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dc.contributor.author
Pulido, Manuel Arturo
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dc.contributor.author
Rosa, Santiago
dc.contributor.author
van Leeuwen, Peter Jan
dc.date.available
2021-06-08T11:54:01Z
dc.date.issued
2019
dc.identifier.citation
Information flow in multi-scale dynamical systems using ordinal symbolic analysis; EGU General Assembly 2019; Vinna; Austria; 2019; 1-1
dc.identifier.issn
1029-7006
dc.identifier.uri
http://hdl.handle.net/11336/133384
dc.description.abstract
In this work, information flow quantifiers between variables of multi-scale dynamical systems simulating atmospheric processes are evaluated in non-linear and non-gaussian statistical regimes. The atmosphere is a spatially extended, highly non-linear dynamical system with complex interactions between the different dynamical scales, as well as between the different physical processes involved in it. We evaluate whether conditional mutual information and transfer entropy are able to detect and quantify causal interactions between large-scale and small-scale dynamics. As simple prototype models of these atmospheric interactions, we use a two-scale Lorenz 96 model and a two dimensional barotropic model. In order to obtain the information quantifiers, temporal series from the experiments are examined with ordinal symbolic analysis using the Band-Pompe symbolic reduction in the data signal and using the Kraskov-Stogbauer-Grassberger method to estimate mutual information using k-nearest neighbors. Comparing different experiments, we show that the interactions between small-scale variables and large-scale variables may introduce spatial long-range information flows. We also found that conditional mutual information is able to detect energy and enstrophy cascades in the barotropic model. Ordinal symbolic analysis allows us to obtain robust measures and may be efficiently applied to long temporal series with correlations between several processes. We conclude that information measures are useful tools to establish observational information flows in the atmosphere. These tools may be helpful to quantify the role of small - scale processes and constraining stochastic parameterizations.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Copernicus Publications
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by/2.5/ar/
dc.subject
TRANSFER ENTROPY
dc.subject
MUTUAL INFORMATION
dc.subject
SHANNON INFORMATION
dc.subject.classification
Meteorología y Ciencias Atmosféricas
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dc.subject.classification
Ciencias de la Tierra y relacionadas con el Medio Ambiente
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dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
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dc.title
Information flow in multi-scale dynamical systems using ordinal symbolic analysis
dc.type
info:eu-repo/semantics/publishedVersion
dc.type
info:eu-repo/semantics/conferenceObject
dc.type
info:ar-repo/semantics/documento de conferencia
dc.date.updated
2021-04-27T13:36:21Z
dc.identifier.eissn
1607-7962
dc.journal.pagination
1-1
dc.journal.pais
Austria
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dc.journal.ciudad
Vienna
dc.description.fil
Fil: Pulido, Manuel Arturo. University of Reading; Reino Unido. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Modelado e Innovación Tecnológica. Universidad Nacional del Nordeste. Facultad de Ciencias Exactas Naturales y Agrimensura. Instituto de Modelado e Innovación Tecnológica; Argentina
dc.description.fil
Fil: Rosa, Santiago. Universidad Nacional de Córdoba; Argentina
dc.description.fil
Fil: van Leeuwen, Peter Jan. University of Reading; Reino Unido
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://meetingorganizer.copernicus.org/EGU2019/EGU2019-5228.pdf
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://meetingorganizer.copernicus.org/EGU2019/sessionprogramme
dc.conicet.rol
Autor
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dc.conicet.rol
Autor
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dc.conicet.rol
Autor
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dc.coverage
Internacional
dc.type.subtype
Congreso
dc.description.nombreEvento
EGU General Assembly 2019
dc.date.evento
2019-04-07
dc.description.ciudadEvento
Vinna
dc.description.paisEvento
Austria
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dc.type.publicacion
Journal
dc.description.institucionOrganizadora
European Geosciences Union
dc.source.revista
Geophysical Research Abstracts
dc.date.eventoHasta
2019-04-12
dc.type
Congreso
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