VN-Net: Vision-Numerical Fusion Graph Convolutional Network for Sparse Spatio-Temporal Meteorological Forecasting

arXiv:2404.16037v1 Announce Type: cross
Abstract: Sparse meteorological forecasting is indispensable for fine-grained weather forecasting and deserves extensive attention. Recent studies have highlighted the potential of spatio-temporal graph convolutional networks (ST-GCNs) in predicting numerical data from ground weather stations. However, as one of the highest fidelity and lowest latency data, the application of the vision data from satellites in ST-GCNs remains unexplored. There are few studies to demonstrate the effectiveness of combining the above multi-modal data for sparse meteorological forecasting. Towards …

abstract arxiv attention cs.cv cs.lg data fidelity fine-grained forecasting fusion graph however latency network networks numerical physics.ao-ph studies temporal type vision weather weather forecasting