Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Ni\~no-Southern Oscillation (ENSO). However, current deep learning models are based on convolutional neural networks which are difficult to interpret and can fail to model large-scale atmospheric patterns. In comparison, graph neural networks (GNNs) are capable of modeling large-scale spatial dependencies and are more interpretable due to the explicit modeling of information flow through edge connections. We propose the first application of graph neural networks to seasonal forecasting. We design a novel graph connectivity learning module that enables our GNN model to learn large-scale spatial interactions jointly with the actual ENSO forecasting task. Our model, \graphino, outperforms state-of-the-art deep learning-based models for forecasts up to six months ahead. Additionally, we show that our model is more interpretable as it learns sensible connectivity structures that correlate with the ENSO anomaly pattern.

翻译：深层次的学习模型最近比预测厄尔尼诺-南方涛动(ENSO)等最先进的季节性预测模型效绩强。然而,目前的深层次学习模型以难以解释且无法模拟大规模大气模式的进化神经网络为基础。相比之下,图形神经网络(GNN)能够建模大规模空间依赖性,并且由于对通过边缘连接的信息流动进行明确的建模,因此更易于解释。我们建议首次将图形神经网络应用于季节性预报。我们设计了一个新型的图形连接学习模块,使我们的GNN模型能够与实际的ENSO预报任务一起学习大规模空间互动。我们的模型、地理学模型、超越先进的深层学习模型,用于预测到六个月的时间。此外,我们显示我们的模型更容易被解释,因为它学习了与ENSO异常模式相关的合理连接结构。