paper
2018
Extremes & hazards
Post-processing & calibration New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 daysColorado State University (incl. CIRA) Journal
2018-05-31
026
May 2018
Gregory R. Herman, R. Schumacher
Monthly Weather Review
Colorado State University (incl. CIRA)
Random forests and logistic regression trained on GEFS reforecast predictors forecast CONUS extreme precipitation at day 2 and day 3, and the paper analyzes what the trained models reveal physically.
“Dendrology” in Numerical Weather Prediction: What Random Forests and Logistic Regression Tell Us about Forecasting Extreme Precipitation
In one sentence. Random forests and logistic regression trained on GEFS reforecast predictors forecast CONUS extreme precipitation at day 2 and day 3, and the paper analyzes what the trained models reveal physically.
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, ...)
- Status
- Research
- Identifiers
- DOI:10.1175/mwr-d-17-0307.1 OpenAlex:W2800258827
- Added
- 2026-10-22
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