paper
2022
Downscaling & super-resolution
Benchmarks, datasets & evaluation Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)Climate projections / centennial Journal
2022-03-18
012
Mar 2022
Alfonso Hernanz, Juan Andrés García Valero, Marta Domínguez et al.
International Journal of Climatology
Compares five statistical downscaling methods, including ANN, SVM and kernel ridge, over 18 CMIP5 GCMs for Spain, quantifying errors from imperfect GCM predictors.
Downscaling & super-resolution
Evaluation of statistical downscaling methods for climate change projections over Spain: Present conditions with imperfect predictors (global climate model experiment)
In one sentence. Compares five statistical downscaling methods, including ANN, SVM and kernel ridge, over 18 CMIP5 GCMs for Spain, quantifying errors from imperfect GCM predictors.
Labels
- Topic
- Downscaling & super-resolution, Benchmarks, datasets & evaluation
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Regional
- Data
- CMIP / climate model output, ERA5 / reanalysis, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1002/joc.7611 OpenAlex:W4221012211
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.