paper
2017
Extremes & hazards
Post-processing & calibration New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 daysNSF NCAR / UCARUniversity of Oklahoma (AI2ES) Journal
2017-09-22
065
Sep 2017
David John Gagne, Amy McGovern, Sue Ellen Haupt et al.
Weather and Forecasting
NSF NCAR / UCAR, University of Oklahoma (AI2ES)
Storm-based machine learning produces calibrated probabilistic severe hail forecasts from convection-allowing ensembles, achieving higher critical success index than existing hail forecasting methods.
Extremes & hazardsClassic
Storm-Based Probabilistic Hail Forecasting with Machine Learning Applied to Convection-Allowing Ensembles
In one sentence. Storm-based machine learning produces calibrated probabilistic severe hail forecasts from convection-allowing ensembles, achieving higher critical success index than existing hail forecasting methods.
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
- DOI:10.1175/waf-d-17-0010.1 OpenAlex:W2743153755
- Added
- 2026-10-22
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