paper
2023
Sub-seasonal to seasonal
Extremes & hazards New model or method
Classical ML (trees, GP, linear, kernels)2 weeks–2 monthsETH ZurichMassachusetts Institute of Technology (MIT) Journal
2023-01-09
022
Jan 2023
Elizabeth Weirich-Benet, Maria Pyrina, Bernat Jiménez‐Esteve et al.
Artificial Intelligence for the Earth Systems
ETH Zurich, Massachusetts Institute of Technology (MIT)
Linear and random forest models forecast central European summer heatwaves 1–6 weeks ahead, matching ECMWF hindcast ensemble mean beyond two weeks lead.
Subseasonal Prediction of Central European Summer Heatwaves with Linear and Random Forest Machine Learning Models
In one sentence. Linear and random forest models forecast central European summer heatwaves 1–6 weeks ahead, matching ECMWF hindcast ensemble mean beyond two weeks lead.
Labels
- Topic
- Sub-seasonal to seasonal, Extremes & hazards
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 2 weeks–2 months
- Domain
- Regional
- Data
- ERA5 / reanalysis, NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.1175/aies-d-22-0038.1 OpenAlex:W4313888525
- Added
- 2026-10-22
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