paper
2016
Post-processing & calibration
New model or method
Classical ML (trees, GP, linear, kernels)6 h–3 daysMétéo-France (incl. CNRM) Journal
2016-06-10
061
Jun 2016
Maxime Taillardat, Olivier Mestre, Michaël Zamo et al.
Monthly Weather Review
Météo-France (incl. CNRM)
Quantile regression forests calibrate Météo-France ensemble temperature and wind forecasts, outperforming EMOS and giving better-calibrated, sharper probabilistic forecasts from 3 to 54 h.
Post-processing & calibrationClassic
Calibrated Ensemble Forecasts Using Quantile Regression Forests and Ensemble Model Output Statistics
In one sentence. Quantile regression forests calibrate Météo-France ensemble temperature and wind forecasts, outperforming EMOS and giving better-calibrated, sharper probabilistic forecasts from 3 to 54 h.
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, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1175/mwr-d-15-0260.1 OpenAlex:W2296277783
- Added
- 2026-10-22
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