paper
2021
Sub-seasonal to seasonal
Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)Seasonal to annualEnvironment and Climate Change Canada (ECCC) Journal
2021-06-08
022
Jun 2021
Qifeng Qian, Xiaojing Jia, Hai Lin et al.
Journal of Climate
Environment and Climate Change Canada (ECCC)
Gradient-boosting and related ML models forecast Eurasian winter precipitation seasonally, outperforming linear regression and matching or beating dynamical models; Arctic autumn sea ice is a key predictor.
Seasonal Forecast of Non-monsoonal Winter Precipitation over the Eurasian Continent using Machine Learning Models
In one sentence. Gradient-boosting and related ML models forecast Eurasian winter precipitation seasonally, outperforming linear regression and matching or beating dynamical models; Arctic autumn sea ice is a key predictor.
Labels
- Topic
- Sub-seasonal to seasonal
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Seasonal to annual
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...), ERA5 / reanalysis
- Status
- Research
- Identifiers
- DOI:10.1175/jcli-d-21-0113.1 OpenAlex:W3168227557
- Added
- 2026-10-22
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