paper
2023
Extremes & hazards
Review, survey or tutorial
Classical ML (trees, GP, linear, kernels)CNN / U-Net6 h–3 daysColorado State University (incl. CIRA)NSF NCAR / UCARNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Oklahoma (AI2ES) Journal
2023-05-26
055
May 2023
A. McGovern, R. Chase, Montgomery L. Flora et al.
Artificial Intelligence for the Earth Systems
Colorado State University (incl. CIRA), NSF NCAR / UCAR, NOAA (incl. GFDL, PSL, GSL, NCEP/EMC), University of Oklahoma (AI2ES)
Reviews classical and deep learning methods for forecasting convective hazards such as tornadoes, hail, wind and lightning, and calls for trustworthy, operationally tested ML forecasts.
A Review of Machine Learning for Convective Weather
In one sentence. Reviews classical and deep learning methods for forecasting convective hazards such as tornadoes, hail, wind and lightning, and calls for trustworthy, operationally tested ML forecasts.
Labels
- Topic
- Extremes & hazards
- Type
- Review, survey or tutorial
- Method
- Classical ML (trees, GP, linear, kernels), CNN / U-Net
- Time scale
- 6 h–3 days
- Domain
- Global
- Status
- Research
- Identifiers
- DOI:10.1175/aies-d-22-0077.1 OpenAlex:W4378377537
- Added
- 2026-10-22
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