The objective of this paper is to evaluate the performances of different data assimilation schemes with the aim of designing suitable assimilation algorithms for short-range ozone forecasts in realistic applications. The underlying atmospheric chemistry-transport 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.