Ensemble–Based Data Assimilation for Atmospheric Chemical Transport Models*

Adrian Sandu1, Emil M. Constantinescu1, Wenyuan Liao1, Gregory R. Carmichael2, Tianfeng Chai2, John H. Seinfeld3, and Dacian Daescu4

1Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061
asandu@cs.vt.edu
emconsta@cs.vt.edu
liao@cs.vt.edu

2Center for Global and Regional Environmental Research, The University of Iowa, Iowa City, 52242-1297
gcarmich@cgrer.uiowa.edu
tchai@cgrer.uiowa.edu

3Department of Chemical Engineering, California Institute of Technology, Pasadena, CA 91125
seinfeld@caltech.edu

4Department of Mathematics and Statistics, Portland State University

Abstract. The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems that efficiently integrate the observational data and the models. In this paper we discuss fundamental aspects of nonlinear ensemble data assimilation applied to atmospheric chemical transport models. We formulate autoregressive models for the background errors and show how these models are capable of capturing flow dependent correlations. Total energy singular vectors describe the directions of maximum errors growth and are used to initialize the ensembles. We highlight the challenges encountered in the computation of singular vectors in the presence of stiff chemistry and propose solutions to overcome them. Results for a large scale simulation of air pollution in East Asia illustrate the potential of nonlinear ensemble techniques to assimilate chemical observations.

Key words: Dynamic data-driven applications and systems, data assimilation, background covariance, ensemble Kalman filter, total energy singular vectors, autoregressive processes.

*This work was supported by the National Science Foundation through the award NSF ITR [AP+IM]-0205198 managed by Dr. Frederica Darema.

LNCS 3515, pp. 648-655.

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