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@ARTICLE{Bocquet:276358,
      author       = {Bocquet, M. and Elbern, H. and Eskes, H. and Hirtl, M. and
                      Žabkar, R. and Carmichael, G. R. and Flemming, J. and
                      Inness, A. and Pagowski, M. and Pérez Camaño, J. L. and
                      Saide, P. E. and San Jose, R. and Sofiev, M. and Vira, J.
                      and Baklanov, A. and Carnevale, C. and Grell, G. and
                      Seigneur, C.},
      title        = {{D}ata assimilation in atmospheric chemistry models:
                      current status and future prospects for coupled chemistry
                      meteorology models},
      journal      = {Atmospheric chemistry and physics},
      volume       = {15},
      number       = {10},
      issn         = {1680-7324},
      address      = {Katlenburg-Lindau},
      publisher    = {EGU},
      reportid     = {FZJ-2015-06816},
      pages        = {5325 - 5358},
      year         = {2015},
      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.},
      cin          = {IEK-8},
      ddc          = {550},
      cid          = {I:(DE-Juel1)IEK-8-20101013},
      pnm          = {243 - Tropospheric trace substances and their
                      transformation processes (POF3-243)},
      pid          = {G:(DE-HGF)POF3-243},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000355289200001},
      doi          = {10.5194/acp-15-5325-2015},
      url          = {https://juser.fz-juelich.de/record/276358},
}