ENSO analysis and prediction using deep learning: A review
Gai‐Ge Wang, Honglei Cheng, Yiming Zhang, Hui Yu
Abstract
El Niño/Southern Oscillation (ENSO) mainly occurs in the tropical Pacific Ocean every a few years. But it affects the climate around the world and has a dramatic impact on the development of ecology and agriculture. The analysis and prediction of ENSO become particularly important for meteorology and disaster management. However, due to insufficient data, spring predictability barrier (SPB), and model uncertainty, traditional analysis models face challenges. To address these issues, researchers begin to apply deep learning (DL) technologies to ENSO research, exploring the impact of ENSO on the world's extreme climate changes. In recent years, deep learning-based methods have obtained impressive progress with more accurate and effective predictions of ENSO. In this paper, we summarize the attempts of DL technologies in predicting ENSO. We first introduce the properties of ENSO, followed by the architecture introduction of DL technologies and their application to ENSO. We then investigate the potential of DL technologies for ENSO prediction from various aspects, including model evaluation metrics, prediction algorithms, overcoming SPB and prediction uncertainty. Finally, we provide discussions on the future trends and challenges of using DL technologies for ENSO prediction.