Important issues involving the assimilation of rain-affected observations using an adjoint mesoscale modeling system are addressed in this study. The adjoint model of the explicit moist physics parameterization is included in the modeling system, which allows for the calculation of gradients with respect to the initial hydrometeor concentrations (cloud water/ice, rain, snow, and graupel). Cloud-scale idealized four dimensional variational data assimilation experiments demonstrate the benefit of assimilating precipitation information and the ability of the adjoint model to produce useful gradients with respect to the hydrometeor fields. The agreement between model fields and observations is greater (especially for the early forecast hydrometeor fields) when rainy observations are incorporated into the assimilation process versus only assimilating conventional model data (windspeeds, temperature, pressure). Additional data assimilation experiments are conducted with microwave radiances. These data improve the initial precipitation structure of a tropical cyclone. These experiments are promising steps for the incorporation of rain-affected observations in operational data assimilation systems.