A deep leaning approach to predict sea surface temperature based on multiple modes

Sea surface temperature (SST) is an essential physical parameter and plays a vital role in ocean-atmosphere interactions. Accurate SST prediction relies on numerical model methods, which require the understanding of complex dynamical and thermal processes. Data-driven SST prediction methods based on deep learning have been studied to obtain quick results. However, recent deep learning models can hardly accurately capture and simulate complex SST patterns, and thus give only lower resolution predictions. Here, SST map prediction is regarded as a spatiotemporal sequence prediction task. The memory in memory (MIM) model and variational mode decomposition (VMD) are combined (VMD-MIM) to accurately detect variations in SST patterns. The original SST data is firstly decomposed into different modes with different frequencies using VMD. For each mode extracted by VMD, MIM is then applied to obtain the corresponding prediction maps separately. SST data from the South China Sea are used to estimate the VMD-MIM method, in which we enter the ten-day SST maps and predict the next seven days. The results show that VMD-MIM can noticeably improve prediction skills by accurately presenting the fine structures (1/10 degrees) of SST maps, which provides a valuable pathway for fast and lightweight short-term spatiotemporal SST predictions.

Automated summary

Sea surface temperature (SST) is a crucial physical parameter for ocean-atmosphere interactions. Accurate prediction of SST relies on numerical model methods, but recent deep learning models struggle to accurately capture complex SST patterns, resulting in lower resolution predictions.