Objective Quantification of Convective Clustering Observed During the AMIE/DYNAMO Two-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 Atmospheric Radiation Measurement Madden-Julian Oscillation Investigation Experiment/Dynamics of the Madden-Julian Oscillation (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, https://doi.org/10.1175/JTECH-D-15-0135.1). Then we apply scalar clustering metrics, including the organization index (Iorg) of Tompkins and Semie, to the radar CCEs to test their ability of quantifying convective clustering during the observed two-day rain episodes. Our results show two distinct phases of convective clustering during the two-day rain episodes, with each phase covering about 10 hr before (Phase 1) and after (Phase 2) the time of peak rain rate. In Phase 1 clustering, the number of CCEs increases and convective cells cluster as new cells form preferentially near existing convective entities. The number of CCEs decreases as the environment stabilizes in Phase 2 clustering, during which already clustered cells with associated stratiform clouds are preferred over the isolated ones. Iorg is capable of capturing convective clustering in both phases. The possible mechanisms for convective clustering are discussed, including cold pool-updraft feedback, moisture-convection interaction, and mesoscale circulations. Our results suggest that parameterizations of convective organization should represent the feedback processes that are responsible for the convective clustering during both phases.