Towards an automated detection of the Gulf Stream North Wall from concurrent satellite images

Developing computational methods to automatically identify the Gulf Stream North Wall (GSNW) and similar currents in the ocean is a long-standing need for many types of operational ocean models. Specifically, the Feature-Oriented regional modeling system requires an accurate digitization of the GSNW and Rings (eddies) on a regular basis. Typical methods to determine its position and boundaries require skilled human operators to do a time-consuming manual extraction of visualized features. These experts are performing a feature extraction task that can be automated to save time, guarantee objectivity, and potentially increase precision.In this paper we present first-results from two independent approaches of addressing this issue. In one of the approaches, the dynamical approach, the methodology begins by finding the most-likely bounds of iso-sea-surface-height contours within which the Gulf Stream north wall might fall. Other features, such as eddies, which are also captured, will be set aside after a round of shape analysis. Any gap in the isoheight contours is filled with segments that are generated by combining the slopes from different heights.The second, a machine-learning approach uses an artificial neural network over a GSNW dataset, which has been generated weekly over past six years (2009-2015) by analysts. An artificial neural network is a type of learning algorithm designed as a system of neurons with connections among them. This neural network will first use the analyst-designated GSNW paths to determine the neural weights of the radial basis functions. Then the network will use the concurrent sea-surface height and temperature data that were used to identify those lines, and train itself to develop a smart network which will be able to identify GSNW paths from the concurrent satellite images on its own, with little to no human intervention.In the long-term, we expect to merge the two techniques in a unique and unifying construct to be used operationally. A general approach of this methodology has the potential of being used for other similar operational modeling, reanalysis and skill assessment of numerical model system with data assimilation.