Variational Pressure Image Assimilation for Atmospheric Motion Estimation

The complexity of dynamical laws governing 3D atmospheric flows associated with incomplete and noisy observations make the recovery of atmospheric dynamics from satellite images sequences very difficult. In this paper, we face the challenging problem of estimating physical sound and time-consistent horizontal motion fields at various atmospheric depths for a whole image sequence. Based on a vertical decomposition of the atmosphere, we propose a dynamically consistent atmospheric motion estimator relying on a multi-layer dynamical model. This estimator is based on a weak constraint variational data assimilation scheme and is applied on noisy and incomplete pressure difference observations derived from satellite images. The dynamical model consists in a simplified vorticity-divergence form of a multi-layer shallow-water model. Average horizontal motion fields are estimated for each layer. The performance of the proposed technique is assessed on real world meteorological satellite image sequences.

[1]  Olivier Talagrand,et al.  Assimilation of Observations, an Introduction (gtSpecial IssueltData Assimilation in Meteology and Oceanography: Theory and Practice) , 1997 .

[2]  F. L. Dimet,et al.  Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects , 1986 .

[3]  Nicolas Papadakis,et al.  Layered Estimation of Atmospheric Mesoscale Dynamics From Satellite Imagery , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[4]  N. Mansour.  Large-eddy simulation of a turbulent mixing layer , 1978 .

[5]  T. Corpetti,et al.  Fluid experimental flow estimation based on an optical-flow scheme , 2006 .

[6]  Andrew F. Bennett,et al.  Inverse Methods in Physical Oceanography: Frontmatter , 1992 .

[7]  Nicolas Papadakis,et al.  A Variational Technique for Time Consistent Tracking of Curves and Motion , 2008, Journal of Mathematical Imaging and Vision.

[8]  Takeo Kanade,et al.  An Iterative Image Registration Technique with an Application to Stereo Vision , 1981, IJCAI.

[9]  CLOUD PROCESSING FOR METEOSAT SECOND GENERATION (MSG) , 1999 .

[10]  Berthold K. P. Horn,et al.  Determining Optical Flow , 1981, Other Conferences.

[11]  A. Bennett,et al.  The generalized inverse of a nonlinear quasigeostrophic ocean circulation model , 1992 .

[12]  Andrew F. Bennett,et al.  Inverse Methods in Physical Oceanography: Bibliography , 1992 .

[13]  J. Lions.  Optimal Control of Systems Governed by Partial Differential Equations , 1971 .

[14]  Jing Yuan,et al.  Discrete Orthogonal Decomposition and Variational Fluid Flow Estimation , 2005, Journal of Mathematical Imaging and Vision.

[15]  P. Courtier,et al.  Variational Assimilation of Meteorological Observations With the Adjoint Vorticity Equation. I: Theory , 2007 .

[16]  T. Petti,et al.  Pressure image assimilation for atmospheric motion estimation , 2009 .

[17]  Geoffrey Ingram Taylor,et al.  The transport of vorticity and heat through fluids in turbulent motion , 1932 .

[18]  Anne Cuzol,et al.  A Stochastic Filtering Technique for Fluid Flow Velocity Fields Tracking , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.