Sandu, AdrianChai, Tianfeng2014-05-142014-05-142011-08-29Sandu, A.; Chai, T. F., "Chemical Data Assimilation-An Overview," Atmosphere 2011, 2(3), 426-463; doi:10.3390/atmos2030426.2073-4433http://hdl.handle.net/10919/47972Chemical 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.application/pdfenCreative Commons Attribution 4.0 InternationalChemical transport modelingData assimilationKalman filteringVariational methodsEnsemble kalman filterVariational data assimilationAdjointSensitivity analysisAir-quality modelsDynamically consistentFormulationsBound-constrained optimizationAtmospheric dataAssimilationSequential data assimilationDiscrete advection adjointsChemistry-transport modelMeteorology & atmospheric sciencesArticle - Refereedhttp://www.mdpi.com/2073-4433/2/3/426https://doi.org/10.3390/atmos2030426