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Atmosphere 2011, 2(3), 426-463; doi:10.3390/atmos2030426

Chemical Data Assimilation—An Overview

1,*  and 2
1 Computational Science Laboratory, Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0106, USA 2 NOAA/OAR/ARL, Silver Spring Metro Center #3, Rm. 3437, 1315 East West Highway, Silver Spring, MD 20910, USA
The paper is dedicated to the memory of Dr. Daewon Byun, whose work remains a lasting legacy to the field of air quality modeling and simulation.
* Author to whom correspondence should be addressed.
Received: 7 June 2011 / Revised: 9 August 2011 / Accepted: 19 August 2011 / Published: 29 August 2011
(This article belongs to the Special Issue Air Pollution Modeling: Reviews of Science Process Algorithms)
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Chemical data assimilation is the process by which models use measurements to produce an optimal representation of the chemical composition of the atmosphere. Leveraging advances in algorithms and increases in the available computational power, the integration of numerical predictions and observations has started to play an important role in air quality modeling. This paper gives an overview of several methodologies used in chemical data assimilation. We discuss the Bayesian framework for developing data assimilation systems, the suboptimal and the ensemble Kalman filter approaches, the optimal interpolation (OI), and the three and four dimensional variational methods. Examples of assimilation real observations with CMAQ model are presented.
Keywords: chemical transport modeling; data assimilation; Kalman filter; variational methods chemical transport modeling; data assimilation; Kalman filter; variational methods
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Sandu, A.; Chai, T. Chemical Data Assimilation—An Overview. Atmosphere 2011, 2, 426-463.

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