Topology-based Feature Detection in Climate Data

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dc.contributor.authorKappe, Christopher P.en_US
dc.contributor.authorBöttinger, Michaelen_US
dc.contributor.authorLeitte, Heikeen_US
dc.contributor.editorBujack, Roxana and Feige, Kathrin and Rink, Karsten and Zeckzer, Dirken_US
dc.date.accessioned2019-06-02T18:12:46Z
dc.date.available2019-06-02T18:12:46Z
dc.date.issued2019
dc.description.abstractThe weather and climate research community needs to analyze increasingly large datasets, mostly obtained by observations or produced by simulations. Ensemble simulation techniques, which are used to capture uncertainty, add a further dimension to the multivariate time-dependent 3D data, even tightening the challenge of finding relevant information in the data for answering the respective research questions. In this paper we propose a topology-based method to support the visual analysis of climate data by detecting regions with particularly strong local minima or maxima and highlighting them with colored contours. Combined with preceding clustering of the data fields, typical spatial patterns characterizing the climate variability are detected and visualized. We demonstrate the utility of our method with a study of global temperature anomalies of a 150-years ensemble simulation consisting of 100 members.en_US
dc.description.sectionheadersWeather and Climate
dc.description.seriesinformationWorkshop on Visualisation in Environmental Sciences (EnvirVis)
dc.identifier.doi10.2312/envirvis.20191099
dc.identifier.isbn978-3-03868-086-4
dc.identifier.pages9-16
dc.identifier.urihttps://doi.org/10.2312/envirvis.20191099
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/envirvis20191099
dc.publisherThe Eurographics Associationen_US
dc.subjectHuman
dc.subjectcentered computing
dc.subjectGeographic visualization
dc.subjectApplied computing
dc.subjectEnvironmental sciences
dc.titleTopology-based Feature Detection in Climate Dataen_US
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