Seasonal forecasting

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Seasonal variations, such as those due to El Niño/Southern Oscillation (ENSO) are difficult to predict using traditional methods. ML and deep learning can be useful for multi-year ENSO forecasting[1][2][3][4][5].

Background Readings

Conferences, Journals, and Professional Organizations

Libraries and Tools


Future Directions

Relevant Groups and Organizations


  1. Ham, Yoo-Geun; Kim, Jeong-Hwan; Luo, Jing-Jia (2019-09). "Deep learning for multi-year ENSO forecasts". Nature. 573 (7775): 568–572. doi:10.1038/s41586-019-1559-7. ISSN 1476-4687. Check date values in: |date= (help)
  2. Toms, Benjamin A.; Barnes, Elizabeth A.; Ebert‐Uphoff, Imme (2020). "Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability". Journal of Advances in Modeling Earth Systems. 12 (9): e2019MS002002. doi:10.1029/2019MS002002. ISSN 1942-2466.
  3. Mahesh,, A., et al., (2019). "Forecasting El Niño with Convolutional andRecurrent Neural Networks" (PDF).CS1 maint: extra punctuation (link)
  4. Cachay,, S. R.; et al. (2020). "Graph Neural Networks for Improved El NiñoForecasting" (PDF). Explicit use of et al. in: |first= (help)CS1 maint: extra punctuation (link)
  5. Guo, Yanan; Cao, Xiaoqun; Liu, Bainian; Peng, Kecheng (2020/6). "El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition". Symmetry. 12 (6): 893. doi:10.3390/sym12060893. Check date values in: |date= (help)