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Feature identification in time-indexed model output.

Justin Shaw¹, Marek Stastna¹

¹Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada.

|December 5, 2019

Summary

This summary is machine-generated.

This study introduces a new method using Empirical Orthogonal Function (EOF) reconstruction errors to identify key features in time-indexed model data. This diagnostic tool quickly highlights important time periods for further analysis in complex datasets.

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Area of Science:

Fluid Dynamics
Data Analysis
Geophysics

Background:

Empirical Orthogonal Functions (EOFs) are widely used for analyzing time-indexed data across various scientific disciplines.
Identifying significant features or time periods of interest in large datasets can be challenging and time-consuming.
Existing methods may require extensive computational resources or specialized knowledge, limiting accessibility.

Purpose of the Study:

To present a novel, computationally efficient method for identifying salient features in time-indexed model output data.
To provide a diagnostic tool that quickly focuses attention on critical subsets of data for further in-depth analysis.
To demonstrate the method's applicability and effectiveness across diverse scientific domains, particularly in Computational Fluid Dynamics (CFD).

Main Methods:

The core method involves calculating the infinity norm errors of Empirical Orthogonal Function (EOF) reconstructions for each time step of the model output.
These errors are visualized as an 'EOF reconstruction error map,' highlighting temporal variations in the error structure.
The method is applied to three distinct CFD datasets involving internal solitary waves and wave collisions.

Main Results:

The EOF reconstruction error map successfully identified significant features and time periods of interest in all tested CFD datasets.
Changes in the error structure over time clearly demarcated events such as instability development and wave interactions.
The method proved effective in pinpointing phenomena worthy of further detailed investigation.

Conclusions:

The EOF error map method offers a robust and accessible approach for feature identification in time-indexed datasets.
Its broad applicability, stemming from the ubiquity of EOF methods, lowers the barrier for implementation across disciplines.
This diagnostic tool enhances the efficiency of analyzing complex model outputs, particularly in fluid dynamics research.