paper
2024
Explainability, uncertainty & physics
Extremes & hazards Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)6 h–3 days3–15 daysColorado State University (incl. CIRA) Journal
2024-12-05
036
Dec 2024
Alexandra C. Mazurek, Aaron J. Hill, Russ S. Schumacher et al.
Weather and Forecasting
Colorado State University (incl. CIRA)
Tree-interpreter analysis shows the operational CSU-MLP random forest severe-weather forecasts use ingredients consistent with severe-storm climatology.
Explainability, uncertainty & physics
Can Ingredients-Based Forecasting Be Learned? Disentangling a Random Forest’s Severe Weather Predictions
In one sentence. Tree-interpreter analysis shows the operational CSU-MLP random forest severe-weather forecasts use ingredients consistent with severe-storm climatology.
Labels
- Topic
- Explainability, uncertainty & physics, Extremes & hazards
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 6 h–3 days, 3–15 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Operational
- Identifiers
- DOI:10.1175/waf-d-23-0193.1 OpenAlex:W4405076745
- Added
- 2026-10-22
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