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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2601.06078 |
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| _version_ | 1866918280985313280 |
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| author | Wang, Yin Gong, Chunlin Xu, Zhuozhen Zhang, Lehan Wu, Xiang |
| author_facet | Wang, Yin Gong, Chunlin Xu, Zhuozhen Zhang, Lehan Wu, Xiang |
| contents | Sea Surface Temperature (SST) prediction plays a vital role in climate modeling and disaster forecasting. However, it remains challenging due to its nonlinear spatiotemporal dynamics and extended prediction horizons. To address this, we propose OptFormer, a novel encoder-decoder model that integrates phase-space reconstruction with a motion-aware attention mechanism guided by optical flow. Unlike conventional attention, our approach leverages inter-frame motion cues to highlight relative changes in the spatial field, allowing the model to focus on dynamic regions and capture long-range temporal dependencies more effectively. Experiments on NOAA SST datasets across multiple spatial scales demonstrate that OptFormer achieves superior performance under a 1:1 training-to-prediction setting, significantly outperforming existing baselines in accuracy and robustness. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_06078 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OptFormer: Optical Flow-Guided Attention and Phase Space Reconstruction for SST Forecasting Wang, Yin Gong, Chunlin Xu, Zhuozhen Zhang, Lehan Wu, Xiang Computer Vision and Pattern Recognition Atmospheric and Oceanic Physics Sea Surface Temperature (SST) prediction plays a vital role in climate modeling and disaster forecasting. However, it remains challenging due to its nonlinear spatiotemporal dynamics and extended prediction horizons. To address this, we propose OptFormer, a novel encoder-decoder model that integrates phase-space reconstruction with a motion-aware attention mechanism guided by optical flow. Unlike conventional attention, our approach leverages inter-frame motion cues to highlight relative changes in the spatial field, allowing the model to focus on dynamic regions and capture long-range temporal dependencies more effectively. Experiments on NOAA SST datasets across multiple spatial scales demonstrate that OptFormer achieves superior performance under a 1:1 training-to-prediction setting, significantly outperforming existing baselines in accuracy and robustness. |
| title | OptFormer: Optical Flow-Guided Attention and Phase Space Reconstruction for SST Forecasting |
| topic | Computer Vision and Pattern Recognition Atmospheric and Oceanic Physics |
| url | https://arxiv.org/abs/2601.06078 |