Forecasting the Indian Ocean Dipole With Deep Learning Techniques - Jun Liu; Youmin Tang; Yanling Wu; Tang Li; Qiang Wang; Dake Chen (Geophysical Research Letters)
Abstract In the present research, Indian Ocean Dipole (IOD) prediction was explored using statistical methods based on deep learning techniques. First, convolutional neural network (CNN) models were trained using sea‐surface temperature anomaly (SSTA) maps of the Indian Ocean from 1854 to 1989, and the properly trained CNN models were then validated with the period from 1991 to 2019. The results indicate that the deep learning approach is capable of forecasting the IOD at lead times up to 7 months. The forecast skills of CNN are superior to those of the dynamic models in the North American Multi‐Model Ensemble (NMME). The CNN outperforms the NMME with lower sensitivity to predictability barriers and fewer systematic errors. Moreover, the gradient heat map analysis demonstrates that the triggering precursors selected by CNN models for IOD events are novel and physically sensible. These results suggest the CNN to be a new and effective tool for both IOD prediction and comprehension.