Bayesian algorithms are a common method of retrieving cloud properties from a set of observed passive microwave brightness temperature (TB) measurements. In practice, such methods often use predefined databases from cloud-resolving models to perform the retrieval. Successful performance in these types of Bayesian retrievals is greatly affected by the similarity of TBs between observed and predefined databases. Here empirical orthogonal function (EOF) analysis methods were used to illustrate the importance of predefined simulation databases on the ability to retrieve known regional variability in cloud structures across different parts of the tropical oceans and to provide insight on the relative impact of environmental conditions to limitations in cloud model microphysical parameterizations in retrieving different types of cloud structures. The spatial distributions of EOF coefficients in EOF space showed that there were both underrepresentation and overrepresentation between manifolds of the predefined and observed databases. Moreover, there was poor alignment in the frequency distribution between observed and simulated clouds structures. Additionally, the ability to retrieve known regional variability in convective cloud structure was dependent on the environmental conditions used to generate the simulated data set more so than limitations in the model microphysical parameterizations. The opposite was true for stratiform rain, as the dominant EOF patterns of the radiation indices for the predefined databases exhibit overproduction of large-sized high-density ice-phase hydrometeors for a given rain content in comparison to the observations. Improvements to microphysical parameterization schemes appear to be necessary to produce radiance index vectors that are consistent with observations.