Variability in the global distribution of precipitation is a key component in understanding the impacts of climate change on the environment. At present, the response of precipitation to annual and inter-annual climate forcings is uncertain due, in part, to discrepancies in satellite-based rainfall estimates and their climatic trends on both regional and global scales. These discrepancies can be traced to regime-dependent systematic errors in algorithm parameters that are not explicitly measured by the observing system. As a result, there is a need to extend modern algorithm validation approaches beyond traditional comparisons of retrieved products to verify the assumptions used to obtain them. This talk explores this concept of "physical validation" in the context of constraining drop size distribution (DSD) in precipitation retrievals from single frequency radars such as the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR). Evidence will be provided in support of the hypothesis that distinct microphysical pathways for rainfall production not only influence the DSD of the resulting raindrops but also the vertical and horizontal structure of the associated radar reflectivity profiles. This link between microphysics, reflectivities, and DSDs provides a means for extending the conventional convective/stratiform separation to define a broader set of rain-type classes based on the observed reflectivity field. Using results from the TRMM-Large-scale Biosphere Atmosphere (LBA) field campaign, a methodology will be illustrated for exporting drop size information from polarimetric radar observations made in regional field experiments to constrain the assumptions in global rainfall retrievals from spaceborne radars. Classifying rainfall in this way offers the potential to mitigate large random errors incurred as a result of natural DSD variability and to provide a means for quantifying systematic errors due climate regime-dependent changes in assumed DSD.