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Feature identification in time-indexed model output

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 error...

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Detalles Bibliográficos
Autores principales: Shaw, Justin, Stastna, Marek
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6892550/
https://www.ncbi.nlm.nih.gov/pubmed/31800624
http://dx.doi.org/10.1371/journal.pone.0225439
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author Shaw, Justin
Stastna, Marek
author_facet Shaw, Justin
Stastna, Marek
author_sort Shaw, Justin
collection PubMed
description 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.
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spelling pubmed-68925502019-12-14 Feature identification in time-indexed model output Shaw, Justin Stastna, Marek PLoS One Research Article 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. Public Library of Science 2019-12-04 /pmc/articles/PMC6892550/ /pubmed/31800624 http://dx.doi.org/10.1371/journal.pone.0225439 Text en © 2019 Shaw, Stastna http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Shaw, Justin
Stastna, Marek
Feature identification in time-indexed model output
title Feature identification in time-indexed model output
title_full Feature identification in time-indexed model output
title_fullStr Feature identification in time-indexed model output
title_full_unstemmed Feature identification in time-indexed model output
title_short Feature identification in time-indexed model output
title_sort feature identification in time-indexed model output
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6892550/
https://www.ncbi.nlm.nih.gov/pubmed/31800624
http://dx.doi.org/10.1371/journal.pone.0225439
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