paper
2020
Sub-seasonal to seasonal
Climate analysis & attribution New model or method
Classical ML (trees, GP, linear, kernels)Seasonal to annual2 weeks–2 monthsColumbia University (LEAP, Lamont-Doherty) Journal
2020-08-18
026
Aug 2020
Julian Hernandez, Óscar J. Mesa, Upmanu Lall
Weather and Forecasting
Columbia University (LEAP, Lamont-Doherty)
Nonhomogeneous hidden Markov model identifies five ENSO regimes from 1856 to 2019 and gives out-of-sample probabilistic Niño forecasts comparable to existing models.
ENSO Dynamics, Trends, and Prediction Using Machine Learning
In one sentence. Nonhomogeneous hidden Markov model identifies five ENSO regimes from 1856 to 2019 and gives out-of-sample probabilistic Niño forecasts comparable to existing models.
Labels
- Topic
- Sub-seasonal to seasonal, Climate analysis & attribution
- Type
- New model or method
- Method
- Classical ML (trees, GP, linear, kernels)
- Time scale
- Seasonal to annual, 2 weeks–2 months
- Domain
- Regional
- Data
- Other
- Status
- Research
- Identifiers
- DOI:10.1175/waf-d-20-0031.1 OpenAlex:W3070087388
- Added
- 2026-10-22
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