pyParaOcean, A System for Visual Analysis of Ocean Data: Abstract and Intro

30 May 2024


(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.


Visual analysis is well adopted within the field of oceanography for the analysis of model simulations, detection of different phenomena and events, and tracking of dynamic processes. With increasing data sizes and the availability of multivariate dynamic data, there is a growing need for scalable and extensible tools for visualization and interactive exploration. We describe pyParaOcean, a visualization system that supports several tasks routinely used in the visual analysis of ocean data. The system is available as a plugin to Paraview and is hence able to leverage its distributed computing capabilities and its rich set of generic analysis and visualization functionalities. pyParaOcean provides modules to support different visual analysis tasks specific to ocean data, such as eddy identification and salinity movement tracking. These modules are available as Paraview filters and this seamless integration results in a system that is easy to install and use. A case study on the Bay of Bengal illustrates the utility of the system for the study of ocean phenomena and processes.

1. Introduction

Understanding ocean data is paramount to predicting extreme weather events such as hurricanes and tsunamis, better understanding of planetary scale processes like global warming, and sustainably managing and preserving ocean resources and its marine life. Visualizing ocean data is challenging due to the presence of multiple fields and parameters that change with time. Ocean currents are undeniably the biggest factor that maintain the heat balance of the ocean-atmosphere system and affect the movement of minerals and salt. Mesoscale eddies, that span from tens to hundreds of kilometers in diameter and have a lifespan that can range from days to months [RR10], are ubiquitous in the oceans. They play a big role in transporting heat and mass within the oceans [McW08]. They also have a big impact on the ecology of the ocean and on the biogeochemical processes [MJD∗ 99,BNBD∗ 07].

With the strides in collection and generation of ocean data [FD06, Ros89], there is a need for tools that support effective visualization of the data, and are scalable to keep pace with the ever expanding resolutions and sizes of ocean datasets.

Visualization in oceanography is a challenging area of research due to the rapidly increasing size of data, the heterogeneity and multivariate nature of the data, and the inherent complexity of the ocean phenomena. The use of general purpose analysis and visualization software such as Matlab, Tecplot, AVS, and Paraview is prevalent in the community. However, oceanographers often use tools developed specifically for ocean data, such as Ferret [Fer23], pyFerret [pyF23], and Copernicus MyOcean [myO23]. These specialized tools provide multiple functionalities and produce 2D views of the data.

A few software frameworks developed within the visualization community provide 2D and 3D data visualization capabilities. COVE [GSK∗ 08] is a collaborative ocean visualization environment that supports interactive analysis of ocean models over the web. RedSeaAtlas [AGT∗ 19] supports the selection of regions in a 2D map and provides exploratory views of winds, waves, tides, chlorophyll, etc. over the Red Sea. OceanPaths [NL15] is a multivariate data visualization tool that computes pathways tracing ocean currents and supports the plotting of different high-dimensional data along the pathways. This enables the study of correlations between different oceanographic features.

An oceanographer’s analysis workflow includes a few common tasks [GSK∗ 08] such as inspection of temperature and salinity distributions and vertical cross sections, compare recently measured salinity data against model data, inspect and analyze current vorticities and circulation based on flow data, and analyze extreme events. Isosurfaces and volume rendering are natural choices for visualization of 3D temperature and salinity distributions [DAN12, PBI04]. However, visualization of the dynamically changing distributions is a challenge. VAPOR [LJP∗ 19] is one of the few tools that provides efficient 3D visualization for oceanography and atmospheric science applications. The VAPOR data collection (VDC) data model supports interactive visual analysis of large data sizes on modern GPUs and commodity hardware.

Xie et al. [XLWD19] and Afzal et al. [AHG∗ 19] present surveys of visual analysis methods and tools developed for ocean data. Xie et al. classify the visual analysis tasks into four categories, namely study of different environmental variables, ocean phenomena identification and tracking, discovery of patterns and correlations, and visualization of ensembles and uncertainty. Further, they identify different opportunities and unexplored areas for future work including efficient and scalable methods for data processing and management, identification of features at multiple scales, and immersive platforms. While we recognize the availability of several methods for oceanography visualization, we note that they are often standalone solutions. We aim to leverage the extensive advancement in visualization technology as implemented in Paraview while providing functionalities and views that are specific to ocean data.

1.2. Contributions

We present pyParaOcean, a system for interactive exploration and visual analysis of ocean data. The system leverages the power of Paraview [AGL05] to enable scalable visualizations of data available from ocean models while supporting a multitude of tasks and functionalities that are specialized for oceanography. The visualization capabilities of pyParaOcean are available via a seamless integration into Paraview using plugins. Key features of the system include

• 3D field visualization to study salinity and temperature distribution with support to display and explore dynamically evolving isovolumes.

• Ocean current visualization with a support for different seeding strategies for streamlines and pathlines.

• Vertical section views and parallel coordinates plot that support interactive selection and slicing of the data.

• Identification and tracking of salinity movement via extraction of surface fronts. • Visualization and tracking of eddy features.

• An extensible design that supports incorporation of new functionalities as filters in Paraview.

We present the results of an exploration of the Bay of Bengal, performed in collaboration with an oceanographer, as a case study to demonstrate the utility of the system.

This paper is available on arxiv under CC 4.0 license.