paper
2023
Post-processing & calibration
Sub-seasonal to seasonal New model or method
GAN or VAESeasonal to annualShanghai Artificial Intelligence LaboratoryNanjing University of Information Science & Technology (NUIST) Other
2023-08-24
047
Aug 2023
Song Yang, Fenghua Ling, Lei Bai et al.
preprint
Shanghai Artificial Intelligence Laboratory, Nanjing University of Information Science & Technology (NUIST)
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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