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Localized Ensemble Kalman Dynamic Data Assimilation for Atmospheric Chemistry
Book chapter   Open access   Peer reviewed

Localized Ensemble Kalman Dynamic Data Assimilation for Atmospheric Chemistry

Adrian Sandu, Emil M Constantinescu, Gregory R Carmichael, Tianfeng Chai, John H Seinfeld and Dacian Dăescu
Computational Science – ICCS 2007, pp.1018-1025
Lecture Notes in Computer Science, Springer Berlin Heidelberg
2007
DOI: 10.1007/978-3-540-72584-8_134
url
https://doi.org/10.1007/978-3-540-72584-8_134View
Published (Version of record) Open Access

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.
 Data Assimilation Ensemble Size Ground Level Ozone Lateral Boundary Condition Ozone Concentration

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