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.
Abstract (translated)
海面温度（SST）预测在气候建模和灾害预报中扮演着重要角色。然而，由于其非线性的时空动态变化以及较长的预测周期，这一任务仍然具有挑战性。为此，我们提出了OptFormer模型，这是一种新颖的编码器-解码器架构，它结合了相空间重构与基于光流引导的运动感知注意机制。不同于传统的注意力方法，我们的方法利用帧间运动线索来强调空间场中的相对变化，从而使模型能够专注于动态区域并更有效地捕捉长期时间依赖关系。 在NOAA SST数据集上进行的多尺度实验表明，在1:1训练与预测设置下，OptFormer达到了优于现有基线模型的表现，在准确性和鲁棒性方面有显著提升。
URL
https://arxiv.org/abs/2601.06078
