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An R-Based Landscape Validation of a Competing Risk Model
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A Probability Density-Based Visual Analytics Approach to Forecast Bias Calibration.

Renpei Huang, Quan Li, Li Chen

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    |September 18, 2020
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    This summary is machine-generated.

    Numerical weather prediction (NWP) biases are improved by a new method that analyzes spatiotemporal correlations. This approach extracts bias patterns to enhance weather forecast calibration.

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    Area of Science:

    • Atmospheric Science
    • Data Science
    • Meteorology

    Background:

    • Numerical weather prediction (NWP) models are subject to inherent biases due to the complex, chaotic nature of atmospheric systems.
    • Accurate bias identification and calibration are essential for reliable weather forecasting.
    • Existing post-processing methods often neglect the spatiotemporal correlations of forecast biases, limiting their effectiveness.

    Purpose of the Study:

    • To develop a novel approach for extracting and analyzing spatiotemporal bias patterns in NWP.
    • To introduce BicaVis, a visual analytics system for assisting experts in weather forecast bias calibration.
    • To improve the efficacy of bias calibration by considering spatial and temporal bias correlations.

    Main Methods:

    • A new bias pattern extraction method using forecasting-observation probability density from historical datasets.
    • Merging historical forecasting and observation data to identify bias patterns within a defined spatiotemporal scope.
    • Developing the BicaVis system to automatically segment regions with similar bias patterns and aid in calibration curve generation.

    Main Results:

    • The proposed approach successfully extracts and fuses spatiotemporal bias patterns.
    • BicaVis effectively divides regions with similar bias characteristics.
    • Case studies using real-world reanalysis datasets demonstrated the approach's effectiveness.

    Conclusions:

    • The novel bias pattern extraction method and BicaVis system offer a significant advancement in NWP bias calibration.
    • Considering spatiotemporal bias correlations enhances the accuracy and reliability of weather forecasts.
    • Domain expert feedback validated the practical utility and efficacy of the proposed visual analytics system.