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    Revealing Multimodality in Ensemble Weather Prediction (Unknown language)

    In: Machine Learning Methods in Visualisation for Big Data ; 7-11  ; 2021

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    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.

    Table of contents conference proceedings

    The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

    1
    Controllably Sparse Perturbations of Robust Classifiers for Explaining Predictions and Probing Learned Concepts
    Roberts, Jay / Tsiligkaridis, Theodoros et al. | 2021
    7
    Revealing Multimodality in Ensemble Weather Prediction
    Galmiche, Natacha / Hauser, Helwig / Spengler, Thomas / Spensberger, Clemens / Brun, Morten / Blaser, Nello et al. | 2021

    How to get this title?

    Check access
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    Commercial Copyright fee: €14.50 Basic fee: €4.00 Total price: €18.50
    Academic Copyright fee: €4.50 Basic fee: €2.00 Total price: €6.50

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