论文标题
图形神经网络可改善厄尔尼诺预测
Graph Neural Networks for Improved El Niño Forecasting
论文作者
论文摘要
基于深度学习的模型最近优于最先进的季节性预测模型，例如预测厄尔尼诺 - 南方振荡（ENSO）。但是，当前的深度学习模型是基于难以解释的卷积神经网络，无法模拟称为远程连接的大规模大气模式。因此，我们建议在长时间的交货时期内的时空图神经网络（GNN）预测ENSO，比当前的最新方法在长时间的粒度和提高的预测技能上应用。通过边缘的信息流的明确建模也可能允许更多可解释的预测。初步结果是有希望的，而且预测的最先进的系统是前1个月和3个月的最先进系统。
Deep learning-based models have recently outperformed state-of-the-art seasonal forecasting models, such as for predicting El Niño-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 called teleconnections. Hence, we propose the application of spatiotemporal Graph Neural Networks (GNN) to forecast ENSO at long lead times, finer granularity and improved predictive skill than current state-of-the-art methods. The explicit modeling of information flow via edges may also allow for more interpretable forecasts. Preliminary results are promising and outperform state-of-the art systems for projections 1 and 3 months ahead.
