Njord用概率图神经网络实现海洋预报不确定性估计，提升全球与区域预测精度。
Njord: A Probabilistic Graph Neural Network for Ensemble Ocean Forecasting
- 基于潜在变量与图神经网络，单次前向传播生成多组预报样本。
- 全球0.25°分辨率下平均误差最低，海表温度预测提升显著。
- 适用于复杂海岸线，适合需要风险评估的海洋科研与气候决策者。
海洋动力学本质上具有混沌性，但现有机器学习海洋模型仅输出确定性预报。我们提出Njord，一种适用于全球与区域尺度的概率数据驱动海洋预报模型。Njord结合深度潜在变量框架与图神经网络结构，可在一次前向传播中完成每一步预报采样。在0.25°全球分辨率与2 km区域分辨率（波罗的海）上应用。为适应大规模不规则海面网格，引入基于K-means的聚类网格。实验表明，相较于确定性机器学习基线，Njord在两个域均表现优异，并提供来自采样集合的不确定性估计。在全局OceanBench基准测试中，对上层海洋变量的平均误差最低，尤其在海表温度预测上改善最为明显。
原文摘要 · Abstract (English)
Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a probabilistic data-driven model for ocean forecasting, applicable to both global and regional domains. Njord combines a deep latent variable framework with a graph neural network architecture, enabling sampling each forecast step in a single forward pass. We apply Njord globally at 0.25° resolution and regionally to the Baltic Sea at 2 km resolution. To scale to these large ocean grids we introduce K-means cluster meshes that adapt to irregular sea surface geometry. Experiments demonstrate strong performance on both domains compared to deterministic machine learning baselines, while also providing uncertainty estimates from the sampled ensemble forecasts. On the global OceanBench benchmark, Njord achieves the lowest errors on average across upper-ocean variables when evaluated against real-world observations, with the largest improvements in surface temperature prediction.
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