In this article, the authors introduce VN-Net, a new approach that combines spatio-temporal graph convolutional networks (ST-GCNs) with vision data from satellites for sparse meteorological forecasting. While previous studies have demonstrated the effectiveness of ST-GCNs in predicting numerical data from ground weather stations, the authors explore the untapped potential of using satellite imagery as high fidelity and low latency data.
VN-Net consists of two main components: Numerical-GCN (N-GCN) and Vision-LSTM Network (V-LSTM). N-GCN is responsible for modeling the static and dynamic patterns of spatio-temporal numerical data, while V-LSTM captures multi-scale joint channel and spatial features from time series satellite images. The authors also develop a GCN-based decoder that generates hourly predictions of specific meteorological factors.
This approach is the first of its kind, as no previous studies have integrated GCN methods with multi-modal data for sparse spatio-temporal meteorological forecasting. To evaluate VN-Net, the authors conducted experiments on the Weather2k dataset and compared the results with state-of-the-art methods. The results demonstrate that VN-Net outperforms existing approaches by a significant margin in terms of mean absolute error (MAE) and root mean square error (RMSE) for temperature, relative humidity, and visibility forecasting.
In addition to the quantitative evaluation, the authors also perform interpretation analysis to gain insights into the impact of incorporating vision data. This analysis helps validate the effectiveness of using satellite imagery in improving meteorological forecasting accuracy.
Overall, this research opens up new possibilities for enhancing meteorological forecasting by leveraging multi-modal data and advanced machine learning techniques. The integration of vision data from satellites with ST-GCNs provides a promising avenue for fine-grained weather forecasting and warrants further exploration. Future studies could focus on expanding the application of VN-Net to other meteorological factors and datasets, as well as optimizing the model architecture to achieve even better results.