OptFormer: Optical Flow-Guided Attention and Phase Space Reconstruction for SST Forecasting

TL;DR

OptFormer is a novel model that combines phase-space reconstruction and optical flow-guided attention to improve sea surface temperature forecasting by capturing nonlinear dynamics and long-range dependencies.

Contribution

It introduces a new encoder-decoder architecture integrating motion-aware attention with phase-space reconstruction for SST prediction.

Findings

Outperforms existing baselines in accuracy and robustness.

Effectively captures long-range temporal dependencies.

Demonstrates superior performance across multiple spatial scales.

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.