Abstract
Accurate digitization of synoptic ocean features is crucial for climate studies and the operational forecasting of ocean and coupled ocean-atmosphere systems. Today, for some North Atlantic operational regional models, skilled human experts visualize and extract the gulf stream and rings (warm and cold eddies) through an extensive and knowledge-based manual process. To automate this task, we develop a dynamics-inspired deep learning system that extracts the Gulf Stream and rings from concurrent satellite images of sea surface temperature (SST) and sea surface height (SSH). We pose the above problem as a multilabel semantic image segmentation problem. A novel deep convolutional neural network architecture named W-Net, with two parallel encoder-decoder branches, is developed to perform the segmentation. The W-Net's one branch is the SST branch (accepts SST image as input) and another is the SSH branch (accepts SSH as input), and the final output is a segmentation of gulf stream, warm rings, and cold rings. A dataset consisting of SST, SSH, and manual feature annotation (ground truth) from 2014 to 2018 is used for training. For gulf stream, we obtain 82.7% raw test accuracy and a low error of 4.39% in the detected path length. For the Rings, we obtain more than 71% raw eddy detection accuracy. A detailed ablation study and an examination of both SST and SSH parts of the network are presented to understand how the deep neural network learns to segment the gulf stream's meandering path and Rings accurately.