This paper presents experiments of PM10 data assimilation with theoptimal interpolation method. The observations are provided by BDQA (Base deDonnées sur la Qualité de l'Air), whose monitoring network coversFrance. Two other databases (EMEP and AirBase) are used to evaluate theimprovements in the analyzed state over January 2001 and for several outputs(PM10, PM2.5 and chemical composition). The method is then applied inoperational-forecast conditions. It is found that the assimilation ofPM10 observations significantly improves the one-day forecast of totalmass (PM10 and PM2.5), whereas the improvement is non significant forthe two-day forecast. The errors on aerosol chemical composition aresometimes amplified by the assimilation procedure, which shows the need forchemical data. Since the observations cover a limited part of the domain(France versus Europe) and since the method used for assimilation issequential, we focus on the horizontal and temporal impacts of theassimilation and we study how several parameters of the assimilation systemmodify these impacts. The strategy followed in this paper, with the optimalinterpolation, could be useful for operational forecasts. Meanwhile,considering the weak temporal impact of the approach (about one day), themethod has to be improved or other methods have to be considered.
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