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

Grant Buster (), Brandon N. Benton, Andrew Glaws and Ryan N. King
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Grant Buster: National Renewable Energy Laboratory
Brandon N. Benton: National Renewable Energy Laboratory
Andrew Glaws: National Renewable Energy Laboratory
Ryan N. King: National Renewable Energy Laboratory

Nature Energy, 2024, vol. 9, issue 7, 894-906

Abstract: 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.

Date: 2024
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DOI: 10.1038/s41560-024-01507-9

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