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Ensemble-Based Data Assimilation for Atmospheric Chemical Transport Models

Sandu, Adrian and Constantinescu, Emil M. and Liao, Wenyuan and Carmichael, Gregory R. and Chai, Tianfeng and Seinfeld, John H. and Dăescu, Dacian (2005) Ensemble-Based Data Assimilation for Atmospheric Chemical Transport Models. In: Computational Science - ICCS 2005. Lecture Notes in Computer Science. No.3515. Springer , Berlin, pp. 648-655. ISBN 978-3-540-26043-1. https://resolver.caltech.edu/CaltechAUTHORS:20200127-095651205

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Abstract

The task of providing an optimal analysis of the state of the atmosphere requires the development of dynamic data-driven systems (D³AS) 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.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://doi.org/10.1007/11428848_84DOIArticle
https://rdcu.be/b3Yn7PublisherFree ReadCube access
ORCID:
AuthorORCID
Seinfeld, John H.0000-0003-1344-4068
Additional Information:© 2005 Springer-Verlag Berlin Heidelberg. This work was supported by the National Science Foundation through the award NSF ITR AP&IM 0205198 managed by Dr. Frederica Darema.
Funders:
Funding AgencyGrant Number
NSFAGS-0205198
Subject Keywords:Dynamic data-driven applications and systems (D3AS); data assimilation; background covariance; ensemble Kalman filter; total energy singular vectors; autoregressive processes
Series Name:Lecture Notes in Computer Science
Issue or Number:3515
Record Number:CaltechAUTHORS:20200127-095651205
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20200127-095651205
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100937
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:28 Jan 2020 19:22
Last Modified:05 May 2020 23:00

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