Objective quantification of convective clustering using ground-based radar reflectivity during AMIE/DYNAMO

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Description

One critical bottleneck in developing and evaluating ways to represent the mesoscale organization of convection in the cumulus parameterization 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 convective elements (contiguous convective echoes, CCEs) from radar reflectivity observations using the rain type classification algorithm of Powell et al. (2016), which was chosen over that of Steiner et al. (1995) based on a test using cloud-resolving model simulations. Scalar metrics, including the Simple Convective Aggregation Index (SCAI) of Tobin et al. (2012) and the organization index (Iorg) of Tompkins and Semie (2017), are applied to the radar CCEs to quantify the degree of clustering using the number of and the distances between CCEs. The extent to which each metric can capture the observed convective organization/clustering is tested against 2-day rain episodes during AMIE/DYNAMO. Our results show two distinct phases of convective clustering during these 2-day rain episodes, with each phase covering about 12 hours before (Phase 1) and after (Phase 2) the time of peak rain rate. During Phase 1, convective clouds are clustered as new CCEs are added to the radar domain, while a decrease in the number of CCEs accompanies the clustering during Phase 2. The convective clustering during Phase 1 is caused by the tendency of new convective cells to form near existing convective entities, presumably through the interaction of cold pools and convective updrafts. During Phase 2, clustered convective cells sustain longer than the isolated ones, possibly through feedback from the stratiform clouds and associated mesoscale circulations. SCAI is able to capture the convective clustering only in Phase 2. Iorg, on the other hand, is capable of capturing convective clustering in both Phases by using the distance between the 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. Convective clustering/organization in WRF perturbed-physics ensemble and UNICON single column model simulations will also be presented. Possible extension of the current work to land convection (MC3E) will be discussed.