paper
2020
Downscaling & super-resolution
Applications & impacts New model or method
GAN or VAEClimate projections / centennial Journal
2020-07-06
050
Jul 2020
Karen Stengel, Andrew Glaws, Dylan Hettinger et al.
Proceedings of the National Academy of Sciences
Adversarial deep learning super-resolves wind and solar outputs from global climate models by up to 50x, producing physically consistent fields for renewable resource assessment.
Downscaling & super-resolution
Adversarial super-resolution of climatological wind and solar data
In one sentence. Adversarial deep learning super-resolves wind and solar outputs from global climate models by up to 50x, producing physically consistent fields for renewable resource assessment.
Labels
- Topic
- Downscaling & super-resolution, Applications & impacts
- Type
- New model or method
- Method
- GAN or VAE
- Time scale
- Climate projections / centennial
- Domain
- Global, Regional
- Data
- CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.1073/pnas.1918964117 OpenAlex:W3039441806
- Added
- 2026-10-22
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