Research on Sea Surface Temperature Prediction Method Based on Multiscale Temporal Dynamics and Frequency-Spatial Attention

Abstract

Seasurface temperature (SST) serves as an essential parameter of the ocean-atmosphere system, and its accurate prediction is of great significance for meteorological disaster warning and marine ecosystem research. To address the inadequate extraction of multiscale temporal evolution and frequency-domain features in existing methods, this article proposes an SST prediction method based on ConvLSTM. By synergizing multiscale temporal dynamics with frequency-spatial feature enhancement, a deep learning architecture named the multiscale temporal frequency-spatial ConvLSTM (MTFS-ConvLSTM) is proposed. The model captures multiscale temporal dependencies in the time dimension via the circular dilated-time module. Simultaneously, it integrates a frequency channel spatial attention module to map the SST field to the frequency domain using discrete cosine transform. This mechanism realizes dynamic filtering of key multifrequency information and spatial feature enhancement, and incorporates spatial attention to improve the modeling capability for complex spatiotemporal patterns of the SST field. Experimental results demonstrate that compared to models, including ConvLSTM, U-Net, SimVP, Swin-Transformer, adaptive Fourier neural operator (AFNO), and WMSR, the proposed MTFS-ConvLSTM model reduces the root-mean-square error of the 20th-day lead time SST prediction by 16.6%, 13.2%, 14.1%, 13.1%, 7.1%, and 9.8%, and the mean absolute error by 17.3%, 20.7%, 28.1%, 20.7%, 4.9%, and 12.8%, respectively, significantly mitigating the error accumulation phenomenon in SST prediction. This study validates the effectiveness of joint frequency and spatiotemporal domain modeling in improving SST prediction accuracy, providing a novel technical approach for marine environmental monitoring.