Galmiche, NatachaHauser, HelwigSpengler, ThomasSpensberger, ClemensBrun, MortenBlaser, NelloArchambault, Daniel and Nabney, Ian and Peltonen, Jaakko2021-06-122021-06-122021978-3-03868-146-5https://doi.org/10.2312/mlvis.20211073https://diglib.eg.org:443/handle/10.2312/mlvis20211073Ensemble 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.Applied computingEarth and atmospheric sciencesHumancentered computingVisual analyticsComputing methodologiesUnsupervised learning10.2312/mlvis.202110737-11