paper
2022
Climate analysis & attribution
Explainability, uncertainty & physicsSub-seasonal to seasonal Application study
CNN / U-NetSeasonal to annual Journal
2022-08-04
016
Aug 2022
Na-Yeon Shin, Yoo‐Geun Ham, Jeong-Hwan Kim et al.
Artificial Intelligence for the Earth Systems
CNN predicts ENSO nine months ahead with about 0.82 correlation; contribution maps identify three precursors with asymmetric roles in El Nino and La Nina.
Climate analysis & attribution
Application of Deep Learning to Understanding ENSO Dynamics
In one sentence. CNN predicts ENSO nine months ahead with about 0.82 correlation; contribution maps identify three precursors with asymmetric roles in El Nino and La Nina.
Labels
- Topic
- Climate analysis & attribution, Explainability, uncertainty & physics, Sub-seasonal to seasonal
- Type
- Application study
- Method
- CNN / U-Net
- Time scale
- Seasonal to annual
- Domain
- Global
- Data
- CMIP / climate model output
- Status
- Research
- Identifiers
- DOI:10.1175/aies-d-21-0011.1 OpenAlex:W4289835106
- Added
- 2026-10-22
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