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Survey on the Application of Artificial Intelligence in ENSO Forecasting

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  • Wei Fang

    (School of Computer and Software, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China
    State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China
    Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Yu Sha

    (School of Computer and Software, Engineering Research Center of Digital Forensics, Ministry of Education, Nanjing University of Information Science and Technology, Nanjing 210044, China)

  • Victor S. Sheng

    (Department of Computer, Texas Tech University, Lubbock, TX 79409, USA)

Abstract

Climate disasters such as floods and droughts often bring heavy losses to human life, national economy, and public safety. El Niño/Southern Oscillation (ENSO) is one of the most important inter-annual climate signals in the tropics and has a global impact on atmospheric circulation and precipitation. To address the impact of climate change, accurate ENSO forecasts can help prevent related climate disasters. Traditional prediction methods mainly include statistical methods and dynamic methods. However, due to the variability and diversity of the temporal and spatial evolution of ENSO, traditional methods still have great uncertainty in predicting ENSO. In recent years, with the rapid development of artificial intelligence technology, it has gradually penetrated into all aspects of people’s lives, and the climate field has also benefited. For example, deep learning methods in artificial intelligence can automatically learn and train from a large amount of sample data, obtain excellent feature representation, and effectively improve the performance of various learning tasks. It is widely used in computer vision, natural language processing, and other fields. In 2019, Ham et al. used a convolutional neural network (CNN) model in ENSO forecasting 18 months in advance, and the winter ENSO forecasting skill could reach 0.64, far exceeding the dynamic model with a forecasting skill of 0.5. The research results were regarded as the pioneering work of deep learning in the field of weather forecasting. This paper introduces the traditional ENSO forecasting methods and focuses on summarizing the various latest artificial intelligence methods and their forecasting effects for ENSO forecasting, so as to provide useful reference for future research by researchers.

Suggested Citation

  • Wei Fang & Yu Sha & Victor S. Sheng, 2022. "Survey on the Application of Artificial Intelligence in ENSO Forecasting," Mathematics, MDPI, vol. 10(20), pages 1-22, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3793-:d:942611
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    References listed on IDEAS

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    Cited by:

    1. Roberto Moro-Visconti & Salvador Cruz Rambaud & Joaquín López Pascual, 2023. "Artificial intelligence-driven scalability and its impact on the sustainability and valuation of traditional firms," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-14, December.

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