paper
2026
Extremes & hazards
Probabilistic & ensemble forecasting Evaluation or intercomparison of existing models
Classical ML (trees, GP, linear, kernels)3–15 daysNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)Princeton UniversityUniversity of Oklahoma (AI2ES) Journal
2026-03-16
051
Mar 2026
Michael J. Hosek, Kimberly A. Hoogewind, Adam James Clark et al.
Weather and Forecasting
NOAA (incl. GFDL, PSL, GSL, NCEP/EMC), Princeton University, University of Oklahoma (AI2ES)
Random forest severe-convection forecasts from C-SHiELD convection-allowing ensembles show skill only through day 7, with a modest gain over parameterized-convection ensembles.
Quantifying the Value of Convection-Allowing Global-Nested Ensembles for Extended-Range Severe Weather Forecasting
In one sentence. Random forest severe-convection forecasts from C-SHiELD convection-allowing ensembles show skill only through day 7, with a modest gain over parameterized-convection ensembles.
Labels
- Topic
- Extremes & hazards, Probabilistic & ensemble forecasting
- Type
- Evaluation or intercomparison of existing models
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 3–15 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...), km-scale or high-res simulation
- Status
- Research
- Identifiers
- DOI:10.1175/waf-d-25-0093.1 OpenAlex:W7136693582
- Added
- 2026-10-22
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