paper
2021
Post-processing & calibration
Extremes & hazards New model or method
CNN / U-NetTransformer / ViT6 h–3 daysFudan University (FuXi) Journal
2021-06-16
047
Jun 2021
Yiqun Liu, Junping Zhang, Lei Chen et al.
IEEE Transactions on Cybernetics
Fudan University (FuXi)
An end-to-end spatiotemporal scale-selection model improves ECMWF precipitation bias correction, especially for heavy rain, beating 11 published methods on two benchmarks.
SSAS: Spatiotemporal Scale Adaptive Selection for Improving Bias Correction on Precipitation
In one sentence. An end-to-end spatiotemporal scale-selection model improves ECMWF precipitation bias correction, especially for heavy rain, beating 11 published methods on two benchmarks.
Labels
- Topic
- Post-processing & calibration, Extremes & hazards
- Type
- New model or method
- Method
- CNN / U-Net, Transformer / ViT
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...), Station / in-situ
- Status
- Research
- Identifiers
- DOI:10.1109/tcyb.2021.3072483 OpenAlex:W3172563922
- Added
- 2026-10-22
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