paper
2026
Sub-seasonal to seasonal
Benchmarks, datasets & evaluation Review, survey or tutorial
2 weeks–2 monthsECMWFFudan University (FuXi)NSF NCAR / UCARNOAA (incl. GFDL, PSL, GSL, NCEP/EMC)University of Washington Journal
★ Highlight 2026-10-15
059
Oct 2026
Meriem Krouma, Yakelyn R. Jauregui, Boualem Khouider et al.
Bulletin of the American Meteorological Society
ECMWF, Fudan University (FuXi), NSF NCAR / UCAR, NOAA (incl. GFDL, PSL, GSL, NCEP/EMC), University of Washington
Reviews MJO predictability limits and sources, arguing dynamical, statistical and machine-learning forecasts should be combined into a unified subseasonal prediction framework.
1
Sub-seasonal to seasonal★ Highlight
MJO Predictability: Its Sources and Limits
Why it matters. Authoritative review of MJO predictability sources and limits, advocating unified dynamical, statistical and ML subseasonal prediction; useful reference for S2S researchers.
In one sentence. Reviews MJO predictability limits and sources, arguing dynamical, statistical and machine-learning forecasts should be combined into a unified subseasonal prediction framework.
Labels
- Topic
- Sub-seasonal to seasonal, Benchmarks, datasets & evaluation
- Type
- Review, survey or tutorial
- Time scale
- 2 weeks–2 months
- Domain
- Global
- Status
- Research
- Identifiers
- DOI:10.1175/bams-d-25-0345.1 OpenAlex:W7222125007
- Added
- 2026-10-22
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