ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-9-57-2009 PM<sub>10</sub> data assimilation over Europe with the optimal interpolation method Tombette M. 1 2 Mallet V. 1 2 Sportisse B. 1 2 CEREA, joint laboratory ENPC – EDF R&D, Université Paris-Est, Marne la Vallée, France INRIA, Paris-Rocquencourt research center, France 07 01 2009 9 1 57 70 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/9/57/2009/acp-9-57-2009.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/9/57/2009/acp-9-57-2009.pdf

This paper presents experiments of PM<sub>10</sub> data assimilation with the optimal interpolation method. The observations are provided by BDQA (Base de Données sur la Qualité de l'Air), whose monitoring network covers France. Two other databases (EMEP and AirBase) are used to evaluate the improvements in the analyzed state over January 2001 and for several outputs (PM<sub>10</sub>, PM<sub>2.5</sub> and chemical composition). The method is then applied in operational-forecast conditions. It is found that the assimilation of PM<sub>10</sub> observations significantly improves the one-day forecast of total mass (PM<sub>10</sub> and PM<sub>2.5</sub>), whereas the improvement is non significant for the two-day forecast. The errors on aerosol chemical composition are sometimes amplified by the assimilation procedure, which shows the need for chemical data. Since the observations cover a limited part of the domain (France versus Europe) and since the method used for assimilation is sequential, we focus on the horizontal and temporal impacts of the assimilation and we study how several parameters of the assimilation system modify these impacts. The strategy followed in this paper, with the optimal interpolation, could be useful for operational forecasts. Meanwhile, considering the weak temporal impact of the approach (about one day), the method has to be improved or other methods have to be considered.

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