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Visual Trends Analysis in Time-Varying Ensembles.

Harald Obermaier, Kevin Bensema, Kenneth I Joy

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    This study introduces a novel flow-graph system for visually analyzing trends in time-varying ensemble data. The framework links trend analysis to parameter and ensemble spaces, aiding simulation design.

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    Area of Science:

    • Data Visualization
    • Scientific Computing
    • Ensemble Data Analysis

    Background:

    • Effective visualization and analysis are crucial for extracting features from ensemble data.
    • Trends, representing persistent patterns in time-varying datasets, are key features in ensemble analysis.

    Purpose of the Study:

    • To develop an interactive framework for the visual analysis of trends in time-varying ensemble datasets.
    • To enable detailed examination of trends and their correlation with input parameter properties.

    Main Methods:

    • A flow-graph representation is developed as the core of the analysis system.
    • The flow-graph is interactively linked to ensemble parameter-space and the ensemble data itself.

    Main Results:

    • The framework facilitates the discovery of trends within complex ensemble data.
    • Demonstrated utility in benchmark datasets for understanding simulation behavior.

    Conclusions:

    • The proposed trends analysis framework supports goal-driven design of time-varying simulations.
    • Visual analysis of trends enhances the understanding of ensemble data properties and correlations.