Ocean current segmentation at different depths and correlation with temperature in a MPAS-ocean simulation

When analyzing and interpreting results of an ocean simulation, the prevalent method in oceanography is to visualize the complete dataset. However, this can lead to data being missed or misinterpreted due to the distraction caused by the extraneous data of the simulation. Furthermore, when the data stretches over many layers in depth or over numerous time-steps, the ability to track attributes such as ocean currents becomes difficult due to the complexity of the data. We propose an image processing approach to simulation preprocessing for visualization purposes, which offers automation of ocean current tracking within a simulation and ocean current segmentation from the rest of the simulation data. Using the proposed approach, it is possible to automatically identify the most scientifically-relevant streams, extract them from the rest of the simulation and correlate their behavior with other simulation parameters.