Inversion of Airborne Contaminants in a Regional Model

Loading...
Thumbnail Image
Penn collection
Departmental Papers (MEAM)
Degree type
Discipline
Subject
Funder
Grant number
License
Copyright date
Distributor
Related resources
Author
Akcelik, Volkan
Draganescu, Andrei
Ghattas, Omar
Hill, Judith
van Bloemen Waanders, Bart
Contributor
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.

Advisor
Date of presentation
2006-05-28
Conference name
Departmental Papers (MEAM)
Conference dates
2023-05-17T00:19:56.000
Conference location
Date Range for Data Collection (Start Date)
Date Range for Data Collection (End Date)
Digital Object Identifier
Series name and number
Volume number
Issue number
Publisher
Publisher DOI
Journal Issue
Comments
Postprint version. Published in Lecture Notes in Computer Science, Volume 3993, Computational Science – ICCS 2006 6th International Conference, Proceedings, Part III, 2006, pages 481-488. Publisher URL: http://dx.doi.org/10.1007/11758532_64
Recommended citation
Collection