paper
2025
Post-processing & calibration
Extremes & hazards New model or method
6 h–3 daysChina Meteorological Administration (CMA)Fudan University (FuXi) Journal
2025-12-13
051
Dec 2025
Shijin Yuan, Xingzhou Wang, Bin Mu et al.
Advances in Atmospheric Sciences
China Meteorological Administration (CMA), Fudan University (FuXi)
Deep learning bias correction of tropical cyclone track and intensity forecasts for the TianXing large weather model, aimed at operational use.
A Deep Learning–Based Bias Correction Model for Tropical Cyclone Track and Intensity towards Forecasting of the TianXing Large Weather Model
In one sentence. Deep learning bias correction of tropical cyclone track and intensity forecasts for the TianXing large weather model, aimed at operational use.
Labels
- Topic
- Post-processing & calibration, Extremes & hazards
- Type
- New model or method
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Pre-operational / experimental
- Identifiers
- DOI:10.1007/s00376-025-4520-8 OpenAlex:W7114992382
- Added
- 2026-10-22
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