Abstract. In cloudy situations, infrared and microwave observations are complementary, with infrared observations being sensitive to the small cloud droplets and ice particles and microwave observations sensitive to precipitation. This complementarity can lead to fruitful synergies in precipitation science (e.g. Kidd and Levizzani, 2022). However, several sources of errors do exist in the treatment of infrared and microwave data that could prevent such synergy. This paper studies several of these sources to estimate their impact on retrievals. To do so, simulations from the radiative transfer model RTTOV v13 are used to build simulated observations. Indeed, we make use of a fully simulated framework to explain the impacts of the identified errors. A combination of infrared and microwave frequencies is built within a Bayesian inversion framework. Synergy is studied using different experiments: (i) with several sources of errors eliminated; (ii) with only one source of errors considered at a time; (iii) with all sources of errors together. The derived retrievals of frozen hydrometeors for each experiment are examined in a statistical study of fifteen days in summer and fifteen days in winter over the Atlantic ocean. One of the main outcomes of the study is that the combination of infrared and microwave frequencies takes advantage of both spectral range strengths leading to accurate retrievals. Each source of error has more or less impact depending on the type of hydrometeor. Another outcome of the study is that even though the errors may decrease the magnitude of benefits generated by the combination of infrared and microwave frequencies, in all cases explored, their combination remains beneficial.