利用大气基础模型进行次区域海表面温度预测
Leveraging an Atmospheric Foundational Model for Subregional Sea Surface Temperature Forecasting
- Centro de Tecnologías de la Imagen (CTIM)（图像技术中心）
- Instituto Universitario de Cibernética, Empresas y Sociedad (IUCES)（网络、企业与社会大学研究所）
- University of Las Palmas de Gran Canaria, Spain（大加那利岛大学）
- Oceanografía Física y Geofísica Aplicada (OFYGA)（应用物理海洋学与地球物理研究所）
- Instituto Universitario de Investigación en Acuicultura Sostenible y Ecosistemas Marinos (ECOAQUA)（可持续水产养殖与海洋生态系统大学研究所）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本研究将大气预训练基础模型Aurora适配用于加那利上升流系统的海表面温度预测，通过分阶段微调等方法实现了高精度预测，验证了跨领域预训练模型在海洋预测中的可行性。
AI中文摘要：
海洋变量的准确预测对于理解气候变化、管理海洋资源以及优化海事活动至关重要。传统海洋预测依赖数值模型，但这类方法在计算成本和可扩展性方面存在局限。本研究将原本为大气预测设计的深度学习基础模型Aurora（极光模型）进行适配，用于预测加那利上升流系统的海表面温度（SST）。通过使用高分辨率海洋再分析数据对该模型进行微调，我们证明其能够捕捉复杂的时空模式，同时降低计算需求。我们的方法采用分阶段微调流程，引入纬度加权误差指标，并优化超参数以实现高效学习。实验结果显示，该模型取得了0.119K的低均方根误差（RMSE），同时维持了较高的异常相关系数（ACC≈0.997）。该模型成功复现了大尺度SST结构，但在捕捉沿海区域的更精细细节方面存在挑战。本研究证明了在不同领域预训练的深度学习模型可应用于海洋场景，为数据驱动的海洋预测领域做出了贡献。未来的改进方向包括整合更多海洋变量、提升空间分辨率，以及探索物理信息神经网络以增强可解释性和认知深度。这些进展能够提升气候建模和海洋预测的准确性，为环境和经济领域的决策提供支持。
英文摘要：
The accurate prediction of oceanographic variables is crucial for understanding climate change, managing marine resources, and optimizing maritime activities. Traditional ocean forecasting relies on numerical models; however, these approaches face limitations in terms of computational cost and scalability. In this study, we adapt Aurora, a foundational deep learning model originally designed for atmospheric forecasting, to predict sea surface temperature (SST) in the Canary Upwelling System. By fine-tuning this model with high-resolution oceanographic reanalysis data, we demonstrate its ability to capture complex spatiotemporal patterns while reducing computational demands. Our methodology involves a staged fine-tuning process, incorporating latitude-weighted error metrics and optimizing hyperparameters for efficient learning. The experimental results show that the model achieves a low RMSE of 0.119K, maintaining high anomaly correlation coefficients (ACC $\approx 0.997$). The model successfully reproduces large-scale SST structures but faces challenges in capturing finer details in coastal regions. This work contributes to the field of data-driven ocean forecasting by demonstrating the feasibility of using deep learning models pre-trained in different domains for oceanic applications. Future improvements include integrating additional oceanographic variables, increasing spatial resolution, and exploring physics-informed neural networks to enhance interpretability and understanding. These advancements can improve climate modeling and ocean prediction accuracy, supporting decision-making in environmental and economic sectors.
