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High-Performance Visualization for Ocean Modeling

Ali, W.H., Y. Gao, C. Foucart, M. Doshi, C. Mirabito, P.J. Haley, and P.F.J. Lermusiaux, 2022. High-Performance Visualization for Ocean Modeling. In: OCEANS '22 IEEE/MTS Hampton Roads, 17–20 October 2022, pp. 1–10. doi:10.1109/OCEANS47191.2022.9977075

Real-time sea experiments often involve large computational costs and software development associated with running numerical ocean simulations. Effective visualization tools that interpret the results of these simulations are therefore a necessity, and must overcome the challenges of plotting large, high-resolution, three-dimensional, time-dependent, and probabilistic ocean fields and associated quantities in real-time. Although disparate visualization tools aimed at ocean forecasting exist, a complete, integrated visualization suite that is efficient, interactive, and has 3D capabilities is still needed. In this work, we present the MSEAS high-performance visualization suite for real-time sea experiments. It processes multidisciplinary oceanographic fields in a computationally efficient manner and creates easy-to-use, portable, and interactive visualizations. The suite includes static visualization tools based on NCAR Graphics and MATLAB; the interactive web-based tool 2DSeaVizKit built using leaflet and D3.js for interactive 2D visualization on the world map; and 3DSeaVizKit, a browser-based, interactive 3D visualization tool built using Plotly and WebGL for exploratory 3D analysis of ocean forecasts. It can provide standard 2D cross-sections for scalar-valued data; quiver plots, pathlines, and streamtubes for vector-valued data; Lagrangian products (such as trajectories, Lagrangian Coherent Structures, etc.); isosurfaces for 3D data; and an interactive graphical user interface for selecting the quantities, times, and sub-domains of interest. We showcase applications of the visualization suite during three recent exercises that took place in the Gulf of Mexico, the Clarion-Clipperton Fracture Zone in the Pacific Ocean, and the Balearic sea.

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SeaVizKit: Interactive Maps for Ocean Visualization

Ali, W.H., M.H. Mirhi, A. Gupta, C.S. Kulkarni, C. Foucart, M.M. Doshi, D.N. Subramani, C. Mirabito, P.J. Haley, Jr., and P.F.J. Lermusiaux, 2019. SeaVizKit: Interactive Maps for Ocean Visualization. In: OCEANS '19 MTS/IEEE Seattle, 27-31 October 2019, doi:10.23919/OCEANS40490.2019.8962794

With the increasing availability of high-resolution comprehensive spatio-temporal ocean models and observation systems, ocean data visualization has become ubiquitous. This is due to the major impact of ocean products on disaster management, shipping, fisheries, autonomy, coastal operations, and scientific studies. Yet, there are several challenges for effective communication of data through visualization techniques. Specifically, ocean data is multivariate (e.g. temperature, salinity, velocity, etc.), is available for multiple depths and multiple time instants, and contains uncertainties, all of which leads to large, multi-dimensional datasets. Thus, it is necessary to have an interactive multiscale multivariate visualization tool that can assist scientists, engineers, policy makers, and the public in making insights from big data produced by ocean predictions and observations. In this work, we present a 3D (spatial) + 1 (temporal) multi-resolution multivariate visualization tool that produces interactive, dynamic, fast and portable ocean maps.

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Clustering of Massive Ensemble of Vehicle Trajectories in Strong, Dynamic and Uncertain Ocean Flows

Dutt, A., D.N. Subramani, C.S. Kulkarni, and P.F.J. Lermusiaux, 2018. Clustering of Massive Ensemble of Vehicle Trajectories in Strong, Dynamic and Uncertain Ocean Flows. In: Oceans '18 MTS/IEEE Charleston, 22-25 October 2018. doi:10.1109/oceans.2018.8604634

Recent advances in probabilistic forecasting of regional ocean dynamics, and stochastic optimal path planning with massive ensembles motivate principled analysis of their large datasets. Specifically, stochastic time-optimal path planning in strong, dynamic and uncertain ocean flows produces a massive dataset of the stochastic distribution of exact timeoptimal trajectories. To synthesize such big data and draw insights, we apply machine learning and data mining algorithms. Particularly, clustering of the time-optimal trajectories is important to describe their PDFs, identify representative paths, and compute and optimize risk of following these paths. In the present paper, we explore the use of hierarchical clustering algorithms along with a dissimilarity matrix computed from the pairwise discrete Frechet distance between all the optimal trajectories. We apply the algorithms to two datasets of massive ensembles of vehicle trajectories in a stochastic flow past a circular island and stochastic wind driven double gyre flow. These paths are computed by solving our dynamically orthogonal level set equations. Hierarchical clustering is applied to the two datasets, and results are qualitatively and quantitatively analyzed.

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Visualizing scalar volumetric data with uncertainty

Djurcilov, S., K. Kim, P.F.J. Lermusiaux and A. Pang, 2002. Visualizing scalar volumetric data with uncertainty. Computers and Graphics, 26 (2): 239-248.

Increasingly, more importance is placed on the uncertainty information of data being displayed. This paper focuses on techniques for visualizing 3D scalar data sets with corresponding uncertainty information at each point which is also representedas a scalar value. In Djurcilov (in: D. Ebert, J.M. Favre, R. Peikert (Eds.), Data Visualization 2001, Springer, Berlin, 2001), we presentedtwo general methods (inline DVR approach anda post-processing approach) for carrying out this task. The first methodinvolves incorporating the uncertainty information directly into the volume rendering equation. The second method involves post-processing information of volume rendered images to composite uncertainty information.
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Volume rendering data with uncertainty information

Djurcilov, S., K. Kim, P.F.J. Lermusiaux and A. Pang, 2001. Volume rendering data with uncertainty information. In "Data visualization", Joint Eurographics - IEEE TCVG Symposium on Visualization, D. Ebert, J. M. Favre and R. Peikert (Eds.), Springer-Verlag. pp. 243-252, 355-356.

This paper explores two general methods for incorporating volumetric uncertainty information in direct volume rendering. The goal is to produce volume rendered images that depict regions of high (or low) uncertainty in the data. The first method involves incorporating the uncertainty information directly into the volume rendering equation. The second method involves post-processing information of volume rendered images to composite uncertainty information. We present some initial findings on what mappings provide qualitatively satisfactory results and what mappings do not. Results are considered satisfactory if the user can identify regions of high or low uncertainty in the rendered image. We also discuss the advantages and disadvantages of both approaches.
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