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| Main Authors: | , , , , , , |
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| Format: | Preprint |
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2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2404.16037 |
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| _version_ | 1866913328179183616 |
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| author | Xiong, Yutong Zhu, Xun Wu, Ming Li, Weiqing Mo, Fanbin Zhang, Chuang Zhang, Bin |
| author_facet | Xiong, Yutong Zhu, Xun Wu, Ming Li, Weiqing Mo, Fanbin Zhang, Chuang Zhang, Bin |
| contents | 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 this objective, we introduce Vision-Numerical Fusion Graph Convolutional Network (VN-Net), which mainly utilizes: 1) Numerical-GCN (N-GCN) to adaptively model the static and dynamic patterns of spatio-temporal numerical data; 2) Vision-LSTM Network (V-LSTM) to capture multi-scale joint channel and spatial features from time series satellite images; 4) a GCN-based decoder to generate hourly predictions of specified meteorological factors. As far as we know, VN-Net is the first attempt to introduce GCN method to utilize multi-modal data for better handling sparse spatio-temporal meteorological forecasting. Our experiments on Weather2k dataset show VN-Net outperforms state-of-the-art by a significant margin on mean absolute error (MAE) and root mean square error (RMSE) for temperature, relative humidity, and visibility forecasting. Furthermore, we conduct interpretation analysis and design quantitative evaluation metrics to assess the impact of incorporating vision data. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_16037 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | VN-Net: Vision-Numerical Fusion Graph Convolutional Network for Sparse Spatio-Temporal Meteorological Forecasting Xiong, Yutong Zhu, Xun Wu, Ming Li, Weiqing Mo, Fanbin Zhang, Chuang Zhang, Bin Computer Vision and Pattern Recognition Machine Learning Atmospheric and Oceanic Physics 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 this objective, we introduce Vision-Numerical Fusion Graph Convolutional Network (VN-Net), which mainly utilizes: 1) Numerical-GCN (N-GCN) to adaptively model the static and dynamic patterns of spatio-temporal numerical data; 2) Vision-LSTM Network (V-LSTM) to capture multi-scale joint channel and spatial features from time series satellite images; 4) a GCN-based decoder to generate hourly predictions of specified meteorological factors. As far as we know, VN-Net is the first attempt to introduce GCN method to utilize multi-modal data for better handling sparse spatio-temporal meteorological forecasting. Our experiments on Weather2k dataset show VN-Net outperforms state-of-the-art by a significant margin on mean absolute error (MAE) and root mean square error (RMSE) for temperature, relative humidity, and visibility forecasting. Furthermore, we conduct interpretation analysis and design quantitative evaluation metrics to assess the impact of incorporating vision data. |
| title | VN-Net: Vision-Numerical Fusion Graph Convolutional Network for Sparse Spatio-Temporal Meteorological Forecasting |
| topic | Computer Vision and Pattern Recognition Machine Learning Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2404.16037 |