One of the most noticeable aspects of mesoscale convective organization in radar data is the degree of convective clustering, which can be characterized by the number and size distribution of convective echoes and the distance between them. We propose a method of defining contiguous convective echoes (CCEs) using precipitating convective echoes identified by a rain type classification algorithm. Two rain type classification algorithms, Steiner et al. (1995) and Powell et al. (2016), are tested and evaluated against WRF simulations to determine which method better represents the degree of convective clustering. Our results suggest that the statistics of CCEs based on Powell et al.’s algorithm better representing the dynamical properties of the convective updrafts and thus provide a reliable metric for convective organization. Furthermore, through a comparison with the observational data, the WRF simulations driven by the DYNAMO large-scale forcing the same way as UNICON Single Column Model simulations will allow us to evaluate the ability of both WRF and UNICON to simulate convective clustering based on the physical processes that are explicitly represented in both models, including the mechanisms leading to convective clustering, and the feedback to the convective properties.