paper
2017
Extremes & hazards
Applications & impacts Review, survey or tutorial
Classical ML (trees, GP, linear, kernels)0–6 h6 h–3 daysNSF NCAR / UCARNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES) Journal
2017-10-01
061
Oct 2017
Amy McGovern, Kimberly L. Elmore, David John Gagne et al.
Bulletin of the American Meteorological Society
NSF NCAR / UCAR, NOAA (incl. GFDL, PSL, GSL, NCEP/EMC), University of Oklahoma (AI2ES)
Shows AI methods, combined with physical understanding, improve forecasts of storm duration, severe wind and hail, precipitation type, renewable energy and aviation turbulence.
Extremes & hazardsClassic
Using Artificial Intelligence to Improve Real-Time Decision-Making for High-Impact Weather
In one sentence. Shows AI methods, combined with physical understanding, improve forecasts of storm duration, severe wind and hail, precipitation type, renewable energy and aviation turbulence.
Labels
- Topic
- Extremes & hazards, Applications & impacts
- Type
- Review, survey or tutorial
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- 0–6 h, 6 h–3 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Radar, Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1175/bams-d-16-0123.1 OpenAlex:W2601923741
- Added
- 2026-10-22
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