Chemical Data Assimilation-An Overview

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Date
2011-08-29
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Volume Title
Publisher
MDPI
Abstract

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.

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Keywords
Chemical transport modeling, Data assimilation, Kalman filtering, Variational methods, Ensemble kalman filter, Variational data assimilation, Adjoint, Sensitivity analysis, Air-quality models, Dynamically consistent, Formulations, Bound-constrained optimization, Atmospheric data, Assimilation, Sequential data assimilation, Discrete advection adjoints, Chemistry-transport model, Meteorology & atmospheric sciences
Citation
Sandu, A.; Chai, T. F., "Chemical Data Assimilation-An Overview," Atmosphere 2011, 2(3), 426-463; doi:10.3390/atmos2030426.