GD-CAF: Graph Dual-stream Convolutional Attention Fusion for Precipitation Nowcasting

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.