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000276358 1001_ $$0P:(DE-HGF)0$$aBocquet, M.$$b0
000276358 245__ $$aData assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models
000276358 260__ $$aKatlenburg-Lindau$$bEGU$$c2015
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000276358 520__ $$aData 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.
000276358 536__ $$0G:(DE-HGF)POF3-243$$a243 - Tropospheric trace substances and their transformation processes (POF3-243)$$cPOF3-243$$fPOF III$$x0
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000276358 7001_ $$0P:(DE-Juel1)129194$$aElbern, H.$$b1$$ufzj
000276358 7001_ $$0P:(DE-HGF)0$$aEskes, H.$$b2
000276358 7001_ $$0P:(DE-HGF)0$$aHirtl, M.$$b3
000276358 7001_ $$0P:(DE-HGF)0$$aŽabkar, R.$$b4
000276358 7001_ $$0P:(DE-HGF)0$$aCarmichael, G. R.$$b5
000276358 7001_ $$0P:(DE-HGF)0$$aFlemming, J.$$b6
000276358 7001_ $$0P:(DE-HGF)0$$aInness, A.$$b7
000276358 7001_ $$0P:(DE-HGF)0$$aPagowski, M.$$b8
000276358 7001_ $$0P:(DE-HGF)0$$aPérez Camaño, J. L.$$b9
000276358 7001_ $$0P:(DE-HGF)0$$aSaide, P. E.$$b10
000276358 7001_ $$0P:(DE-HGF)0$$aSan Jose, R.$$b11
000276358 7001_ $$0P:(DE-HGF)0$$aSofiev, M.$$b12
000276358 7001_ $$0P:(DE-HGF)0$$aVira, J.$$b13
000276358 7001_ $$0P:(DE-HGF)0$$aBaklanov, A.$$b14
000276358 7001_ $$0P:(DE-HGF)0$$aCarnevale, C.$$b15
000276358 7001_ $$0P:(DE-HGF)0$$aGrell, G.$$b16
000276358 7001_ $$0P:(DE-HGF)0$$aSeigneur, C.$$b17$$eCorresponding author
000276358 773__ $$0PERI:(DE-600)2069847-1$$a10.5194/acp-15-5325-2015$$gVol. 15, no. 10, p. 5325 - 5358$$n10$$p5325 - 5358$$tAtmospheric chemistry and physics$$v15$$x1680-7324$$y2015
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