首个实现六小时全球海洋预报的智能模型，精度达1/12°。
FuXi-Ocean: A Global Ocean Forecasting System with Sub-Daily Resolution
- 用多时间上下文注意力机制融合不同时段预测，减少误差累积。
- 在1500米深度、1/12°空间分辨率下实现六小时预报，性能领先。
- 适合海洋航运、气候监测等需高频高精度预报的场景。
精准的高分辨率海洋预报对航海作业与环境监测至关重要。传统数值模型虽可实现亚日级、涡旋分辨的预报，但计算成本高，且在细粒度时空尺度上难以保持精度。相较之下，近年数据驱动方法虽提升效率并展现潜力，却通常仅支持日级预报，在亚日预测中因误差累积而表现受限。本文提出FuXi-Ocean，首个实现六小时间隔、1/12°涡旋分辨（最高达1500米深度）的全球数据驱动海洋预报模型。其架构融合上下文感知特征提取与堆叠注意力块预测网络，核心创新为混合时间（Mixture-of-Time, MoT）模块，通过学习变量特性的可靠性权重，自适应融合多时间上下文预测，有效缓解序列预报中的误差累积。全面实验表明，该模型在温度、盐度和海流等关键变量预测上表现优异，覆盖多个深度层。
原文摘要 · Abstract (English)
Accurate, high-resolution ocean forecasting is crucial for maritime operations and environmental monitoring. While traditional numerical models are capable of producing sub-daily, eddy-resolving forecasts, they are computationally intensive and face challenges in maintaining accuracy at fine spatial and temporal scales. In contrast, recent data-driven approaches offer improved computational efficiency and emerging potential, yet typically operate at daily resolution and struggle with sub-daily predictions due to error accumulation over time. We introduce FuXi-Ocean, the first data-driven global ocean forecasting model achieving six-hourly predictions at eddy-resolving 1/12° spatial resolution, reaching depths of up to 1500 meters. The model architecture integrates a context-aware feature extraction module with a predictive network employing stacked attention blocks. The core innovation is the Mixture-of-Time (MoT) module, which adaptively integrates predictions from multiple temporal contexts by learning variable-specific reliability , mitigating cumulative errors in sequential forecasting. Through comprehensive experimental evaluation, FuXi-Ocean demonstrates superior skill in predicting key variables, including temperature, salinity, and currents, across multiple depths.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。