paper
2020
Extremes & hazards
Downscaling & super-resolution Benchmark or dataset
Classical ML (trees, GP, linear, kernels)Climate projections / centennial Journal
2020-03-27
012
Mar 2020
Abhishek Gaur, André Schardong, Slobodan P. Simonović
Journal of Hydrologic Engineering
Produces a gridded Canadian extreme precipitation IDF dataset by linking reanalysis predictors to extremes with a support vector machine, with 5–25% RMSE against observations.
Gridded Extreme Precipitation Intensity–Duration–Frequency Estimates for the Canadian Landmass
In one sentence. Produces a gridded Canadian extreme precipitation IDF dataset by linking reanalysis predictors to extremes with a support vector machine, with 5–25% RMSE against observations.
Labels
- Topic
- Extremes & hazards, Downscaling & super-resolution
- Type
- Benchmark or dataset
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Regional
- Data
- ERA5 / reanalysis, Other
- Status
- Research
- Identifiers
- DOI:10.1061/(asce)he.1943-5584.0001924 OpenAlex:W3014039916
- Added
- 2026-10-22
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