EnvirVis2021

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https://diglib.eg.org/handle/10.2312/2633051

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Browsing EnvirVis2021 by Subject "Human"

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    Probabilistic Principal Component Analysis Guided Spatial Partitioning of Multivariate Ocean Biogeochemistry Data
    (The Eurographics Association, 2021) Hazarika, Subhashis; Biswas, Ayan; Lawrence, Earl; Wolfram, Philip J.; Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
    Farm-scale cultivation of macroalgae for the production of renewable biofuel depends on complex ocean hydrodynamics and also on the availability of different essential nutrients. To better understand such conditions that are conducive for the growth of macroalgae, scientists implement large-scale computational models, simulating several physical variables (essential nutrients, and other chemical compounds), relevant to study oceanic biogeochemistry (BGC). Visualizing and analysing the different physical variables and their inter-variable relationships across the spatial domain is crucial to form concrete understanding of the underlying physical phenomenon. To facilitate such multivariate analyses for large-scale simulation data, a popular and effective way is to decompose the spatial domain into smaller local regions based on the variable relationships. However, spatial decomposition of multivariate data is not trivial. In this paper, we propose a novel multivariate spatial data partitioning approach using probabilistic principal component analysis. We also perform detailed study of other prospective multivariate partitioning schemes and compare them with our proposed method. To demonstrate the efficacy of our approach, we studied nutrient relationships across different regions of the ocean using a high-resolution Ocean BCG simulation data set, which comprises of multiple physical variables essential for macroalgae cultivation. We further validate the results of our analyses by getting feedback from domain experts in the field of ocean sciences.
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    A Winding Angle Framework for Tracking and Exploring Eddy Transport in Oceanic Ensemble Simulations
    (The Eurographics Association, 2021) Friederici, Anke; Falk, Martin; Hotz, Ingrid; Dutta, Soumya and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirk
    Oceanic eddies, which are highly mass-coherent vortices traveling through the earth's waters, are of special interest for their mixing properties. Therefore, large-scale ensemble simulations are performed to approximate their possible evolution. Analyzing their development and transport behavior requires a stable extraction of both their shape and properties of water masses within. We present a framework for extracting the time series of full 3D eddy geometries based on an winding angle criterion. Our analysis tools enables users to explore the results in-depth by linking extracted volumes to extensive statistics collected across several ensemble members. The methods are showcased on an ensemble simulation of the Red Sea. We show that our extraction produces stable and coherent geometries even for highly irregular eddies in the Red Sea. These capabilities are utilized to evaluate the stability of our method with respect to variations of user-defined parameters. Feedback gathered from domain experts was very positive and indicates that our methods will be considered for newly simulated, even larger data sets.