Global Oceanic Eddy-Front Associations From Synergetic Remote Sensing Data by Deep Learning

Recently, fronts (eddies) at the margins of eddies (fronts) have been discovered by observing sea surface temperature (SST) and sea level anomaly (SLA) data. They can both induce strong vertical motions and submesoscale processes, and are important for the vertical exchange of ocean mass and energy as well as ocean ecological processes. However, it raises an important challenge about the global spatiotemporal distribution of eddy-induced fronts and frontal eddies. This letter proposes a deep learning (DL) approach, dubbed eddy-front association detection network (EFADN), that is appropriate for mining eddy-front associations (EFAs) to extract the features of eddy-induced fronts (anticyclonic and cyclonic eddy-induced fronts) and frontal eddies [frontal anticyclonic eddies (AEs) and frontal cyclonic eddies (CEs)] from SLA and SST satellite data during 2006–2015 in the global ocean. The EFADN model integrates encoder-decoder and attention structures. The introduced spatial attention (SA) module in attention structure utilizes large-scale convolutional kernels to extract spatial information, which enlarges the receptive field to enhance the recognition of topological structures between eddies and fronts, improving the ability of EFA detection. The results of comparative experiments demonstrate that EFADN surpasses the state-of-the-art (SOTA) eddy detection model. Ablation studies underscore the crucial importance of all modules within EFADN for achieving accurate detection of EFAs. Moreover, the spatiotemporal distribution characteristics of eddy-induced fronts and frontal eddies are displayed. They are widely dispersed in the western boundary current (WBC) and Antarctic Circumpolar Current (ACC) regions, and they are active in the boreal summer while weak in the austral summer.