paper
2019
Extremes & hazards
Observations & remote sensing New model or method
CNN / U-NetNSF NCAR / UCAR Journal
2019-03-01
061
Mar 2019
Buo‐Fu Chen, Boyo Chen, Boyo Chen et al.
Weather and Forecasting
NSF NCAR / UCAR
A CNN trained on satellite infrared and microwave data estimates tropical cyclone intensity with 8.39 kt RMS error, offering an objective alternative to the Dvorak technique.
Estimating Tropical Cyclone Intensity by Satellite Imagery Utilizing Convolutional Neural Networks
In one sentence. A CNN trained on satellite infrared and microwave data estimates tropical cyclone intensity with 8.39 kt RMS error, offering an objective alternative to the Dvorak technique.
Labels
- Topic
- Extremes & hazards, Observations & remote sensing
- Type
- New model or method
- Method
- CNN / U-Net
- Domain
- Global
- Data
- Satellite, Other
- Status
- Research
- Identifiers
- DOI:10.1175/waf-d-18-0136.1 OpenAlex:W2919354935
- Added
- 2026-10-22
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