paper
2020
Observations & remote sensing
Extremes & hazards New model or method
Classical ML (trees, GP, linear, kernels)CNN / U-Net Other
2020-04-08
016
Apr 2020
Ana Paula Barros, Malarvizhi Arulraj
preprint
Random forest and CNN framework corrects GPM DPR orographic precipitation retrievals using HRRR, cutting false alarms by 77% over the Southern Appalachians.
Automatic Detection and Classification of Orographic Precipitation using Machine Learning
In one sentence. Random forest and CNN framework corrects GPM DPR orographic precipitation retrievals using HRRR, cutting false alarms by 77% over the Southern Appalachians.
Labels
- Topic
- Observations & remote sensing, Extremes & hazards
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels), CNN / U-Net
- Domain
- Regional
- Data
- Satellite, Radar, NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.1002/essoar.10502701.1 OpenAlex:W3024177547
- Added
- 2026-10-22
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