EGU21-5146, updated on 04 Mar 2021
EGU General Assembly 2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.

El Niño Index forecasting using machine learning techniques 

Wanjiao Song1, Wenfang Lu2, and Qing Dong3
Wanjiao Song et al.
  • 1National Satellite Meteorological Center, China Meteorological Administration,Beijing, China (
  • 2Key Laboratory of Spatial Data Mining & Information Sharing of Ministry of Education, National & Local Joint Engineering Research Center of Satellite Geospatial Information Technology, Fuzhou University, Fuzhou, China
  • 3Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China

El Niño is a large-scale ocean-atmospheric coupling phenomenon in the Pacific. The interaction among marine and atmospheric variables over the tropical Pacific modulate the evolution of El Niño. The latest research shows that machine learning and neural network (NN) have appeared as effective tools to achieve meaningful information from multiple marine and atmospheric parameters. In this paper, we aim to predict the El Niño index more accurately and increase the forecast efficiency of El Niño events. Here, we propose an approach combining a neural network technique with long short-term memory (LSTM) neural network to forecast El Niño phenomenon. The attributes of model are resulted from physical explanation which are tested with the experiments and observations. The neural network represents the connection among multiple variables and machine learning creates models to identify the El Niño events. The preliminary experimental results exhibit that training NN-LSTM model on network metrics time series dataset provides great potential for predicting El Niño phenomenon at lag times of up to more than 6 months.  

How to cite: Song, W., Lu, W., and Dong, Q.: El Niño Index forecasting using machine learning techniques , EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5146,, 2021.

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