用图神经网络模拟全球海洋，15天预报仍准确，相关性损失提升预测效果。
Skillful Global Ocean Emulation and the Role of Correlation-Aware Loss
- 基于图结构的模型，用大气驱动实现海洋短期预报。
- 10-15天预报仍有显著技巧，无需自回归训练。
- 引入马哈拉诺比斯距离损失，更好捕捉海洋变量关联性。
机器学习海况模拟器在大气预报中表现优异，其应用于全球海洋动力学也前景广阔。本文将GraphCast架构改造为仅处理海洋的模拟器，以预设大气条件为驱动，进行中短期预测。模型在NOAA UFS-Replay数据集上训练，采用24小时时间步长、单初始条件，且不使用自回归训练，仍可实现10-15天的技能性预报。我们进一步验证了马哈拉诺比斯距离作为损失函数的效果，相比均方误差损失，能更有效利用目标变量倾向项之间的相关性，提升预报精度。通过分析预报场的空间相关性，发现该相关性感知损失在统计与动力层面起到了正则化作用，改善了缓慢耦合的海洋动力背景场，有利于后续数据同化等任务。
原文摘要 · Abstract (English)
Machine learning emulators have shown extraordinary skill in forecasting atmospheric states, and their application to global ocean dynamics offers similar promise. Here, we adapt the GraphCast architecture into a dedicated ocean-only emulator, driven by prescribed atmospheric conditions, for medium-range predictions. The emulator is trained on NOAA's UFS-Replay dataset. Using a 24 hour time step, single initial condition, and without using autoregressive training, we produce an emulator that provides skillful forecasts for 10-15 day lead times. We further demonstrate the use of Mahalanobis distance as loss that improves the forecast skill compared to the Mean Squared Error loss by explicitly accounting for the correlations between tendencies of the target variables. Using spatial correlation analysis of the forecasted fields, we also show that the proposed correlation-aware loss acts as a statistical-dynamical regularizer for the slow, correlated dynamics of the global oceans, offering a better background forecast for downstream tasks like data assimilation.
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