paper
2021
Sub-seasonal to seasonal
Climate analysis & attribution New model or method
Classical ML (trees, GP, linear, kernels)2 weeks–2 months Journal
2021-07-20
012
Jul 2021
Elizabeth K. Carter, Dimitris A. Herrera, Scott Steinschneider
Journal of Climate
Interpretable machine learning ranks 24 predictive indices for March–September Midwest US S2S precipitation and links them to three moisture and circulation mechanisms.
Feature engineering for subseasonal-to-seasonal warm-season precipitation forecasts in the Midwestern US: towards a unifying hypothesis of anomalous warm-season hydroclimatic circulation
In one sentence. Interpretable machine learning ranks 24 predictive indices for March–September Midwest US S2S precipitation and links them to three moisture and circulation mechanisms.
Labels
- Topic
- Sub-seasonal to seasonal, Climate analysis & attribution
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 2 weeks–2 months
- Domain
- Regional
- Data
- ERA5 / reanalysis
- Status
- Research
- Identifiers
- DOI:10.1175/jcli-d-20-0264.1 OpenAlex:W3185464719
- Added
- 2026-10-22
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