Objective quantification of convective clustering observed during the AMIE/DYNAMO 2-day rain episodes

Abstract

One critical bottleneck in developing and evaluating ways to represent the mesoscale organization of convection in cumulus parameterization schemes is that there is no single accepted method of objectively quantifying the degree of convective organization or clustering from observations. This study addresses this need using high-quality S-PolKa radar data from the AMIE/DYNAMO field campaign. We first identify contiguous convective echoes (CCEs) from radar reflectivity observations using the rain type classication algorithm of Powell et al. Scalar metrics, including the organization index (Iorg) of Tompkins and Semie, are applied to the radar CCEs to test their ability of quantifying convective clustering during the observed 2-day rain episodes. Our results show two distinct phases of convective clustering during the 2-day rain episodes, with each phase covering about 10 hours before (Phase 1) and after (Phase 2) the time of peak rain rate. During Phase 1, convective cells cluster as new cells are formed near existing convective entities, presumably through the interaction of cold pools with convective updrafts. During Phase 2, the clustered convective entities are sustained longer than the isolated ones, possibly through feedback from the stratiform clouds and associated mesoscale circulations. Iorg is capable of capturing convective clustering in both phases by using the distance between nearest-neighboring CCEs. Our results suggest that parameterizations of convective organization should represent the two feedback processes from the boundary layer cold pools and the stratiform clouds to convective updrafts.