paper
2020
Downscaling & super-resolution
Post-processing & calibration New model or method
Classical ML (trees, GP, linear, kernels)Climate projections / centennial Journal
2020-04-30
012
Apr 2020
Aneesh Goly, Ramesh S. V. Teegavarapu
Journal of Hydrologic Engineering
Neural-network-augmented quantile mapping bias corrects GCM-downscaled precipitation at 18 Florida gauges, reproducing extreme precipitation indices better than existing quantile methods.
Downscaling & super-resolution
Optimization and Variants of Quantile-Based Methods for Bias Corrections of Statistically Downscaled Precipitation Data
In one sentence. Neural-network-augmented quantile mapping bias corrects GCM-downscaled precipitation at 18 Florida gauges, reproducing extreme precipitation indices better than existing quantile methods.
Labels
- Topic
- Downscaling & super-resolution, Post-processing & calibration
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Regional
- Data
- CMIP / climate model output, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1061/(asce)he.1943-5584.0001926 OpenAlex:W3023738816
- Added
- 2026-10-22
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