Multi-head attention ResUnet with sequential sliding windows for sea surface height anomaly field forecast: A regional study in North Atlantic Ocean

Accurate and efficient prediction of Sea surface height anomaly (SSHA) field is very important for operational marine monitoring and engineering. It is also a vital indicator of better understanding global climate changes and ocean dynamics. Many traditional SSHA forecasting methods focused primarily on single grid-point based predictions, and some classical Recurrent Neural Network/Long-Short-Term-Memory based machine learning approaches can cost heavily computational consumption, also the SSHA prediction by only employing the observations in single locations would lead to large potential uncertainties. This study proposed a novel purely Convolutional neural network (CNN) based Unet structure with Multi-head attention mechanism and Residual CNN blocks to accomplish daily SSHA variabilities prediction with higher accuracy and better computational efficiency. Specifically, the satellite altimetry observations aggregated by Data Unification and Altimeter Combination System (DUACS) from a sub-region with very high occurring frequently of storm surges and mesoscale/small-scale eddies in North Atlantic Ocean (NAO) was employed to verify the practicability and functionality of the proposed model. In addition, several existed deep learning approaches were adopted to implement a better comparison. Experimental results in this study demonstrated that the proposed methodology can achieve superior prediction performance amongst the four deep learning methods, and specifically, presenting significant superiorities on the aspect of computational costs compared to recurrent-based neural networks. Very high accurate SSHA predictions with averaged RMSE as 0.018m and Correlation coefficient as 0.99 for 1day ahead forecasting, and corresponded 0.107m, 0.80 for 24days ahead forecasting was obtained by the proposed approach. It took about less than 2minutes to fulfil a 5-year SSHA field forecasts together with optimal model training process, Additional forecasting experiments based on different seasonal SSHA sequential patterns reveal that the proposed purely CNN-based technique show pretty well generalization capability. This study provides an alternative promising-prospect on the application of purely CNN-based deep learning methods in sea surface variabilities forecasting.