Feature identification in time-indexed model output


Autoři: Justin Shaw aff001;  Marek Stastna aff001
Působiště autorů: Department of Applied Mathematics, University of Waterloo, Waterloo, Ontario, Canada aff001
Vyšlo v časopise: PLoS ONE 14(12)
Kategorie: Research Article
doi: 10.1371/journal.pone.0225439

Souhrn

We present a method for identifying features (time periods of interest) in data sets consisting of time-indexed model output. The method is used as a diagnostic to quickly focus the attention on a subset of the data before further analysis methods are applied. Mathematically, the infinity norm errors of empirical orthogonal function (EOF) reconstructions are calculated for each time output. The result is an EOF reconstruction error map which clearly identifies features as changes in the error structure over time. The ubiquity of EOF-type methods in a wide range of disciplines reduces barriers to comprehension and implementation of the method. We apply the error map method to three different Computational Fluid Dynamics (CFD) data sets as examples: the development of a spontaneous instability in a large amplitude internal solitary wave, an internal wave interacting with a density profile change, and the collision of two waves of different vertical mode. In all cases the EOF error map method identifies relevant features which are worthy of further study.

Klíčová slova:

Built structures – Covariance – Eigenvalues – El Niño-Southern Oscillation – Fluid dynamics – Fluid flow – principal component analysis – Singular value decomposition


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Článek vyšel v časopise

PLOS One


2019 Číslo 12