paper
2019
Post-processing & calibration
Extremes & hazards New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 daysNSF NCAR / UCARUniversity of Oklahoma (AI2ES) Journal
2019-11-26
051
Nov 2019
Amanda L. Burke, Nathan Snook, David John Gagne et al.
Weather and Forecasting
NSF NCAR / UCAR, University of Oklahoma (AI2ES)
Random forest postprocesses HREFv2 ensembles for hail, with isotonic calibration matching SPC outlook reliability for operational day-1 forecasts.
Calibration of Machine Learning–Based Probabilistic Hail Predictions for Operational Forecasting
In one sentence. Random forest postprocesses HREFv2 ensembles for hail, with isotonic calibration matching SPC outlook reliability for operational day-1 forecasts.
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
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Radar
- Status
- Pre-operational / experimental
- Identifiers
- DOI:10.1175/waf-d-19-0105.1 OpenAlex:W2989751705
- Added
- 2026-10-22
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