paper
2018
Extremes & hazards
Post-processing & calibration New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 days Preprint
2018-01-01
012
Jan 2018
Gregory R. Herman, Schumacher, Russ S., Barnes, Elizabeth A. et al.
Digital Collections of Colorado (Colorado State University)
Machine-learning post-processing gives calibrated probabilistic forecasts of flash floods, extreme rain, tornadoes, hail and convective winds across the US.
A new post-processing paradigm? Improving high-impact weather forecasts with machine learning
In one sentence. Machine-learning post-processing gives calibrated probabilistic forecasts of flash floods, extreme rain, tornadoes, hail and convective winds across the US.
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:10.25675/3.020697 OpenAlex:W2976493830
- Added
- 2026-10-22
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