paper
2020
Extremes & hazards
Post-processing & calibration Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)6 h–3 daysColorado State University (incl. CIRA) Journal
2020-05-01
032
May 2020
Aaron J. Hill, Gregory R. Herman, Russ S. Schumacher
Monthly Weather Review
Colorado State University (incl. CIRA)
Random forests trained on GEFS reforecasts give calibrated Day 1-3 severe weather outlooks that beat SPC at Days 2-3, and blending with SPC improves Day 1 outlooks.
Forecasting Severe Weather with Random Forests
In one sentence. Random forests trained on GEFS reforecasts give calibrated Day 1-3 severe weather outlooks that beat SPC at Days 2-3, and blending with SPC improves Day 1 outlooks.
Labels
- Topic
- Extremes & hazards, Post-processing & calibration
- Type
- Evaluation or intercomparison of existing models
- 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-19-0344.1 OpenAlex:W3014703899
- Added
- 2026-10-22
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