Abstract
The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems (DDDAS) that efficiently integrate the observational data and the models. Data assimilation, the dynamic incorporation of additional data into an executing application, is an essential DDDAS concept with wide applicability. In this paper we discuss practical aspects of nonlinear ensemble Kalman data assimilation applied to atmospheric chemical transport models. We highlight the challenges encountered in this approach such as filter divergence and spurious corrections, and propose solutions to overcome them, such as background covariance inflation and filter localization. The predictability is further improved by including model parameters in the assimilation process. Results for a large scale simulation of air pollution in North-East United States illustrate the potential of nonlinear ensemble techniques to assimilate chemical observations.
This work was supported by the National Science Foundation through the award NSF ITR AP&IM 0205198 managed by Dr. Frederica Darema.
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Sandu, A., Constantinescu, E.M., Carmichael, G.R., Chai, T., Seinfeld, J.H., Dăescu, D. (2007). Localized Ensemble Kalman Dynamic Data Assimilation for Atmospheric Chemistry. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds) Computational Science – ICCS 2007. ICCS 2007. Lecture Notes in Computer Science, vol 4487. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72584-8_134
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DOI: https://doi.org/10.1007/978-3-540-72584-8_134
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