Uncertainty in cloud microphysical parameterization -- a leading order contribution to numerical weather prediction error -- is estimated in order to deterimine how vertically resolved radar reflectivity observations constrain error in a bulk, single-moment microphysical parameterization scheme. To this end, a Markov chain Monte Carlo (MCMC) algorithm is employed to perform an inversion on ten microphysical parameters using radar reflectivity observations with vertically covarying error as the likelihood constraint. In addition, novel diagnostics allow for the probabilistic investigation of individual microphysical process behavior vis-à-vis parameter uncertainty. Uncertainty in the microphysical parameterization is presented via probability density functions (PDFs) of parameters, observations and microphysical processes. The results of this study show that radar reflectivity observations provide a much stronger constraint on microphysical parameters than column-integral observations, in most cases reducing variance and bias in the maximum likelihood estimate of parameter values. This highlights the enhanced ability of radar reflectivity observations to provide information about microphysical processes within convective storm systems. The analysis of parameterization uncertainty in terms of both parameter and process activity PDFs allows for conclusions to be drawn on the prospect of a stochastic representation of microphysical parameterization uncertainty -- specifically that error may be more easily represented by microphysical process uncertainty rather than microphysical parameter uncertainty.