paper
2023
Sub-seasonal to seasonal
New model or method
Classical ML (trees, GP, linear, kernels)2 weeks–2 monthsKarlsruhe Institute of Technology (KIT) Journal
2023-07-28
022
Jul 2023
Selina M. Kiefer, Sebastian Lerch, Patrick Ludwig et al.
Artificial Intelligence for the Earth Systems
Karlsruhe Institute of Technology (KIT)
Quantile regression forests and random forest classifiers give skillful 14–28 day probabilistic forecasts of central European winter 2-m temperatures and cold waves, beating climatology.
Can Machine Learning Models Be a Suitable Tool for Predicting Central European Cold Winter Weather on Subseasonal to Seasonal Time Scales?
In one sentence. Quantile regression forests and random forest classifiers give skillful 14–28 day probabilistic forecasts of central European winter 2-m temperatures and cold waves, beating climatology.
Labels
- Topic
- Sub-seasonal to seasonal
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 2 weeks–2 months
- Domain
- Regional
- Data
- ERA5 / reanalysis, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1175/aies-d-23-0020.1 OpenAlex:W4385350660
- Added
- 2026-10-22
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