Topological Characterization and Uncertainty Visualization of Atmospheric Rivers

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dc.contributor.authorLan, Fangfeien_US
dc.contributor.authorGamelin, Brandien_US
dc.contributor.authorYan, Linen_US
dc.contributor.authorWang, Jialien_US
dc.contributor.authorWang, Beien_US
dc.contributor.authorGuo, Hanqien_US
dc.contributor.editorAigner, Wolfgangen_US
dc.contributor.editorArchambault, Danielen_US
dc.contributor.editorBujack, Roxanaen_US
dc.date.accessioned2024-05-21T08:17:59Z
dc.date.available2024-05-21T08:17:59Z
dc.date.issued2024
dc.description.abstractAtmospheric rivers (ARs) are long, narrow regions of water vapor in the Earth's atmosphere that transport heat and moisture from the tropics to the mid-latitudes. ARs are often associated with extreme weather events in North America and contribute significantly to water supply and flood risk. However, characterizing ARs has been a major challenge due to the lack of a universal definition and their structural variations. Existing AR detection tools (ARDTs) produce distinct AR boundaries for the same event, making the risk assessment of ARs a difficult task. Understanding these uncertainties is crucial to improving the predictability of AR impacts, including their landfall areas and associated precipitation, which could cause catastrophic flooding and landslides over the coastal regions. In this work, we develop an uncertainty visualization framework that captures boundary and interior uncertainties, i.e., structural variations, of an ensemble of ARs that arise from a set of ARDTs. We first provide a statistical overview of the AR boundaries using the contour boxplots of Whitaker et al. that highlight the structural variations of AR boundaries based on their nesting relationships. We then introduce the topological skeletons of ARs based on Morse complexes that characterize the interior variation of an ensemble of ARs. We propose an uncertainty visualization of these topological skeletons, inspired by MetroSets of Jacobson et al. that emphasizes the agreements and disagreements across the ensemble members. Through case studies and expert feedback, we demonstrate that the two approaches complement each other, and together they could facilitate an effective comparative analysis process and provide a more confident outlook on an AR's shape, area, and onshore impact.en_US
dc.description.number3
dc.description.sectionheadersScalars, Vectors, and Topology
dc.description.seriesinformationComputer Graphics Forum
dc.description.volume43
dc.identifier.doi10.1111/cgf.15084
dc.identifier.issn1467-8659
dc.identifier.pages12 pages
dc.identifier.urihttps://doi.org/10.1111/cgf.15084
dc.identifier.urihttps://diglib.eg.org/handle/10.1111/cgf15084
dc.publisherThe Eurographics Association and John Wiley & Sons Ltd.en_US
dc.rights.urihttps://creativecommons.org/licenses/by-nc/4.0/
dc.subjectCCS Concepts: Human-centered computing → Scientific visualization; Visualization application domains
dc.subjectHuman centered computing → Scientific visualization
dc.subjectVisualization application domains
dc.titleTopological Characterization and Uncertainty Visualization of Atmospheric Riversen_US
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