paper
2019
Post-processing & calibration
New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 daysNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES) Journal
2019-10-17
051
Oct 2019
Eric D. Loken, Adam James Clark, Amy McGovern et al.
Weather and Forecasting
NOAA (incl. GFDL, PSL, GSL, NCEP/EMC), University of Oklahoma (AI2ES)
Random forest calibrates 12–36 h SREF and HREFv2 precipitation ensembles over CONUS, improving reliability and Brier skill over raw and smoothed probabilities.
Postprocessing Next-Day Ensemble Probabilistic Precipitation Forecasts Using Random Forests
In one sentence. Random forest calibrates 12–36 h SREF and HREFv2 precipitation ensembles over CONUS, improving reliability and Brier skill over raw and smoothed probabilities.
Labels
- Topic
- 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:10.1175/waf-d-19-0109.1 OpenAlex:W2981173151
- Added
- 2026-10-22
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