A residual network with geographical and meteorological attention for multi-year ENSO forecasts - Song, Dan; Ling, Yuting; Hao, Tong; Li, Wenhui; Liu, Wen; Ren, Tongwei; Wei, Zhiqiang; Liu, An-an (Frontiers in Marine Science, 2023)
Introduction As global temperatures continue to rise, extreme weather phenomena such as El Niño and the Southern Oscillation (ENSO) near the equatorial Pacific Ocean are occurring more frequently and leading to tropical cyclones, droughts, and a series of extreme weather disasters. Accurately predicting ENSO in advance can greatly reduce the serious damage to human society, economy, and ecological environment. However, existing methods often neglect the data relation between geographical regions and meteorological factors, hindering the accuracy of ENSO prediction. Methods To overcome this problem, we propose a residual network with geographical and meteorological attention to capture important geographical information and explore the spatio-temporal correlation of different meteorological factors. Specifically, we propose two main attention modules: (1) the Geographical Semantic Information Enhancement Module (GSIEM), which selectively attends to important geographical regions and filters out irrelevant noise through a spatial-axis attention map, and (2) the Meteorological Factors Discriminating Enhancement Module (MFDEM), which aims to learn the spatio-temporal dependency of different meteorological factors using a learnable channel-axis weight map. We then integrate our proposed two attention modules into the backbone using residual connection, enhancing the model's prediction ability. Results We conducted extensive experimental comparisons and ablation studies to evaluate the performance of our proposed method. The results show that our method outperforms existing state-of-the-art methods in ENSO prediction, with a significant improvement in prediction accuracy. Discussion Our proposed method effectively captures geographical and meteorological information, facilitating accurate ENSO prediction. The attention modules we proposed can effectively filter out irrelevant noise and learn the spatio-temporal dependency of different meteorological factors, contributing to the superior performance of our model. Overall, our study provides a novel approach for ENSO prediction and has great potential for practical applications.