paper
2019
Climate analysis & attribution
Observations & remote sensing New model or method
CNN / U-NetNSF NCAR / UCARUniversity of Oklahoma (AI2ES) Journal
2019-06-13
065
Jun 2019
Ryan Lagerquist, Amy McGovern, David John Gagne
Weather and Forecasting
NSF NCAR / UCAR, University of Oklahoma (AI2ES)
CNN with a novel probability-to-object step detects synoptic warm and cold fronts in reanalysis grids, achieving 0.73 probability of detection at 250 km.
Climate analysis & attribution
Deep Learning for Spatially Explicit Prediction of Synoptic-Scale Fronts
In one sentence. CNN with a novel probability-to-object step detects synoptic warm and cold fronts in reanalysis grids, achieving 0.73 probability of detection at 250 km.
Labels
- Topic
- Climate analysis & attribution, Observations & remote sensing
- Type
- New model or method
- Method
- CNN / U-Net
- Domain
- Regional
- Data
- ERA5 / reanalysis
- Status
- Pre-operational / experimental
- Identifiers
- DOI:10.1175/waf-d-18-0183.1 OpenAlex:W2950210192
- Added
- 2026-10-22
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