Uncertainty-aware Detection and Visualization of Ocean Eddies in Ensemble Flow Fields - A Case Study of the Red Sea

Raith, Felix; Scheuermann, Gerik; Gillmann, Christina

doi:10.2312/envirvis.20211080

Cited by 1 publication

(2 citation statements)

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“…The position of the overlaid coordinate system is irrelevant, that is, positions, distances, and sizes in one visualization do not directly translate to the other. An example is overlaying glyphs [ RSG21 ]. While the space depicted in the overlaid graphics in Figure 16a is the same as in the selected region of interest underneath, the offset and repetition make the approach different from Superposition.…”

Section: Input/output Visualizationmentioning

confidence: 99%

“…Raith et al. [ RSG21 ] show uncertainty glyphs at locations where ocean eddies were detected ( output). Glyphs depict whether the uncertainty source is time (number of time steps without Eddie), environmental conditions (e.g., Eddie detected only at certain water temperature), or the parameter setting of the detection algorithm.…”

Section: Input/output Visualizationmentioning

confidence: 99%

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Visual Parameter Space Exploration in Time and Space

Piccolotto

Bögl

Miksch

2023

Computer Graphics Forum

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Computational models, such as simulations, are central to a wide range of fields in science and industry. Those models take input parameters and produce some output. To fully exploit their utility, relations between parameters and outputs must be understood. These include, for example, which parameter setting produces the best result (optimization) or which ranges of parameter settings produce a wide variety of results (sensitivity). Such tasks are often difficult to achieve for various reasons, for example, the size of the parameter space, and supported with visual analytics. In this paper, we survey visual parameter space exploration (VPSE) systems involving spatial and temporal data. We focus on interactive visualizations and user interfaces. Through thematic analysis of the surveyed papers, we identify common workflow steps and approaches to support them. We also identify topics for future work that will help enable VPSE on a greater variety of computational models.

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Section: Input/output Visualizationmentioning

confidence: 99%

Section: Input/output Visualizationmentioning

confidence: 99%