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Localized Ensemble Kalman Dynamic Data Assimilation for Atmospheric Chemistry

Sandu, Adrian and Constantinescu, Emil M. and Carmichael, Gregory R. and Chai, Tianfeng and Seinfeld, John H. and Dăescu, Dacian (2007) Localized Ensemble Kalman Dynamic Data Assimilation for Atmospheric Chemistry. In: Computational Science – ICCS 2007. Lecture Notes in Computer Science. No.4487. Springer , Berlin, pp. 1018-1025. ISBN 978-3-540-72583-1.

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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.

Item Type:Book Section
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Seinfeld, John H.0000-0003-1344-4068
Additional Information:© 2007 Springer Berlin Heidelberg. This work was supported by the National Science Foundation through the award NSF ITR AP&IM 0205198 managed by Dr. Frederica Darema.
Funding AgencyGrant Number
Subject Keywords:Data Assimilation; Ozone Concentration; Lateral Boundary Condition; Ensemble Size; Ground Level Ozone
Series Name:Lecture Notes in Computer Science
Issue or Number:4487
Record Number:CaltechAUTHORS:20200603-084113107
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:103661
Deposited By: Tony Diaz
Deposited On:03 Jun 2020 16:03
Last Modified:16 Nov 2021 18:23

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