paper
2019
Downscaling & super-resolution
Extremes & hazardsClimate analysis & attribution New model or method
CNN / U-NetClassical ML (trees, GP, linear, kernels)Climate projections / centennial Journal
2019-03-25
012
Mar 2019
James O'Neil Knighton, Geoff Pleiss, Elizabeth K. Carter et al.
Journal of Hydrometeorology
Archetypal analysis plus a 2D CNN predicts eastern US regional precipitation and discharge extremes from synoptic-scale fields, with the most reliable skill from 300 hPa geopotential height.
Downscaling & super-resolution
Potential Predictability of Regional Precipitation and Discharge Extremes Using Synoptic-Scale Climate Information via Machine Learning: An Evaluation for the Eastern Continental United States
In one sentence. Archetypal analysis plus a 2D CNN predicts eastern US regional precipitation and discharge extremes from synoptic-scale fields, with the most reliable skill from 300 hPa geopotential height.
Labels
- Topic
- Downscaling & super-resolution, Extremes & hazards, Climate analysis & attribution
- Type
- New model or method
- Method
- CNN / U-Net, Classical ML (trees, GP, linear, kernels)
- Time scale
- Climate projections / centennial
- Domain
- Regional
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1175/jhm-d-18-0196.1 OpenAlex:W2924604776
- Added
- 2026-10-22
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