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• W3039441806 endingPage "16815" @default.
• W3039441806 startingPage "16805" @default.
• W3039441806 abstract "Significance Global climate simulations are typically unable to resolve wind and solar data at a resolution sufficient for renewable energy resource assessment in different climate scenarios. We introduce an adversarial deep learning approach to super resolve wind and solar outputs from global climate models by up to 50×. The inferred high-resolution fields are robust, physically consistent with the properties of atmospheric turbulence and solar irradiation, and can be adapted to domains from regional to global scales. This resolution enhancement enables critical localized assessments of the potential long-term economic viability of renewable energy resources." @default.
• W3039441806 created "2020-07-10" @default.
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• W3039441806 date "2020-07-06" @default.
• W3039441806 modified "2023-10-15" @default.
• W3039441806 title "Adversarial super-resolution of climatological wind and solar data" @default.
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• W3039441806 doi "https://doi.org/10.1073/pnas.1918964117" @default.
• W3039441806 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7382270" @default.
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• W3039441806 hasPublicationYear "2020" @default.
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