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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2502.10957 |
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| _version_ | 1866929717792210944 |
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| author | Chen, Haoming Zhong, Xiaohui Zhai, Qiang Li, Xiaomeng Chan, Ying Wa Chan, Pak Wai Huang, Yuanyuan Li, Hao Shi, Xiaoming |
| author_facet | Chen, Haoming Zhong, Xiaohui Zhai, Qiang Li, Xiaomeng Chan, Ying Wa Chan, Pak Wai Huang, Yuanyuan Li, Hao Shi, Xiaoming |
| contents | Accurate nowcasting of convective clouds from satellite imagery is essential for mitigating the impacts of meteorological disasters, especially in developing countries and remote regions with limited ground-based observations. Recent advances in deep learning have shown promise in video prediction; however, existing models frequently produce blurry results and exhibit reduced accuracy when forecasting physical fields. Here, we introduce SATcast, a diffusion model that leverages a cascade architecture and multimodal inputs for nowcasting cloud fields in satellite imagery. SATcast incorporates physical fields predicted by FuXi, a deep-learning weather model, alongside past satellite observations as conditional inputs to generate high-quality future cloud fields. Through comprehensive evaluation, SATcast outperforms conventional methods on multiple metrics, demonstrating its superior accuracy and robustness. Ablation studies underscore the importance of its multimodal design and the cascade architecture in achieving reliable predictions. Notably, SATcast maintains predictive skill for up to 24 hours, underscoring its potential for operational nowcasting applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_10957 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Skillful Nowcasting of Convective Clouds With a Cascade Diffusion Model Chen, Haoming Zhong, Xiaohui Zhai, Qiang Li, Xiaomeng Chan, Ying Wa Chan, Pak Wai Huang, Yuanyuan Li, Hao Shi, Xiaoming Computer Vision and Pattern Recognition Atmospheric and Oceanic Physics Accurate nowcasting of convective clouds from satellite imagery is essential for mitigating the impacts of meteorological disasters, especially in developing countries and remote regions with limited ground-based observations. Recent advances in deep learning have shown promise in video prediction; however, existing models frequently produce blurry results and exhibit reduced accuracy when forecasting physical fields. Here, we introduce SATcast, a diffusion model that leverages a cascade architecture and multimodal inputs for nowcasting cloud fields in satellite imagery. SATcast incorporates physical fields predicted by FuXi, a deep-learning weather model, alongside past satellite observations as conditional inputs to generate high-quality future cloud fields. Through comprehensive evaluation, SATcast outperforms conventional methods on multiple metrics, demonstrating its superior accuracy and robustness. Ablation studies underscore the importance of its multimodal design and the cascade architecture in achieving reliable predictions. Notably, SATcast maintains predictive skill for up to 24 hours, underscoring its potential for operational nowcasting applications. |
| title | Skillful Nowcasting of Convective Clouds With a Cascade Diffusion Model |
| topic | Computer Vision and Pattern Recognition Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2502.10957 |