/10.25984/1970814
Buster, Grant, Benton, Brandon, Glaws, Andrew, and King, Ryan. 2023. "Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC)". United States. https://dx.doi.org/10.25984/1970814. https://data.openei.org/submissions/5839.
@div{oedi_5839, title = {Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC)}, author = {Buster, Grant, Benton, Brandon, Glaws, Andrew, and King, Ryan.}, abstractNote = {The Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) data is a collection of 4km hourly wind, solar, temperature, humidity, and pressure fields for the contiguous United States under various climate change scenarios.
Sup3rCC is downscaled Global Climate Model (GCM) data. The downscaling process was performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data (linked below as "Sup3r GitHub Repo"). The data includes both historical and future weather years, although the historical years represent the historical climate, not the actual historical weather that we experienced. You cannot use Sup3rCC data to study historical weather events, although other sup3r datasets may be intended for this.
The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar capacity. Please note that all climate change data is only a representation of the possible future climate and contains significant uncertainty. Analysis of multiple climate change scenarios and multiple climate models can help quantify this uncertainty.
}, doi = {10.25984/1970814}, url = {https://data.openei.org/submissions/5839}, journal = {}, number = , volume = , place = {United States}, year = {2023}, month = {04}}
https://dx.doi.org/10.25984/1970814
Details
Data from Apr 19, 2023
Last updated Mar 3, 2025
Submitted Apr 20, 2023
Organization
National Renewable Energy Laboratory (NREL)
Contact
Grant Buster
720.495.6245
Authors
Research Areas
Keywords
energy, power, solar, wind, temperature, windspeed, GHI, DNI, irradiance, climate change, machine learning, generative machine learning, resource data, weather, climate, contiguous United States, generative adversarial learning, GAN, high-resolution, renewable energy, energy systems, power systems, energy planning, Sup3rCC, generative adversarial networkDOE Project Details
Project Name National Transmission Planning Study (NTPS)
Project Number 38843
