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For convective clouds and precipitation, model uncertainty in cloud microphysics is considered one of the most significant sources of model error. In this study, samples for model microphysical uncertainty are obtained by calculating the differences between simulations equipped with two‐ and one‐moment schemes during a one‐month training period. The samples are then added to convective‐scale ensemble data assimilation as additive noise and combined with large‐scale additive noise based on samples from climatological atmospheric background error covariance. Two experiments, including the combination and large‐scale error only, are conducted for a one‐week convective period. The results reveal that the simulation with a two‐moment scheme triggers more convection and has larger ice‐phase precipitation particles, which produce a stronger signal in the melting layer. During data assimilation cycling, although more water is introduced to the model, it is shown that the combination performs better for both background and analysis and significantly improves short‐term ensemble forecasts of radar reflectivity and hourly precipitation. Plain Language Summary: One of the main difficulties hindering the improvements of weather forecasts is correct representation of uncertainties in clouds and precipitation in the numerical weather prediction models. The goal of this work is to improve the representation of this uncertainty when combining our prediction with observations. This way, we would obtain better initial condition for our model and better prediction of convection. Here, we obtain the samples for model error by computing the differences between simulations that use two cloud microphysical schemes in the model. Then such obtained samples for model microphysical uncertainty are combined with large‐scale error and incorporated into data assimilation. To evaluate the performance of this method, two experiments are carried out in a one‐week period over Germany. We find that the combination indeed achieves significant improvement of short‐term forecasts of radar reflectivity and hourly precipitation. Key Points: Model simulations with two microphysical schemes are used to represent uncertainty in clouds and precipitation during data assimilationIn data assimilation, including the combination of microphysical and large‐scale uncertainty improves the performanceThe improvement is also significant for short‐term ensemble forecasts of radar reflectivity and hourly precipitation [ABSTRACT FROM AUTHOR]