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An Interactive Framework for Visualization of Weather Forecast Ensembles.

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    This study introduces an interactive framework to improve weather forecast uncertainty visualization using enhanced spaghetti plots. The new methods, including mode plots, offer clearer analysis of ensemble weather prediction data.

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    Area of Science:

    • Meteorology and Atmospheric Sciences
    • Computer Science and Visualization

    Background:

    • Numerical Weather Prediction (NWP) ensembles are crucial for assessing weather forecast uncertainty.
    • Conventional spaghetti plots visualize ensemble isocontours but suffer from clutter and interpretation challenges, especially with large ensembles.
    • Existing methods require careful isovalue selection and struggle with complex ensemble isocontour topology.

    Purpose of the Study:

    • To develop an interactive framework that enhances the utility of spaghetti plots for weather forecast ensemble analysis.
    • To provide tools for improved isovalue selection, interactive exploration of isocontour variability, and sub-region re-analysis.
    • To introduce novel visualizations, the mode plot and simplified spaghetti plot, for clearer uncertainty representation.

    Main Methods:

    • Developed a framework based on high-density clustering to represent ensemble isocontour density as a data hierarchy.
    • Generalized high-density clustering for isocontours and proposed a bandwidth selection method for density estimation.
    • Introduced interactive mode plots and simplified spaghetti plots for summarizing and exploring ensemble isocontour distributions and variability.

    Main Results:

    • The proposed framework enables a complete visual exploration of ensemble isocontours, including selection and interactive analysis.
    • Novel visualizations like the mode plot effectively summarize ensemble isocontour distributions and facilitate subset selection.
    • The simplified spaghetti plot aids in selecting informative isovalues, and interactive sub-region analysis addresses spatial variability.

    Conclusions:

    • The interactive framework significantly improves upon traditional spaghetti plots for analyzing weather forecast ensemble uncertainty.
    • The novel visualizations and interactive features offer a more comprehensive and interpretable approach to ensemble data analysis.
    • The approach demonstrates advantages over existing tools for visualizing and understanding complex ensemble weather prediction data.