Predicting temporal and spatial 4-D ocean temperature using satellite data based on a novel deep learning model

Abstract

The prediction of ocean temperature using sea surface data is crucial for studying ocean-related events and climate change. However, current temperature predictions mainly focus on surface data and rarely consider the temporal relationship of ocean temperature. In this study, we propose a novel deep-learning model to predict ocean temperature for the next two months, which fully considers both temporal and spatial features. The input consists of satellite remote sensing data from the past month, including weekly sea surface temperature, salinity, height, and velocity. The model consists of four modules: convolutional, attention, residual, and transposed convolutional. We compare the model estimation with reanalysis data and conduct temporal, spatial, and vertical distribution analyses. The results demonstrate that the model can accurately predict ocean temperature at different lead time, depths, and locations. Finally, we compare the predicted temperature with actual sea observations to ensure the model's good performance in practical applications. This study shows the tremendous potential of the proposed model in predicting 4-D ocean temperature, providing powerful data support for ocean-related events and climate change research.