paper
2023
Extremes & hazards
New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 daysNOAA (incl. GFDL, PSL, GSL, NCEP/EMC) Journal
2023-05-22
051
May 2023
Mu-Chieh Ko, Xiaomin Chen, Miroslav Kubát et al.
Weather and Forecasting
NOAA (incl. GFDL, PSL, GSL, NCEP/EMC)
Consensus of classical ML models on HWRF predictors raises rapid-intensification detection to 56% POD, versus 10–30% for operational models.
The Development of a Consensus Machine Learning Model for Hurricane Rapid Intensification Forecasts with Hurricane Weather Research and Forecasting (HWRF) Data
In one sentence. Consensus of classical ML models on HWRF predictors raises rapid-intensification detection to 56% POD, versus 10–30% for operational models.
Labels
- Topic
- 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, ...)
- Status
- Research
- Identifiers
- DOI:10.1175/waf-d-22-0217.1 OpenAlex:W4377238622
- Added
- 2026-10-22
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