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    Revealing Multimodality in Ensemble Weather Prediction

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    Date
    2021
    Author
    Galmiche, Natacha ORCID
    Hauser, Helwig
    Spengler, Thomas ORCID
    Spensberger, Clemens ORCID
    Brun, Morten ORCID
    Blaser, Nello ORCID
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    Abstract
    Ensemble 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.
    BibTeX
    @inproceedings {10.2312:mlvis.20211073,
    booktitle = {Machine Learning Methods in Visualisation for Big Data},
    editor = {Archambault, Daniel and Nabney, Ian and Peltonen, Jaakko},
    title = {{Revealing Multimodality in Ensemble Weather Prediction}},
    author = {Galmiche, Natacha and Hauser, Helwig and Spengler, Thomas and Spensberger, Clemens and Brun, Morten and Blaser, Nello},
    year = {2021},
    publisher = {The Eurographics Association},
    ISBN = {978-3-03868-146-5},
    DOI = {10.2312/mlvis.20211073}
    }
    URI
    https://doi.org/10.2312/mlvis.20211073
    https://diglib.eg.org:443/handle/10.2312/mlvis20211073
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    Eurographics Association copyright © 2013 - 2022 
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    Theme by @mire NV
    System hosted at  Graz University of Technology.
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