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High-resolution meteorology with climate change impacts from global climate model data using generative machine learning

Nature Energy (2024)Cite this article

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Abstract

As renewable energy generation increases, the impacts of weather and climate on energy generation and demand become critical to the reliability of the energy system. However, these impacts are often overlooked. Global climate models (GCMs) can be used to understand possible changes to our climate, but their coarse resolution makes them difficult to use in energy system modelling. Here we present open-source generative machine learning methods that produce meteorological data at a nominal spatial resolution of 4 km at an hourly frequency based on inputs from 100 km daily-average GCM data. These methods run 40 times faster than traditional downscaling methods and produce data that have high-resolution spatial and temporal attributes similar to historical datasets. We demonstrate that these methods can be used to downscale projected changes in wind, solar and temperature variables across multiple GCMs including projections for more frequent low-wind and high-temperature events in the Eastern United States.

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Fig. 1: Illustration of the Sup3rCC data on 22 August 2050.
Fig. 2: Evaluation of high-resolution spatial attributes of the Sup3rCC data.
Fig. 3: Evaluation of high-resolution temporal attributes of the Sup3rCC data.
Fig. 4: Joint distributions comparing measurements to GCM and Sup3rCC data.
Fig. 5: Joint distributions comparing measurements to WTK, NSRDB and Sup3rCC data.
Fig. 6: Climate change impacts to synchronous wind speed and WBT hourly anomalies.
Fig. 7: Climate change impacts to synchronous GHI and WBT hourly anomalies.

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Data availability

All data in this work are open-access and freely available to the public. The NSRDB, WTK and Sup3rCC data and models are publicly available via the Open Energy Data Initiative (OEDI) with Digital Object Identifiers: https://doi.org/10.25984/1810289 (NSRDB), https://doi.org/10.25984/1822195 (WTK) and https://doi.org/10.25984/1970814 (Sup3rCC)71,72,73. The HRRR is publicly available via Amazon Web Services at https://registry.opendata.aws/noaa-hrrr-pds/. The initial Sup3rCC data presented in this work have been assigned v.0.1.0.

Code availability

The Super-Resolution for Renewable Resource Data (sup3r) software is open-source and publicly available at https://github.com/NREL/sup3r and https://doi.org/10.5281/zenodo.7826915 (ref. 68). Examples of how to use the Sup3rCC models and access the data are included with the sup3r software here: https://github.com/NREL/sup3r/tree/main/examples/sup3rcc.

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Acknowledgements

We thank A. Lopez, T. Mai, P. Lamers, M. Ruth, D. Bilello, M. Mooney, D. Arent, G. Porro, E. Laidlaw and Z. Goff-Eldredge for their thoughtful reviews of an earlier draft. We also thank B. Roberts for developing Fig. 1 and M. Heine, R. Olson, J. Gu and N. Taverna for making the Sup3rCC data available via OEDI. This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the US Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by the DOE Grid Deployment Office (GDO) (award no. 38843), the DOE Office of Advanced Scientific Computing Research (ASCR) (field work proposal no. ERW1579), the DOE Solar Energy Technologies Office (SETO) (award no. 38421) and the Laboratory Directed Research and Development (LDRD) programme at the National Renewable Energy Laboratory. The research was performed using computational resources sponsored by the DOE Office of Energy Efficiency and Renewable Energy and located at the National Renewable Energy Laboratory. The views expressed in the article do not necessarily represent the views of the DOE or the US Government. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the US Government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for US Government purposes.

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  1. National Renewable Energy Laboratory, Golden, CO, USA

    Grant Buster, Brandon N. Benton, Andrew Glaws & Ryan N. King

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G.B. developed software, developed methods, trained models, produced data and wrote the paper. B.N.B. developed software, advised on methods and wrote the paper. A.G. and R.N.K. advised on methods and wrote the paper.

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Correspondence to Grant Buster.

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Buster, G., Benton, B.N., Glaws, A. et al. High-resolution meteorology with climate change impacts from global climate model data using generative machine learning. Nat Energy (2024). https://doi.org/10.1038/s41560-024-01507-9

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