Using Network Theory and Machine Learning to predict El Niño

The skill of current predictions of the warm phase of the El Niño Southern Oscillation (ENSO) reduces significantly beyond a lag of six months. In this paper, we aim to increase this prediction skill at lags up to one year. The new method to do so combines a classical Autoregressive Integrated Movin...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2018-08
Main Authors: Nooteboom, Peter D, Qing Yi Feng, López, Cristóbal, Hernández-García, Emilio, Dijkstra, Henk A
Format: Article
Language:English
Subjects:
Artificial intelligence
Artificial neural networks
El Nino
Lead time
Machine learning
Mathematical models
Neural networks
Southern Oscillation
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The skill of current predictions of the warm phase of the El Niño Southern Oscillation (ENSO) reduces significantly beyond a lag of six months. In this paper, we aim to increase this prediction skill at lags up to one year. The new method to do so combines a classical Autoregressive Integrated Moving Average technique with a modern machine learning approach (through an Artificial Neural Network). The attributes in such a neural network are derived from topological properties of Climate Networks and are tested on both a Zebiak-Cane-type model and observations. For predictions up to six months ahead, the results of the hybrid model give a better skill than the CFSv2 ensemble prediction by the National Centers for Environmental Prediction (NCEP). Moreover, results for a twelve month lead time prediction have a similar skill as the shorter lead time predictions.
ISSN:2331-8422
DOI:10.48550/arxiv.1803.10076