paper
2020
Post-processing & calibration
Extremes & hazards New model or method
GAN or VAEOther6 h–3 daysChina Meteorological Administration (CMA) Journal
2020-09-25
018
Sep 2020
Huosheng Xie, Lidong Wu, Wei Xie et al.
Atmospheric Research
China Meteorological Administration (CMA)
Applies GANs and deep belief networks to improve ECMWF short-term intense rainfall forecasts; the abstract is missing, so reported gains are unverified.
Improving ECMWF short-term intensive rainfall forecasts using generative adversarial nets and deep belief networks
In one sentence. Applies GANs and deep belief networks to improve ECMWF short-term intense rainfall forecasts; the abstract is missing, so reported gains are unverified.
Labels
- Topic
- Post-processing & calibration, Extremes & hazards
- Type
- New model or method
- Method
- GAN or VAE, Other
- Time scale
- 6 h–3 days
- Domain
- Regional
- Data
- NWP analysis or forecasts (IFS, GFS, ...)
- Status
- Research
- Identifiers
- DOI:10.1016/j.atmosres.2020.105281 OpenAlex:W3088110263
- Added
- 2026-10-22
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