Statistical Rendering for Visualization of Red Sea Eddy Simulation Data

Analyzing the effects of ocean eddies is important in oceanology for gaining insights into transport of energy and biogeochemical particles. We present an application of statistical visualization algorithms for the analysis of the Red Sea eddy simulation ensemble. Specifically, we demonstrate the applications of statistical volume rendering and statistical Morse complex summary maps to a velocity magnitude field for studying the eddy positions in the flow dataset. In statistical volume rendering, we model per-voxel data uncertainty using noise models, such as parametric and nonparametric, and study the propagation of uncertainty into the volume rendering pipeline. In the statistical Morse complex summary maps, we derive histograms charactering uncertainty of gradient flow destinations to understand Morse complex topological variations across the ensemble. We demonstrate the utility of our statistical visualizations for an effective analysis of the potential eddy positions and their spatial uncertainty.

[1]  Herbert Edelsbrunner,et al.  Hierarchical morse complexes for piecewise linear 2-manifolds , 2001, SCG '01.

[2]  Herbert Edelsbrunner,et al.  Topological Persistence and Simplification , 2000, Proceedings 41st Annual Symposium on Foundations of Computer Science.

[3]  J. Hoar,et al.  A MITgcm/DART ocean analysis and prediction system with application to the Gulf of Mexico [poster] , 2008 .

[4]  Valerio Pascucci,et al.  Gaussian mixture model based volume visualization , 2012, IEEE Symposium on Large Data Analysis and Visualization (LDAV).

[5]  Joseph Salmon,et al.  Mandatory Critical Points of 2D Uncertain Scalar Fields , 2014, Comput. Graph. Forum.

[6]  Alireza Entezari,et al.  A Statistical Direct Volume Rendering Framework for Visualization of Uncertain Data , 2017, IEEE Transactions on Visualization and Computer Graphics.

[7]  Junpeng Wang,et al.  Visualization and Visual Analysis of Ensemble Data: A Survey , 2019, IEEE Transactions on Visualization and Computer Graphics.

[8]  Ibrahim Hoteit,et al.  Impact of Atmospheric and Model Physics Perturbations on a High‐Resolution Ensemble Data Assimilation System of the Red Sea , 2020, Journal of Geophysical Research: Oceans.

[9]  Chris R. Johnson,et al.  Direct Volume Rendering with Nonparametric Models of Uncertainty , 2020, IEEE Transactions on Visualization and Computer Graphics.

[10]  Valerio Pascucci,et al.  Uncertainty Visualization of 2D Morse Complex Ensembles Using Statistical Summary Maps , 2019, IEEE Transactions on Visualization and Computer Graphics.