Power laws and inverse motion modelling: Application to turbulence measurements from satellite images

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 stru...

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Detalles Bibliográficos
Autor principal:	Mininni, Pablo Daniel
Publicado:	2012
Materias:	Atmospheric turbulence Bayesian inference Energy flux Image assimilation Motion structure functions Power-laws Bayesian analysis energy flux estimation method inverse problem numerical model power law satellite imagery turbulence
Acceso en línea:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_02806495_v64_n1_p_Heas http://hdl.handle.net/20.500.12110/paper_02806495_v64_n1_p_Heas
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	paper:paper_02806495_v64_n1_p_Heas
record_format	dspace
spelling	paper:paper_02806495_v64_n1_p_Heas2023-06-08T15:26:56Z Power laws and inverse motion modelling: Application to turbulence measurements from satellite images Mininni, Pablo Daniel Atmospheric turbulence Bayesian inference Energy flux Image assimilation Motion structure functions Power-laws Bayesian analysis energy flux estimation method inverse problem numerical model power law satellite imagery turbulence 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. Fil:Mininni, P.D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2012 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_02806495_v64_n1_p_Heas http://hdl.handle.net/20.500.12110/paper_02806495_v64_n1_p_Heas
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Atmospheric turbulence Bayesian inference Energy flux Image assimilation Motion structure functions Power-laws Bayesian analysis energy flux estimation method inverse problem numerical model power law satellite imagery turbulence
spellingShingle	Atmospheric turbulence Bayesian inference Energy flux Image assimilation Motion structure functions Power-laws Bayesian analysis energy flux estimation method inverse problem numerical model power law satellite imagery turbulence Mininni, Pablo Daniel Power laws and inverse motion modelling: Application to turbulence measurements from satellite images
topic_facet	Atmospheric turbulence Bayesian inference Energy flux Image assimilation Motion structure functions Power-laws Bayesian analysis energy flux estimation method inverse problem numerical model power law satellite imagery turbulence
description	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.
author	Mininni, Pablo Daniel
author_facet	Mininni, Pablo Daniel
author_sort	Mininni, Pablo Daniel
title	Power laws and inverse motion modelling: Application to turbulence measurements from satellite images
title_short	Power laws and inverse motion modelling: Application to turbulence measurements from satellite images
title_full	Power laws and inverse motion modelling: Application to turbulence measurements from satellite images
title_fullStr	Power laws and inverse motion modelling: Application to turbulence measurements from satellite images
title_full_unstemmed	Power laws and inverse motion modelling: Application to turbulence measurements from satellite images
title_sort	power laws and inverse motion modelling: application to turbulence measurements from satellite images
publishDate	2012
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_02806495_v64_n1_p_Heas http://hdl.handle.net/20.500.12110/paper_02806495_v64_n1_p_Heas
work_keys_str_mv	AT mininnipablodaniel powerlawsandinversemotionmodellingapplicationtoturbulencemeasurementsfromsatelliteimages
_version_	1768543367674724352

Power laws and inverse motion modelling: Application to turbulence measurements from satellite images

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