A Review of Geospatial Content in IEEE Visualization Publications

Yoshizumi, Alexander; Coffer, Megan M.; Collins, Elyssa L.; Gaines, Mollie D.; Gao, Xiaojie; Jones, Kate; McGregor, Ian R.; McQuillan, Katie A.; Perin, Vinicius; Tomkins, Laura M.; Worm, Thom; Tateosian, Laura

doi:10.1109/vis47514.2020.00017

Cited by 9 publications

(6 citation statements)

References 33 publications

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“…Feature-based Visualization in Meteorology Our driving science domain in this study is meteorology. Meteorological applications have frequently motivated work on visualization in recent years, overviews were provided by Rautenhaus et al [52], Azfal et al [1] and Yohizumi et al [70]. Different aspects of visualization in meteorology have been addressed, including feature-based visualization, flow visualization and 3D interactive visual analysis techniques, all relevant to the techniques discussed in our work.…”

Section: Related Workmentioning

confidence: 99%

Visualization of Model Parameter Sensitivity along Trajectories in Numerical Weather Predictions

Neuhauser¹,

Hieronymus²,

Kern³

et al. 2022

Preprint

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Fig. 1. Visual analysis of the sensitivity of a state variable to selected model parameters (red names in curve-plot overlay) along warm conveyor belt trajectories in the extratropical cyclone "Vladiana", to assess uncertainties of parameterizations in numerical weather prediction models. State variable (blue) and maximum sensitivity (red) are color coded in view-aligned bands along trajectories. Embedded curve-plot shows statistical summaries of state variables and sensitivities. Selected sensitivities and state variables (in top to bottom order) are assigned to pie charts in the 3D view in clockwise order. Pie charts show values of multiple sensitivities or state variables along trajectories at selected time steps, or (as shown in Fig. 10) aligned to the time of ascent of each trajectory. View-aligned pie charts enable an effective comparison of sensitivities on multiple trajectories. The blue color on the ground shows surface precipitation.

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Section: Related Workmentioning

confidence: 99%

Visualization of Model Parameter Sensitivity along Trajectories in Numerical Weather Predictions

Neuhauser¹,

Hieronymus²,

Kern³

et al. 2022

Preprint

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“…A summary of geospatial content by Yoshizumi et al (2020) indicates that there were 94 of 220 papers in recent IEEE VIS publications used geospatial data. A variety of VA methods and tools have been developed to visually make sense of geospatial data (Andrienko et al 2016).…”

Section: Visual Exploration Of Spatiotemporal Datamentioning

confidence: 99%

User-centered visual explorer of in-process comparison in spatiotemporal space

Dong

Ian

Liu

et al. 2022

J Vis

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We propose a user-centered visual explorer (UcVE) for progressive comparing multiple visualization units in spatiotemporal space. We create unique unit visualization with the customizable aggregated view based on the visual metaphor of flower bursts. Each visualization unit is encoded with the abstraction of spatiotemporal properties. To reduce user cognition load, UcVE allows users to visualize, save, and track in-the-process exploration results. In coordination of storage sequence and block tracking views, UcVE can facilitate comparison with multiple visualization units concurrently, selected from historical and current exploration results. UcVE offers a flexible geo-based layout, with aggregation functions and temporal views of the timeline with categorized events, to maximize the user’s exploration capabilities. Finally, we demonstrate the usefulness by using COVID-19 datasets, case studies with different user scenarios, and expert feedback. Graphical abstract

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“…Visualization approaches for meteorological analysis have been discussed widely in the literature. Comprehensive overviews have been provided by Rautenhaus et al (2018); Afzal et al (2019); Yoshizumi et al (2020). Our workflow builds upon and extends approaches to perform interactive statistical data analysis (Love et al, 2005;Potter et al, 2010;Orf et al, 2016;Meyer et al, 2021), and touches on aspects of 3-D feature-based visualization (Rautenhaus et al, 2015a;Kern et al, 2018Kern et al, , 2019Bader et al, 2019;Kappe et al, 2022;Bösiger et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar0)

Neuhauser

Hieronymus

Kern³

et al. 2023

Preprint

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Abstract. Numerical weather prediction models rely on parameterizations for subgrid-scale processes, e.g., for cloud microphysics, which are a well-known source of uncertainty in weather forecasts. Via algorithmic differentiation, which computes the sensitivities of prognostic variables to changes in model parameters, these uncertainties can be quantified. In this article, we present visual analytics solutions to analyze interactively the sensitivities of a selected prognostic variable to multiple model parameters along strongly ascending trajectories, so-called warm conveyor belt (WCB) trajectories. We propose a visual interface that enables to a) compare the values of multiple sensitivities at a single time step on multiple trajectories, b) assess the spatio-temporal relationships between sensitivities and the trajectories' shapes and locations, and c) find similarities in the temporal development of sensitivities along multiple trajectories. We demonstrate how our approach enables atmospheric scientists to interactively analyze the uncertainty in the microphysical parameterizations, and along the trajectories, with respect to the selected prognostic variable. We apply our approach to the analysis of WCB trajectories within the extratropical cyclone "Vladiana", which occurred between 22–25 September 2016 over the North Atlantic.

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A Review of Geospatial Content in IEEE Visualization Publications

Cited by 9 publications

References 33 publications

Visualization of Model Parameter Sensitivity along Trajectories in Numerical Weather Predictions

Visualization of Model Parameter Sensitivity along Trajectories in Numerical Weather Predictions

User-centered visual explorer of in-process comparison in spatiotemporal space

Visual analysis of model parameter sensitivities along warm conveyor belt trajectories using Met.3D (1.6.0-multivar0)

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