Revealing Multimodality in Ensemble Weather Prediction

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dc.contributor.authorGalmiche, Natachaen_US
dc.contributor.authorHauser, Helwigen_US
dc.contributor.authorSpengler, Thomasen_US
dc.contributor.authorSpensberger, Clemensen_US
dc.contributor.authorBrun, Mortenen_US
dc.contributor.authorBlaser, Nelloen_US
dc.contributor.editorArchambault, Daniel and Nabney, Ian and Peltonen, Jaakkoen_US
dc.date.accessioned2021-06-12T11:28:26Z
dc.date.available2021-06-12T11:28:26Z
dc.date.issued2021
dc.description.abstractEnsemble methods are widely used to simulate complex non-linear systems and to estimate forecast uncertainty. However, visualizing and analyzing ensemble data is challenging, in particular when multimodality arises, i.e., distinct likely outcomes. We propose a graph-based approach that explores multimodality in univariate ensemble data from weather prediction. Our solution utilizes clustering and a novel concept of life span associated with each cluster. We applied our method to historical predictions of extreme weather events and illustrate that our method aids the understanding of the respective ensemble forecasts.en_US
dc.description.sectionheadersPapers
dc.description.seriesinformationMachine Learning Methods in Visualisation for Big Data
dc.identifier.doi10.2312/mlvis.20211073
dc.identifier.isbn978-3-03868-146-5
dc.identifier.pages7-11
dc.identifier.urihttps://doi.org/10.2312/mlvis.20211073
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/mlvis20211073
dc.publisherThe Eurographics Associationen_US
dc.subjectApplied computing
dc.subjectEarth and atmospheric sciences
dc.subjectHuman
dc.subjectcentered computing
dc.subjectVisual analytics
dc.subjectComputing methodologies
dc.subjectUnsupervised learning
dc.titleRevealing Multimodality in Ensemble Weather Predictionen_US
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