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Inversion of Airborne Contaminants in a Regional Model

  • Volkan Akcelik
  • George Biros
  • Andrei Draganescu
  • Omar Ghattas
  • Judith Hill
  • Bart van Bloemen Waanders
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3993)

Abstract

We are interested in a DDDAS problem of localization of airborne contaminant releases in regional atmospheric transport models from sparse observations. Given measurements of the contaminant over an observation window at a small number of points in space, and a velocity field as predicted for example by a mesoscopic weather model, we seek an estimate of the state of the contaminant at the begining of the observation interval that minimizes the least squares misfit between measured and predicted contaminant field, subject to the convection-diffusion equation for the contaminant. Once the “initial” conditions are estimated by solution of the inverse problem, we issue predictions of the evolution of the contaminant, the observation window is advanced in time, and the process repeated to issue a new prediction, in the style of 4D-Var. We design an appropriate numerical strategy that exploits the spectral structure of the inverse operator, and leads to efficient and accurate resolution of the inverse problem. Numerical experiments verify that high resolution inversion can be carried out rapidly for a well-resolved terrain model of the greater Los Angeles area.

Keywords

Inverse Problem Inverse Operator Observation Window Airborne Contaminant Forward Transport 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Volkan Akcelik
    • 1
  • George Biros
    • 2
  • Andrei Draganescu
    • 4
  • Omar Ghattas
    • 3
  • Judith Hill
    • 4
  • Bart van Bloemen Waanders
    • 4
  1. 1.Stanford Linear Accelerator Center 
  2. 2.Department of Mechanical Engineering and Applied MechanicsUniversity of Pennsylvania 
  3. 3.Institute for Computational Engineering and SciencesThe University of Texas at Austin 
  4. 4.Optimization and Uncertainty Estimation DepartmentSandia National Laboratories 

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