paper
2020
Applications & impacts
Extremes & hazardsPost-processing & calibration New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 daysNSF NCAR / UCAR Journal
2020-08-31
047
Aug 2020
Domingo Muñoz‐Esparza, Robert D. Sharman, Wiebke K. Deierling
Journal of Applied Meteorology and Climatology
NSF NCAR / UCAR
Random forests and gradient-boosted regression trees using HRRR forecast features improve upper-level aviation turbulence (EDR) forecasts and reduce false alarms versus regression baselines.
Aviation Turbulence Forecasting at Upper Levels with Machine Learning Techniques Based on Regression Trees
In one sentence. Random forests and gradient-boosted regression trees using HRRR forecast features improve upper-level aviation turbulence (EDR) forecasts and reduce false alarms versus regression baselines.
Labels
- Topic
- Applications & impacts, 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, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1175/jamc-d-20-0116.1 OpenAlex:W3081677703
- Added
- 2026-10-22
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