{"ID":2824522,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2601.06078","arxiv_id":"2601.06078","title":"OptFormer: Optical Flow-Guided Attention and Phase Space Reconstruction for SST Forecasting","abstract":"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.","short_abstract":"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 recons...","url_abs":"https://arxiv.org/abs/2601.06078","url_pdf":"https://arxiv.org/pdf/2601.06078v1","authors":"[\"Yin Wang\",\"Chunlin Gong\",\"Zhuozhen Xu\",\"Lehan Zhang\",\"Xiang Wu\"]","published":"2025-12-29T22:27:15Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"physics.ao-ph\"]","methods":"[]","has_code":false}