paper
2023
Post-processing & calibration
Sub-seasonal to seasonal New model or method
GAN or VAESeasonal to annualNanjing University of Information Science & Technology (NUIST)Shanghai Artificial Intelligence Laboratory Journal
2023-08-24
047
Aug 2023
Song Yang, Fenghua Ling, Lei Bai et al.
Advances in Atmospheric Sciences
Nanjing University of Information Science & Technology (NUIST), Shanghai Artificial Intelligence Laboratory
CycleGAN bias correction of NUIST-CFS summer precipitation forecasts in southeastern China outperforms quantile mapping and better captures extreme precipitation frequency and intensity.
Improving Seasonal Forecast of Summer Precipitation in Southeastern China using CycleGAN Deep Learning Bias Correction
In one sentence. CycleGAN bias correction of NUIST-CFS summer precipitation forecasts in southeastern China outperforms quantile mapping and better captures extreme precipitation frequency and intensity.
Labels
- Topic
- Post-processing & calibration, Sub-seasonal to seasonal
- Type
- New model or method
- Method
- GAN or VAE
- Time scale
- Seasonal to annual
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.22541/essoar.169290554.48887852/v1 OpenAlex:W4386147724
- Added
- 2026-10-22
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