{
  "annotation_id": "f058c76c-93b8-474e-b583-7c790ea8d9bb",
  "date_created": "2026-02-17T05:53:05.001000Z",
  "date_modified": "2026-02-17T05:53:05.001000Z",
  "file_hash": "79eba839e2fedb96d0e176079275d8bec7c5bfbb9d70015304be237c184c8ad2",
  "private": false,
  "record": {
    "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.",
    "arxiv_id": "2601.06078",
    "authors": [
      "Yin Wang",
      "Chunlin Gong",
      "Zhuozhen Xu",
      "Lehan Zhang",
      "Xiang Wu"
    ],
    "categories": [
      "cs.CV",
      "physics.ao-ph"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "title": "OptFormer: Optical Flow-Guided Attention and Phase Space Reconstruction for SST Forecasting",
    "url": "https://arxiv.org/abs/2601.06078",
    "version": "v1"
  },
  "schema_id": "dorsal/arxiv",
  "source": {
    "execution_id": "6f681f02-d589-47d8-8fa9-3b2f2b5b806d",
    "id": "arXiv Dataset",
    "type": "Model",
    "variant": "snapshot-2026-01-17",
    "version": "0.1.0"
  },
  "user_id": 1000002
}