paper
2020
Extremes & hazards
Observations & remote sensing New model or method
Other0–6 h6 h–3 days Preprint
2020-01-01
012
Jan 2020
Juhyun Lee, Cheolhee Yoo, Jungho Im et al.
Scholarworks@UNIST (Ulsan National Institute of Science and Technology)
Multi-task deep learning fuses COMS geostationary images with CFSv2 forecasts, cutting 6h and 12h tropical cyclone intensity forecast RMSE by 13% and 16%.
Multi-task Learning Based Tropical Cyclone Intensity Monitoring and Forecasting through Fusion of Geostationary Satellite Data and Numerical Forecasting Model Output
In one sentence. Multi-task deep learning fuses COMS geostationary images with CFSv2 forecasts, cutting 6h and 12h tropical cyclone intensity forecast RMSE by 13% and 16%.
Labels
- Topic
- Extremes & hazards, Observations & remote sensing
- Type
- New model or method
- Method
- Other
- Time scale
- 0–6 h, 6 h–3 days
- Domain
- Regional
- Data
- Satellite, NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.7780/kjrs.2020.36.5.3.4 OpenAlex:W3130207954
- Added
- 2026-10-22
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