用物理约束提升北极海冰速度和浓度预测精度
Prediction of Sea Ice Velocity and Concentration in the Arctic Ocean using Physics-informed Neural Network
- 将海冰物理规律融入神经网络，增强模型泛化能力
- 小样本训练下仍优于纯数据驱动模型，尤其在融冰期表现更优
- 适合需要高物理一致性海冰预测的气候研究与导航应用
随着北极海洋遥感数据增多，基于数据的机器学习技术被广泛用于预测海冰速度（SIV）和海冰浓度（SIC）。然而，完全依赖数据的模型在泛化性和物理一致性方面存在局限，尤其当海冰变薄、融化加速时，历史数据训练的模型可能无法准确反映未来动态变化。本研究基于层次信息共享U-Net（HIS-Unet）架构，提出物理信息神经网络（PINN）策略，引入物理损失函数和激活函数，确保输出符合海冰物理规律。结果表明，该模型在日尺度的SIV和SIC预测中优于纯数据驱动模型，即使在小样本训练下也表现优异，尤其在融冰季、初冻季及快速移动冰区的SIC预测上提升显著。
原文摘要 · Abstract (English)
As an increasing amount of remote sensing data becomes available in the Arctic Ocean, data-driven machine learning (ML) techniques are becoming widely used to predict sea ice velocity (SIV) and sea ice concentration (SIC). However, fully data-driven ML models have limitations in generalizability and physical consistency due to their excessive reliance on the quantity and quality of training data. In particular, as Arctic sea ice entered a new phase with thinner ice and accelerated melting, there is a possibility that an ML model trained with historical sea ice data cannot fully represent the dynamically changing sea ice conditions in the future. In this study, we develop physics-informed neural network (PINN) strategies to integrate physical knowledge of sea ice into the ML model. Based on the Hierarchical Information-sharing U-net (HIS-Unet) architecture, we incorporate the physics loss function and the activation function to produce physically plausible SIV and SIC outputs. Our PINN model outperforms the fully data-driven model in the daily predictions of SIV and SIC, even when trained with a small number of samples. The PINN approach particularly improves SIC predictions in melting and early freezing seasons and near fast-moving ice regions.
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