paper
2023
Post-processing & calibration
New model or method
Classical ML (trees, GP, linear, kernels)3–15 days Journal
2023-07-01
012
Jul 2023
David Jobst, Annette Möller, Jürgen Groß
Quarterly Journal of the Royal Meteorological Society
D-vine copula quantile regression postprocesses 10 m wind ensembles at 60 German stations, outperforming gradient-boosted tEMOS globally.
D‐vine‐copula‐based postprocessing of wind speed ensemble forecasts
In one sentence. D-vine copula quantile regression postprocesses 10 m wind ensembles at 60 German stations, outperforming gradient-boosted tEMOS globally.
Labels
- Topic
- Post-processing & calibration
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 3–15 days
- Domain
- Local / station
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1002/qj.4521 OpenAlex:W4383621501
- Added
- 2026-10-22
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