paper
2020
Downscaling & super-resolution
Post-processing & calibration New model or method
Classical ML (trees, GP, linear, kernels)University of Reading Journal
2020-05-13
022
May 2020
Thomas C. M. Martin, Humberto Ribeiro da Rocha, Gabriel M. P. Perez
International Journal of Climatology
University of Reading
Machine learning with PCA, ANN and quantile mapping downscales GCM output to 30 m sub-daily temperature, humidity and wind in complex terrain, improving on GCM accuracy.
Downscaling & super-resolution
Fine scale surface climate in complex terrain using machine learning
In one sentence. Machine learning with PCA, ANN and quantile mapping downscales GCM output to 30 m sub-daily temperature, humidity and wind in complex terrain, improving on GCM accuracy.
Labels
- Topic
- Downscaling & super-resolution, Post-processing & calibration
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Domain
- Local / station
- Data
- Station / in-situ, CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.1002/joc.6617 OpenAlex:W3024895859
- Added
- 2026-10-22
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