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dc.contributor.author
Héas, Patrick
dc.contributor.author
Mémin, Etienne
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Heitz, Dominique
dc.contributor.author
Mininni, Pablo Daniel
dc.date.available
2018-08-15T17:41:23Z
dc.date.issued
2012-01
dc.identifier.citation
Héas, Patrick; Mémin, Etienne; Heitz, Dominique; Mininni, Pablo Daniel; Power laws and inverse motion modelling: Application to turbulence measurements from satellite images; Wiley Blackwell Publishing, Inc; Tellus A; 64; 1; 1-2012; 1-24
dc.identifier.issn
0280-6495
dc.identifier.uri
http://hdl.handle.net/11336/55646
dc.description.abstract
In the context of tackling the ill-posed inverse problem of motion estimation from image sequences, we propose to introduce prior knowledge on flow regularity given by turbulence statistical models. Prior regularity is formalised using turbulence power laws describing statistically self-similar structure of motion increments across scales. The motion estimation method minimises the error of an image observation model while constraining second-order structure function to behave as a power law within a prescribed range. Thanks to a Bayesian modelling framework, the motion estimation method is able to jointly infer the most likely power law directly from image data. The method is assessed on velocity fields of 2-D or quasi-2-D flows. Estimation accuracy is first evaluated on a synthetic image sequence of homogeneous and isotropic 2-D turbulence. Results obtained with the approach based on physics of fluids outperform state-of-the-art. Then, the method analyses atmospheric turbulence using a real meteorological image sequence. Selecting the most likely power law model enables the recovery of physical quantities, which are of major interest for turbulence atmospheric characterisation. In particular, from meteorological images we are able to estimate energy and enstrophy fluxes of turbulent cascades, which are in agreement with previous in situ measurements. © 2012 P. Héas.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Wiley Blackwell Publishing, Inc
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Atmospheric Turbulence
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Bayesian Inference
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Energy Flux
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Image Assimilation
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Motion Structure Functions
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Power-Laws
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Astronomía
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Ciencias Físicas
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CIENCIAS NATURALES Y EXACTAS
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Meteorología y Ciencias Atmosféricas
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Ciencias de la Tierra y relacionadas con el Medio Ambiente
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CIENCIAS NATURALES Y EXACTAS
dc.title
Power laws and inverse motion modelling: Application to turbulence measurements from satellite images
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2018-08-10T17:45:42Z
dc.journal.volume
64
dc.journal.number
1
dc.journal.pagination
1-24
dc.journal.pais
Reino Unido
dc.journal.ciudad
Londres
dc.description.fil
Fil: Héas, Patrick. Institut National de Recherche en Informatique et en Automatique; Francia
dc.description.fil
Fil: Mémin, Etienne. Institut National de Recherche en Informatique et en Automatique; Francia
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Fil: Heitz, Dominique. Irstea;
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Fil: Mininni, Pablo Daniel. Universidad de Buenos Aires; Argentina. National Center for Atmospheric Research; Estados Unidos. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Física de Buenos Aires. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Física de Buenos Aires; Argentina
dc.journal.title
Tellus A
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.tellusa.net/index.php/tellusa/article/view/10962
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.3402/tellusa.v64i0.10962
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