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sup3ruhi (Super Resolution for Renewable Resource Data and Urban Heat Islands) [SWR-25-05]

RESOURCE

Publicly Accessible Repository
https://github.com/NREL/sup3ruhi
https://doi.org/10.11578/dc.20250326.1
Documentation
https://doi.org/10.48550/arXiv.2411.05952

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Abstract

Urban heat is a growing concern, particularly in dense metropolitan areas where high temperatures increase the risk of heat-related illness and drive energy expenses for cooling. Estimating the effects of urban heat remains a challenge due to limitations in describing the built environment, computational constraints, and the need for high-resolution data. This software presents open-source, computationally efficient machine learning methods that enhance the accuracy of urban temperature estimates compared to historical reanalysis data. Models trained using this software have been applied to urban microclimates in Los Angeles and Seattle showing greater accuracy and less bias when compared to low-resolution reanalysis datasets like ERA5 and even when compared to high-resolution mesoscale numerical weather models like WRF with an urban canopy model. Initial findings highlight how machine learning can support urban heat resilience planning by enabling improved assessments of local heat islands, mitigation strategies, and their energy implications. This software is an extension of (sup3r). This software supports the following publication: Buster, Grant, et al. Tackling Extreme Urban Heat: A Machine Learning Approach to Assess the Impacts of Climate Change and the Efficacy of Climate Adaptation Strategies in Urban Microclimates. arXiv:2411.05952, arXiv, 8 Nov. 2024. arXiv.org, https://doi.org/10.48550/arXiv.2411.05952. And has related public data records available at: Buster, Grant,  More>>
Developers:
Buster, Grant [1] ;  Benton, Brandon [1] ;  Cox, Jordan [1] ;  King, Ryan [1]
  1. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Release Date:
2025-03-10
Project Type:
Open Source, Publicly Available Repository
Software Type:
Scientific
Programming Languages:
Python
Licenses:
BSD 3-clause "New" or "Revised" License
Sponsoring Org.:
Code ID:
153282
Site Accession Number:
NREL SWR-25-05
Research Org.:
National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Country of Origin:
United States

RESOURCE

Publicly Accessible Repository
https://github.com/NREL/sup3ruhi
https://doi.org/10.11578/dc.20250326.1
Documentation
https://doi.org/10.48550/arXiv.2411.05952

SAVE / SHARE



NREL Badge

Citation Formats

Buster, Grant, Benton, Brandon, Cox, Jordan, and King, Ryan. sup3ruhi (Super Resolution for Renewable Resource Data and Urban Heat Islands) [SWR-25-05]. Computer Software. https://github.com/NREL/sup3ruhi. USDOE Laboratory Directed Research and Development (LDRD) Program. 10 Mar. 2025. Web. doi:10.11578/dc.20250326.1.
Buster, Grant, Benton, Brandon, Cox, Jordan, & King, Ryan. (2025, March 10). sup3ruhi (Super Resolution for Renewable Resource Data and Urban Heat Islands) [SWR-25-05]. [Computer software]. https://github.com/NREL/sup3ruhi. https://doi.org/10.11578/dc.20250326.1.
Buster, Grant, Benton, Brandon, Cox, Jordan, and King, Ryan. "sup3ruhi (Super Resolution for Renewable Resource Data and Urban Heat Islands) [SWR-25-05]." Computer software. March 10, 2025. https://github.com/NREL/sup3ruhi. https://doi.org/10.11578/dc.20250326.1.
@misc{ doecode_153282,
title = {sup3ruhi (Super Resolution for Renewable Resource Data and Urban Heat Islands) [SWR-25-05]},
author = {Buster, Grant and Benton, Brandon and Cox, Jordan and King, Ryan},
abstractNote = {Urban heat is a growing concern, particularly in dense metropolitan areas where high temperatures increase the risk of heat-related illness and drive energy expenses for cooling. Estimating the effects of urban heat remains a challenge due to limitations in describing the built environment, computational constraints, and the need for high-resolution data. This software presents open-source, computationally efficient machine learning methods that enhance the accuracy of urban temperature estimates compared to historical reanalysis data. Models trained using this software have been applied to urban microclimates in Los Angeles and Seattle showing greater accuracy and less bias when compared to low-resolution reanalysis datasets like ERA5 and even when compared to high-resolution mesoscale numerical weather models like WRF with an urban canopy model. Initial findings highlight how machine learning can support urban heat resilience planning by enabling improved assessments of local heat islands, mitigation strategies, and their energy implications. This software is an extension of (sup3r). This software supports the following publication: Buster, Grant, et al. Tackling Extreme Urban Heat: A Machine Learning Approach to Assess the Impacts of Climate Change and the Efficacy of Climate Adaptation Strategies in Urban Microclimates. arXiv:2411.05952, arXiv, 8 Nov. 2024. arXiv.org, https://doi.org/10.48550/arXiv.2411.05952. And has related public data records available at: Buster, Grant, Cox, Jordan, Benton, Brandon, and King, Ryan. Super-Resolution for Renewable Resource Data and Urban Heat Islands (Sup3rUHI). United States: N.p., 16 Oct, 2024. Web. https://data.openei.org/submissions/6220.},
doi = {10.11578/dc.20250326.1},
url = {https://doi.org/10.11578/dc.20250326.1},
howpublished = {[Computer Software] \url{https://doi.org/10.11578/dc.20250326.1}},
year = {2025},
month = {mar}
}