The Application of Machine Learning Techniques to Improve El Niño Prediction Skill

We review prediction efforts of El Niño events in the tropical Pacific with particular focus on using modern machine learning (ML) methods based on artificial neural networks. With current classical prediction methods using both statistical and dynamical models, the skill decreases substantially for...

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Bibliographic Details
Published inFrontiers in physics Vol. 7
Main Authors Dijkstra, Henk A., Petersik, Paul, Hernández-García, Emilio, López, Cristóbal
Format Journal Article
LanguageEnglish
Published Frontiers Media S.A 10.10.2019
Subjects
attributes
climate networks
El Niño
machine learning
neural networks
prediction
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Summary:We review prediction efforts of El Niño events in the tropical Pacific with particular focus on using modern machine learning (ML) methods based on artificial neural networks. With current classical prediction methods using both statistical and dynamical models, the skill decreases substantially for lead times larger than about 6 months. Initial ML results have shown enhanced skill for lead times larger than 12 months. The search for optimal attributes in these methods is described, in particular those derived from complex network approaches, and a critical outlook on further developments is given.
ISSN:2296-424X
2296-424X
DOI:10.3389/fphy.2019.00153