paper
2022
Climate analysis & attribution
Extremes & hazards Application study
CNN / U-NetClimate projections / centennialNSF NCAR / UCAR Journal
2022-10-20
051
Oct 2022
Katherine Dagon, John Truesdale, James C Biard et al.
Journal of Geophysical Research Atmospheres
NSF NCAR / UCAR
Deep learning detects weather fronts in high-resolution CESM simulations over CONUS, showing frontal extreme precipitation becomes less likely under climate change despite overall increases.
Climate analysis & attribution
Machine Learning‐Based Detection of Weather Fronts and Associated Extreme Precipitation in Historical and Future Climates
In one sentence. Deep learning detects weather fronts in high-resolution CESM simulations over CONUS, showing frontal extreme precipitation becomes less likely under climate change despite overall increases.
Labels
- Topic
- Climate analysis & attribution, Extremes & hazards
- Type
- Application study
- Method
- CNN / U-Net
- Time scale
- Climate projections / centennial
- Domain
- Regional
- Data
- CMIP / climate model output, ERA5 / reanalysis, km-scale or high-res simulation
- Status
- Research
- Identifiers
- DOI:10.1029/2022jd037038 OpenAlex:W4306919374
- Added
- 2026-10-22
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