Air temperature prediction is a significant task for researchers and forecasters in the field of meteorology. In this paper, we present an innovative, deep spatial–temporal learning air temperature forecasting framework based on graph attention network and gated recurrent unit. Particularly, historical observations containing multiple environmental variables at different stations are constructed as graph signals. The original stations’ conditions and the learned attention information are all included in our model, which overcomes the flaw of the conventional graph network approach. Results of experiments on a real-world dataset demonstrate that, compared to the state-of-the-art baselines, our model achieves the best performance in terms of short-, middle- and long-term air temperature predictions.

气温预测是气象领域研究人员和预报员的一项重要任务。在本文中，我们提出了一种基于图注意力网络和门控循环单元的创新、深度时空学习气温预测框架。特别是，在不同站点包含多个环境变量的历史观测被构建为图形信号。原始站点的条件和学习到的注意力信息都包含在我们的模型中，克服了传统图网络方法的缺陷。真实世界数据集的实验结果表明，与最先进的基线相比，我们的模型在短期、中期和长期气温预测方面取得了最佳性能。