paper
2018
Post-processing & calibration
Extremes & hazards New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 days3–15 daysColorado State University (incl. CIRA) Journal
2018-04-18
036
Apr 2018
Gregory R. Herman, Russ S. Schumacher
Monthly Weather Review
Colorado State University (incl. CIRA)
Random forests trained on 11 years of GEFS reforecasts give better CONUS extreme precipitation exceedance forecasts than raw ensembles and ECMWF.
Money Doesn't Grow on Trees, but Forecasts Do: Forecasting Extreme Precipitation with Random Forests
In one sentence. Random forests trained on 11 years of GEFS reforecasts give better CONUS extreme precipitation exceedance forecasts than raw ensembles and ECMWF.
Labels
- Topic
- Post-processing & calibration, Extremes & hazards
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days, 3–15 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.1175/mwr-d-17-0250.1 OpenAlex:W2801493395
- Added
- 2026-10-22
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