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A Visual Analysis Concept for the Validation of Geoscientific Simulation Models.

A Unger1, S Schulte, V Klemann

  • 1GFZ German Research Centre for Geosciences, Potsdam, Germany. unger@gfz-potsdam.de

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Summary
This summary is machine-generated.

Geoscientific model validation is improved with a new visual analysis concept. This approach tackles challenges with sparse, imprecise data, enhancing Earth system model understanding.

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Area of Science:

  • Earth System Science
  • Geoscientific Modeling
  • Geospatial Analysis

Background:

  • Geoscientific modeling is crucial for understanding Earth systems, requiring rigorous model validation.
  • Model validation assesses agreement between model outputs and observational data, termed 'goodness of fit'.
  • Analyzing goodness of fit is complex due to model parameterization uncertainties, spatio-temporal variations, data resolution/precision, and ambiguous correlations.

Purpose of the Study:

  • To introduce a novel visual analysis concept for geoscientific model validation.
  • To address specific challenges in validating models with sparse, unevenly distributed, and imprecise observational data.
  • To facilitate exploration of goodness of fit dependencies through interactive visualization.

Main Methods:

  • Developed a visual analysis concept with four integrated components tailored to validation challenges.
  • Implemented interactive drill-down capabilities in both model parameter space and data sample sets.
  • Applied the concept to geoscientific modeling of glacial isostatic adjustments using sea-level indicators.

Main Results:

  • The visualization concept effectively addresses the complexities of geoscientific model validation.
  • Interactive exploration allows for a deeper understanding of goodness of fit dependencies.
  • Initial use cases confirm its value as a complementary validation tool for Earth system modelers.

Conclusions:

  • The proposed visualization concept offers a valuable approach to geoscientific model validation, especially with challenging datasets.
  • It enhances the ability of Earth system modelers to assess model performance and reliability.
  • Further adoption and feedback are expected to refine its application in diverse geoscientific studies.