Journal cover Journal topic
Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
Atmos. Chem. Phys., 15, 5325-5358, 2015
https://doi.org/10.5194/acp-15-5325-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Review article
18 May 2015
Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models
M. Bocquet1,2, H. Elbern3, H. Eskes4, M. Hirtl5, R. Žabkar6, G. R. Carmichael7, J. Flemming8, A. Inness8, M. Pagowski9, J. L. Pérez Camaño10, P. E. Saide7, R. San Jose10, M. Sofiev11, J. Vira11, A. Baklanov12, C. Carnevale13, G. Grell9, and C. Seigneur1 1CEREA, Joint Laboratory École des Ponts ParisTech/EDF R&D, Université Paris-Est, Marne-la-Vallée, France
2INRIA, Paris Rocquencourt Research Center, Rocquencourt, France
3Institute for Physics and Meteorology, University of Cologne, Cologne, Germany
4KNMI, De Bilt, The Netherlands
5Central Institute for Meteorology and Geodynamics, Vienna, Austria
6Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
7Center for Global and Regional Environmental Research, University of Iowa, Iowa City, USA
8European Centre for Medium-range Weather Forecasts, Reading, UK
9NOAA/ESRL, Boulder, Colorado, USA
10Technical University of Madrid (UPM), Madrid, Spain
11Finnish Meteorological Institute, Helsinki, Finland
12World Meteorological Organization (WMO), Geneva, Switzerland and Danish Meteorological Institute (DMI), Copenhagen, Denmark
13Department of Mechanical and Industrial Engineering, University of Brescia, Brescia, Italy
Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM.

Citation: Bocquet, M., Elbern, H., Eskes, H., Hirtl, M., Žabkar, R., Carmichael, G. R., Flemming, J., Inness, A., Pagowski, M., Pérez Camaño, J. L., Saide, P. E., San Jose, R., Sofiev, M., Vira, J., Baklanov, A., Carnevale, C., Grell, G., and Seigneur, C.: Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models, Atmos. Chem. Phys., 15, 5325-5358, https://doi.org/10.5194/acp-15-5325-2015, 2015.
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Short summary
Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of concentrations, and perform inverse modeling. Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. We review here the current status of data assimilation in atmospheric chemistry models, with a particular focus on future prospects for data assimilation in CCMM.
Data assimilation is used in atmospheric chemistry models to improve air quality forecasts,...
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