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This paper aims at presenting a validation of multi-pollutants over Europe with a focus on aerosols. Chemistry-Transport Models are now used for forecast and emission reduction studies not only for gas-phase species but also for aerosols. Comprehensive model-to-data comparisons are therefore required. We present in this paper a preliminary validation study of the POLYPHEMUS system applied over Europe for 2001. The aerosol model is the SIze REsolved Aerosol Model (SIREAM). It is a sectional model that describes the temporal evolution of the size/composition distribution of atmospheric particles containing a mix of black carbon, mineral dust, inorganic species, and primary and secondary organics. In addition to a brief model description, we present an overview of the model validation. A comprehensive set of model-to-data statistics is computed with observational data extracted from three European databases (the EMEP, AirBase and BDQA databases). Model performance criteria are verified for ozone and particulate matter (PM) and its inorganic components. Comparisons of correlations and root mean square errors with those generated by other models run over Europe for 2001 indicate a good performance of the POLYPHEMUS system. Modifications of the system configuration and parameterizations may have a significant impact on error statistics, which may question the robustness of such models. Because large differences exist between databases, the robustness of model-to-data error statistics is also investigated.

[1] The objective of this paper is to evaluate the performances of different data assimilation schemes with the aim of designing suitable assimilation algorithms for shortrange ozone forecasts in realistic applications. The underlying atmospheric chemistrytransport models are stiff but stable systems with high uncertainties (e.g., over 20% for ozone daily peaks (Hanna et al., 1998;Mallet and Sportisse, 2006b), and much more for other pollutants like aerosols). Therefore the main difficulty of the ozone data assimilation problem is how to account for the strong model uncertainties. In this paper, the model uncertainties are either parameterized with homogeneous error correlations of the model state or estimated by perturbing some sources of the uncertainties, for example, the model uncertain parameters. Four assimilation methods have been considered, namely, optimal interpolation, reduced-rank square root Kalman filter, ensemble Kalman filter, and four-dimensional variational assimilation. These assimilation algorithms are compared under the same experimental settings. It is found that the assimilations significantly improve the 1-day ozone forecasts. The comparison results reveal the limitations and the potentials of each assimilation algorithm. In our four-dimensional variational method, the low dependency of model simulations on initial conditions leads to moderate performances. In our sequential methods, the optimal interpolation algorithm has the best performance during assimilation periods. Our ensemble Kalman filter algorithm perturbs the uncertain parameters to approximate model uncertainties and has better forecasts than the optimal interpolation algorithm during prediction periods. This could partially be explained by the low dependency on the uncertainties in initial conditions. The sensitivity analysis on the algorithmic parameters is also conducted for the design of suitable assimilation algorithms for ozone forecasts.Citation: Wu, L., V. Mallet, M. Bocquet, and B. Sportisse (2008), A comparison study of data assimilation algorithms for ozone forecasts,