Chemical Data Assimilation—An Overview ‡

Atmosphere

Adrian Sandu¹ 

[1] Computational Science Laboratory, Department of Computer Science, Virginia Polytechnic Institute and State University, Blacksburg, VA 24061-0106, USA

关键词: chemical transport modeling;    data assimilation;    Kalman filter;    variational methods;   

DOI  :  10.3390/atmos2030426

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.