Deep Learning-Based Sea Surface Temperature Forecasting Integrating Real-Time Satellite Observation

Shota Fukuyama, Masaaki Iiyama

Abstract: This study proposes a method for forecasting sea surface temperature one day in advance using a deep learning approach. Unlike conventional machine learning-based forecasting methods, our proposed method is novel in its approach for predicting deviations from simulation results using real-time satellite observations. The method involves predicting an optical flow, representing the movement of water masses derived from the simulation results. This predicted flow is then used to deform the satellite observation data, which serves as the input for the deep learning model. Experimental results show that our method effectively improves prediction performance in the area where warm and cold currents meet.