The DOI matches its Crossref record; 65 of the 65 references it cites were found in a registry or an index.
Adversarial super-resolution of climatological wind and solar data
Open accessAbstract
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.
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Deep Residual Learning for Image Recognition
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GAN（Generative Adversarial Nets）
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Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
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Towards neural Earth system modelling by integrating artificial intelligence in Earth system science
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Machine learning applications for weather and climate need greater focus on extremes
Watson, P. A. G. (2022) · Environmental Research Letters · doi:10.1088/1748-9326/ac9d4e · Cited by 32
Metadata
- Authors
- Stengel, K., Glaws, A., Hettinger, D., & King, R. N.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2020-07-06
- Volume
- 117
- Issue
- 29
- Pages
- 16805-16815
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W3039441806
- Cited by
- 287
- References
- 65
- FWCI
- 14.21
- Type
- journal article
- Language
- en
- Open access
- green
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