Accurate precipitation nowcasting is essential for various purposes, including flood prediction, disaster management, optimizing agricultural activities, managing transportation routes and renewable energy. While several studies have addressed this challenging task from a sequence-to-sequence perspective, most of them have focused on a single area without considering the existing correlation between multiple disjoint regions. In this paper, we formulate precipitation nowcasting as a spatiotemporal graph sequence nowcasting problem. In particular, we introduce Graph Dual-stream Convolutional Attention Fusion (GD-CAF), a novel approach designed to learn from historical spatiotemporal graph of precipitation maps and nowcast future time step ahead precipitation at different spatial locations. GD-CAF consists of spatio-temporal convolutional attention as well as gated fusion modules which are equipped with depthwise-separable convolutional operations. This enhancement enables the model to directly process the high-dimensional spatiotemporal graph of precipitation maps and exploits higher-order correlations between the data dimensions. We evaluate our model on seven years of precipitation maps across Europe and its neighboring areas collected from the ERA5 dataset, provided by Copernicus. The model receives a fully connected graph in which each node represents historical observations from a specific region on the map. Consequently, each node contains a 3D tensor with time, height, and width dimensions. Experimental results demonstrate that the proposed GD-CAF model outperforms the other examined models. Furthermore, the averaged seasonal spatial and temporal attention scores over the test set are visualized to provide additional insights about the strongest connections between different regions or time steps. These visualizations shed light on the decision-making process of our model.

翻译：准确的降水临近预报对于洪水预测、灾害管理、优化农业活动、管理交通路线以及可再生能源等多种应用至关重要。尽管已有研究从序列到序列的角度解决了这一具有挑战性的任务，但大多数研究聚焦于单一区域，而未考虑多个不相邻区域之间存在的相关性。在本文中，我们将降水临近预报建模为时空图序列临近预测问题。具体而言，我们提出了图双流卷积注意力融合（GD-CAF），这是一种新颖的方法，旨在从历史降水图的时空图中学习，并预测未来时间步长内不同空间位置的降水。GD-CAF由时空卷积注意力和门控融合模块组成，这些模块配备了深度可分离卷积操作。这一增强功能使模型能够直接处理高维的降水图时空图，并利用数据维度之间的高阶相关性。我们使用来自Copernicus提供的ERA5数据集，对欧洲及其邻近地区七年的降水图进行评估。模型接收一个全连接图，其中每个节点代表地图上特定区域的历史观测值。因此，每个节点包含一个具有时间、高度和宽度维度的三维张量。实验结果表明，所提出的GD-CAF模型优于其他被测试模型。此外，我们可视化了测试集上季节平均的空间和时间注意力分数，以提供关于不同区域或时间步长之间最强连接的额外见解。这些可视化揭示了模型决策过程的内部机制。