pyParaOcean, A System for Visual Analysis of Ocean Data: Case study: Bay of Bengal
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Authors:
(1) Toshit Jain, Indian Institute of Science Bangalore, India;
(2) Varun Singh, Indian Institute of Science Bangalore, India;
(3) Vijay Kumar Boda, Indian Institute of Science Bangalore, India;
(4) Upkar Singh, Indian Institute of Science Bangalore, India;
(5) Ingrid Hotz, Indian Institute of Science Bangalore, India and Department of Science and Technology (ITN), Linköping University, Norrköping, Sweden;
(6) P. N. Vinayachandran, Indian Institute of Science Bangalore, India;
(7) Vijay Natarajan, Indian Institute of Science Bangalore, India.
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Table of Links
Abstract and Intro
Ocean data
pyParaOcean: Architecture
pyParaOcean: Functionalities
Case study: Bay of Bengal
Conclusion, Acknowledgments, and References
5. Case study: Bay of Bengal
The Summer Monsoon Current (SMC) is a prominent feature of Indian ocean circulation and the SMC flows around Sri Lanka to flow into the Bay of Bengal. We use pyParaOcean to study different phenomena in the Bay of Bengal, particularly during the monsoon.
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Eddies. Figure 5 is a rough schematic of the major currents and eddies in the Bay during the monsoon season. A large anticyclonic eddy (AE) located to the right of the SMC and a cyclonic eddy known as the the Sri Lanka Dome (SLD) to its left [VY98] are regular features in this region during summer. The AE has a diameter of about 500 km, located to the southeast off the coast of Sri Lanka, and is characterized by intense downwelling inside owing to its anticyclonic circulation. [VY98] proposed that the AE is formed by the interaction of the SMC and the incoming Rossby waves from Sumatra. The timeline of appearance and disappearance of the AE was documented in later work [VCMN04]. The AE starts forming in June, develops into its circular shape in July, and weakens in August, as shown in Figure 6 and the accompanying video.
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Salinity transport. pyParaOcean serves as an efficient tool to analyze the effects of AE on the Bay of Bengal. Streamlines and pathlines offer visualization of circulation associated with the AE and its movement in the ocean. The field lines may be overlaid on a volume rendering of a scalar to visualize the transport caused by the eddy. Figure 7 and the accompanying video show the streamlines overlaid on a salinity volume rendering at different time steps to show the role of the AE in transport of salt. The movement of high salinity water from the Arabian sea by the SMC into the Bay of Bengal and its recirculation by the AE is well captured in this representation. Tracking surface fronts of high salinity water and highlighting the long-lived tracks helps capture an overview of significant salinity movement in the region. We observe a track that moves towards the coast of India, see Figure 4.
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Downwelling. Figure 8 and the accompanying video show the use of the depth profile filter to visualize the depression of the 27◦ isotherm by the AE. The anticyclonic nature of the eddy causes a downwelling inside the eddy and pushes the relatively warmer water downward. The parallel coordinates view shows changes in temperature, salinity, and speed in the water column caused by the arrival of the eddy at the point of interest.
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Experience and performance. This case study was conducted in collaboration with an oceanographer coauthor. Several observations on phenomena such as the SLD and the movement of high salinity water could be made using pyParaOcean. While our oceanographer collaborators typically use tools such as pyFerret for 2D analysis, they found the capability of pyParaOcean to be very useful. After this initial satisfying experience, we plan to work together on the study of higher resolution model output using pyParaOcean. The surface front tracking and eddy detection filters take a few minutes, while all other filters take 1-2 seconds or less. All the experiments were run on a workstation with an 8 core AMDEPYC 7262 @ 3.2 GHz CPU with 512 GB main memory and NVIDIA RTX A4000 (16 GB) GPU. The surface front computation is parallelized using the python multiprocessing library but there is scope for further improvement in runtime. The eddy detection and visualization filter can also be optimized by parallelizing some of the computation. We plan to take this up in the future.
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This paper is available on arxiv under CC 4.0 license.
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