Leveraging an Atmospheric Foundational Model for Subregional Sea Surface Temperature Forecasting

• URL: http://arxiv.org/abs/2510.25563v1
• Date: Wed, 29 Oct 2025 14:30:12 GMT
• Title: Leveraging an Atmospheric Foundational Model for Subregional Sea Surface Temperature Forecasting
• Authors: Víctor Medina, Giovanny A. Cuervo-Londoño, Javier Sánchez,
• Abstract summary: We adapt a deep learning model to predict sea temperature (SST) in the Canary Upwelling System.<n>By fine-tuning this model with high-resolution oceanographic reanalysis data, we demonstrate its ability to capture complex patterns.<n>The model successfully reproduces large-scale SST structures but faces challenges in capturing finer details in coastal regions.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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