paper
2021
Extremes & hazards
Post-processing & calibration New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 daysColorado State University (incl. CIRA) Journal
2021-07-21
026
Jul 2021
Aaron J. Hill, Russ S. Schumacher
Weather and Forecasting
Colorado State University (incl. CIRA)
Random forests trained on convection-allowing WRF output probabilistically forecast excessive rainfall over CONUS, but they consistently underperform WPC's day-1 Excessive Rainfall Outlooks.
Forecasting excessive rainfall with random forests and a deterministic convection-allowing model
In one sentence. Random forests trained on convection-allowing WRF output probabilistically forecast excessive rainfall over CONUS, but they consistently underperform WPC's day-1 Excessive Rainfall Outlooks.
Labels
- Topic
- Extremes & hazards, Post-processing & calibration
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Radar, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1175/waf-d-21-0026.1 OpenAlex:W3187052348
- Added
- 2026-10-22
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