paper
2018
Post-processing & calibration
Extremes & hazards Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)6 h–3 daysKNMI (Royal Netherlands Meteorological Institute) Journal
2018-07-06
051
Jul 2018
Kirien Whan, Maurice J. Schmeits
Monthly Weather Review
KNMI (Royal Netherlands Meteorological Institute)
Compares ELR, ZAGA and quantile regression forests for postprocessing HARMONIE-AROME hourly precipitation; QRF is most skillful for high thresholds.
Comparing Area Probability Forecasts of (Extreme) Local Precipitation Using Parametric and Machine Learning Statistical Postprocessing Methods
In one sentence. Compares ELR, ZAGA and quantile regression forests for postprocessing HARMONIE-AROME hourly precipitation; QRF is most skillful for high thresholds.
Labels
- Topic
- Post-processing & calibration, Extremes & hazards
- Type
- Evaluation or intercomparison of existing models
- 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-17-0290.1 OpenAlex:W2830616863
- Added
- 2026-10-22
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