Modeling cloud processes with microphysical models critically relies on constraint by laboratory measurements and observations. Such data provide the basis for model parameterizations, corroborate/critique model performance, and guide model improvements. Additionally, forward and inverse modeling with observations has been used to inform key model parameters that otherwise cannot be measured. Recent advances in remote-sensing retrievals, in-situ particle imaging, and laboratory measurements of particle formation/growth provide unique data that can inform/constrain models. We propose dedicated session on synergistic use of measurements and observations in the development and testing of cloud models of all scales. We solicit abstracts on novel theoretical, modeling, and observational techniques that elucidate cloud microphysical evolution. We solicit studies employing rigorous uncertainty quantification (e.g., through Bayesian methods), studies using laboratory measurements to inform/critique models, novel uses of in-situ measurements in microphysical modeling, and studies exploring the information content of remote-sensing and other observations relevant to microphysics.