Feature Tracking and Visual Analysis of Temporal Scalar Fields in the Ocean

Abstract

The Bay of Bengal (BoB) has maintained its salinity distribution over the years despite a continuous flow of fresh water entering it through rivers on the northern coast, which can dilute its salinity. This can be attributed to the periodic flow of high salinity water (≥ 35 PSU) from the Arabian Sea entering BoB from the south, moving northward, and mixing with this fresh water.

Studying such scientific phenomena depends largely on collecting, exploring, and analyzing available data. Measuring or simulating scientific processes generally results in a set of temporal scalar fields that may be part of an ensemble, or unrelated. To analyze and better understand them, it helps to study the behavior of these fields over time. In oceanography and atmospheric science, visual analysis is frequently used to detect different phenomena and to track dynamic processes like cyclones, eddies, and hurricanes over time. This re-quires the development of methods and frameworks for a meaningful study of these fields by tracking and visualization. The sheer volume of data available for these studies only adds to the list of requirements. In this thesis, we focus on the problem of designing efficient methods and frameworks, that can scale to the growing volume of data and use them to track and visually analyze the movement of salinity in the Bay of Bengal (BoB).

The first part of the thesis discusses a front-based tracking method and its use to track and visually analyze the movement of salinity in the BoB. We introduce a feature definition that represents the movement and shape of high salinity water in BoB, namely fronts, based on geometric analysis of the high salinity water. The method is validated via comparison with well-established observations on the flow of southwest monsoon current (SMC) in the BoB, including its entry from the Arabian Sea and its movement near Sri Lanka. These tools offer new perspectives on the propagation of high salinity water and its mixing with the ambient low salinity waters.

The second part presents an advection-based tracking method and its use in analyzing the contribution of ocean currents in salinity movement in BoB. We introduce a feature definition based on the geometry of advection in high salinity water representing the movement of high salinity water. We also present algorithms to track their evolution over time. This method allows us to trace the movement of the high salinity water caused by ocean currents. The method is validated via comparison with well-established observations on the flow of Summer Monsoon Current (SMC) in the BoB, including its entry from the Arabian Sea and its movement near Sri Lanka. Further, the visual analysis and tracking framework enables us to compare it with previous work and analyze the contribution of advection/ocean currents to salinity movement.

In the third part, we introduce efficient implementations of the front-based method for tracking salinity movement. We perform a scaling study over these implementations to show the robust nature of this method. We claim that it is highly scalable and present experimental results over two large-scale datasets.

The final part of the thesis introduces the front-based method as a simple filter in pyParaOcean, a visualization system built on top of Paraview supporting several tasks routinely used in the visual analysis of ocean data. We also discuss the incorporation of the parallel and fast implementation of the front-based method as a pyParaOcean filter via an interactive process.