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.
Latest release: second-generation v0.2.2 Sup3rCC data with six GCMs across two climate scenarios (5x SSP2-4.5 and 1x SSP5-8.5). This version includes new generative models that have a larger effective receptive field for improved spatiotemporal weather dynamics over large-areas and improved diurnal shapes. This version also includes seasonal double-bias correction with Quantile Delta Mapping (QDM) at the 100km and 4km resolutions over a longer historical period (20-40 years) for greatly reduced historical climate bias. This release includes "sup3rcc_models_202412" that can be used with sup3r software v0.2.2 and phygnn v0.0.30 to reproduce this data release, and "sup3rcc_models_202507" that can be used with sup3r v0.2.3 and phygnn v0.0.31 with some additional non-ML performance improvements.
Complete Metadata
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| description | 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. Latest release: second-generation v0.2.2 Sup3rCC data with six GCMs across two climate scenarios (5x SSP2-4.5 and 1x SSP5-8.5). This version includes new generative models that have a larger effective receptive field for improved spatiotemporal weather dynamics over large-areas and improved diurnal shapes. This version also includes seasonal double-bias correction with Quantile Delta Mapping (QDM) at the 100km and 4km resolutions over a longer historical period (20-40 years) for greatly reduced historical climate bias. This release includes "sup3rcc_models_202412" that can be used with sup3r software v0.2.2 and phygnn v0.0.30 to reproduce this data release, and "sup3rcc_models_202507" that can be used with sup3r v0.2.3 and phygnn v0.0.31 with some additional non-ML performance improvements. |
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"description": "Released to the public as part of the Department of Energy's Open Energy Data Initiative, these data represent a serially complete collection of hourly 4km wind, solar, temperature, humidity, and pressure fields for the Continental United States under climate change scenarios. Sup3rCC is downscaled Global Climate Model (GCM) data. For example, the initial file set tagged "sup3rcc_conus_mriesm20_ssp585_r1i1p1f1" is downscaled from MRI ESM 2.0 for climate change scenario SSP5 8.5 and variant label r1i1p1f1. The downscaling process is performed using a generative machine learning approach called sup3r: Super-Resolution for Renewable Energy Resource Data. The data includes both historical and future weather years, although the historical years represent the historical average climate, not true historical weather. The Sup3rCC data is intended to help researchers study the impact of climate change on energy systems with high levels of wind and solar power generation. 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. "
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|
| DOI | 10.25984/1970814 |
| identifier | https://data.openei.org/submissions/5839 |
| issued | 2023-04-19T06:00:00Z |
| keyword |
[
"DNI",
"GAN",
"GHI",
"Sup3rCC",
"climate",
"climate change",
"contiguous United States",
"energy",
"energy planning",
"energy systems",
"generative adversarial learning",
"generative adversarial network",
"generative machine learning",
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"irradiance",
"machine learning",
"power",
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"renewable energy",
"resource data",
"solar",
"temperature",
"weather",
"wind",
"windspeed"
]
|
| landingPage | https://data.openei.org/submissions/5839 |
| license | https://creativecommons.org/licenses/by/4.0/ |
| modified | 2025-07-15T20:07:01Z |
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| title | Super-Resolution for Renewable Energy Resource Data with Climate Change Impacts (Sup3rCC) |
