paper
2019
Post-processing & calibration
Extremes & hazards New model or method
CNN / U-Net6 h–3 days3–15 days Journal
2019-09-06
016
Sep 2019
W. E. Chapman, A. C. Subramanian, L. Delle Monache et al.
Geophysical Research Letters
A CNN postprocesses GFS integrated vapor transport forecasts, cutting RMSE by 9-17% from 3 hours to 7 days and improving atmospheric river forecasts over the western US.
Improving Atmospheric River Forecasts With Machine Learning
In one sentence. A CNN postprocesses GFS integrated vapor transport forecasts, cutting RMSE by 9-17% from 3 hours to 7 days and improving atmospheric river forecasts over the western US.
Labels
- Topic
- Post-processing & calibration, Extremes & hazards
- Type
- New model or method
- Method
- CNN / U-Net
- Time scale
- 6 h–3 days, 3–15 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.1029/2019gl083662 OpenAlex:W2970999468
- Added
- 2026-10-22
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