paper
2024
Sub-seasonal to seasonal
Explainability, uncertainty & physics New model or method
OtherSeasonal to annualFudan University (FuXi) Journal
2024-10-01
051
Oct 2024
Bo Qin, Zeyun Yang, Mu Mu et al.
Quarterly Journal of the Royal Meteorological Society
Fudan University (FuXi)
A deep-learning ENSO model uses conditional nonlinear optimal perturbations to identify fastest-growing initial errors for two El Niño types, validated in GFDL CM2p1.
The first kind of predictability problem of El Niño predictions in a multivariate coupled data‐driven model
In one sentence. A deep-learning ENSO model uses conditional nonlinear optimal perturbations to identify fastest-growing initial errors for two El Niño types, validated in GFDL CM2p1.
Labels
- Topic
- Sub-seasonal to seasonal, Explainability, uncertainty & physics
- Type
- New model or method
- Method
- Other
- Time scale
- Seasonal to annual
- Domain
- Global
- Status
- Research
- Identifiers
- DOI:10.1002/qj.4882 OpenAlex:W4403790134
- Added
- 2026-10-22
Labels and summary are generated by Claude and reviewed weekly. Wrong? Edit this entry.