paper
2019
Extremes & hazards
Post-processing & calibration New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 days Preprint
2019-08-01
012
Aug 2019
Amanda L. Burke
SHAREOK (University of Oklahoma; Oklahoma State University; Central Oklahoma University)
Random forests post-processing HREFv2 ensembles with isotonic calibration give severe hail probabilities comparable to SPC outlooks over CONUS.
Using Machine Learning Applications and HREFv2 to Enhance Hail Prediction for Operations
In one sentence. Random forests post-processing HREFv2 ensembles with isotonic calibration give severe hail probabilities comparable to SPC outlooks over CONUS.
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
- Status
- Research
- Identifiers
- OpenAlex:W2963960945
- Added
- 2026-10-22
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