Deep Residual Convolutional Neural Network Combining Dropout and Transfer Learning for ENSO Forecasting

Jie Hu, Bin Weng, Tianqiang Huang, Jianyun Gao, Feng Ye, Lijun You

Geophysical Research Letters · 25 November 2021 · China Meteorological Administration (CMA)

In one sentence. A deep residual CNN with dropout and transfer learning extends ENSO Niño3.4 forecast skill to a 20-month lead, three months beyond the previous optimal model.

Abstract

Abstract To improve EI Niño‐Southern Oscillation (ENSO) amplitude and type forecast, we propose a model based on a deep residual convolutional neural network with few parameters. We leverage dropout and transfer learning to overcome the challenge of insufficient data in model training process. By applying the dropout technique, the model effectively predicts the Niño3.4 Index at a lead time of 20 months during the 1984–2017 evaluation period, which is three more months than that by the existing optimal model. Moreover, with homogeneous transfer learning this model precisely predicts the Oceanic Niño Index up to 18 months in advance. Using heterogeneous transfer learning this model achieved 83.3% accuracy for forecasting the 12‐month‐lead EI Niño type. These results suggest that our proposed model can enhance the ENSO prediction performance.

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