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Original Research Papers

Assimilation of image sequences in numerical models

Authors:

Olivier Titaud ,

INRIA Grenoble Rhône-Alpes, Montbonnot, 38334 Saint Ismier Cedex; Université de Grenoble et CNRS, Laboratoire Jean Kuntzmann, BP 53, 38041 Grenoble Cédex 9, FR
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Arthur Vidard,

INRIA Grenoble Rhône-Alpes, Montbonnot, 38334 Saint Ismier Cedex; Université de Grenoble et CNRS, Laboratoire Jean Kuntzmann, BP 53, 38041 Grenoble Cédex 9, FR
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Innocent Souopgui,

INRIA Grenoble Rhône-Alpes, Montbonnot, 38334 Saint Ismier Cedex; Université de Grenoble et CNRS, Laboratoire Jean Kuntzmann, BP 53, 38041 Grenoble Cédex 9, FR
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François-Xavier Le Dimet

INRIA Grenoble Rhône-Alpes, Montbonnot, 38334 Saint Ismier Cedex; Université de Grenoble et CNRS, Laboratoire Jean Kuntzmann, BP 53, 38041 Grenoble Cédex 9, FR
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Abstract

Understanding and forecasting the evolution of geophysical fluids is a major scientific and societal challenge. Forecasting algorithms should take into account all the available information on the considered dynamic system. The variational data assimilation (VDA) technique combines all these informations in an optimality system (O.S.) in a consistent way to reconstruct the model inputs. VDA is currently used by the major meteorological centres. During the last two decades about 30 satellites were launched to improve the knowledge of the atmosphere and of the oceans. They continuously provide a huge amount of data that are still underused by numerical forecast systems. In particular, the dynamic evolution of certain meteorological or oceanic features (such as eddies, fronts, etc.) that the human vision may easily detect is not optimally taken into account in realistic applications of VDA. Image Assimilation in VDA framework can be performed using ‘pseudo-observation’ techniques: they provide apparent velocity fields, which are assimilated as classical observations. These measurements are obtained by certain external procedures, which are decoupled with the considered dynamic system. In this paper, we suggest a more consistent approach, which directly incorporates image sequences into the O.S.

How to Cite: Titaud, O., Vidard, A., Souopgui, I. and Le Dimet, F.-X., 2010. Assimilation of image sequences in numerical models. Tellus A: Dynamic Meteorology and Oceanography, 62(1), pp.30–47. DOI: http://doi.org/10.1111/j.1600-0870.2009.00416.x
18
Citations
  Published on 01 Jan 2010
 Accepted on 3 Oct 2009            Submitted on 28 Oct 2008

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