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

• Autores: Héas, Patrick; Mémin, Etienne; Heitz, Dominique; Mininni, Pablo Daniel
• Año de publicación: 2012
• Idioma: inglés
• Tipo de recurso: artículo
• Materia: ATMOSPHERIC TURBULENCE; BAYESIAN INFERENCE; ENERGY FLUX; IMAGE ASSIMILATION; MOTION STRUCTURE FUNCTIONS; POWER-LAWS
• Institución: Consejo Nacional de Investigaciones Científicas y Técnicas

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: Héas, Patrick. Institut National de Recherche en Informatique et en Automatique; Francia
Fil: Mémin, Etienne. Institut National de Recherche en Informatique et en Automatique; Francia
Fil: Heitz, Dominique. Irstea;
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