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.

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Physical mechanisms that are key to observed convective clustering in 2‐day rain events are examined. Previous analysis of the 2‐day rain events during the Atmospheric Radiation Measurement Madden‐Julian Oscillation Investigation Experiment (AMIE)/Dynamics of the Madden‐Julian Oscillation (DYNAMO) field campaign data revealed two distinct phases of convective clustering. Using a cloud‐system‐resolving model, we perform a series of intervention experiments to investigate the underlying mechanisms for convective clustering in each phase. In the developing phase, in addition to previously emphasized processes such as the cold pool‐updraft interaction and moisture‐convection feedbacks, our results show that the vertical wind shear in the lower free troposphere is a critical factor for convective clustering. Stronger lower free‐tropospheric wind shear increases the entrainment of environmental air into updrafts and prevents convective clouds from being omnipresent. This result suggests that stronger vertical wind shear in the lower free troposphere can help spatially organize the convection, even for non–squall‐line‐type convective systems. In the decaying phase, the cold pool‐updraft interaction becomes less effective in aggregating convective clouds because the boundary layer is widely cooled by stratiform precipitation. Instead, the mesoscale downdraft driven by the stratiform precipitation becomes the dominant factor to maintain the relatively aggregated convection. Additionally, removing horizontal variations in radiative heating has no impact on convective clustering on this 2‐day time scale, even in the decaying phase when stratiform clouds are widespread. The implication of these results for improving the representation of mesoscale convective organization in convection schemes is discussed.

In observations, tropical cyclones with cyclonically rotating elliptical eyewalls are often characterized by wave number 2 (WN2) deep convection located at the edge of the major axis. A simple modeling framework is used to understand this phenomenon, where a nondivergent barotropic model (NBM) is employed to represent the elliptical vortex in the free atmosphere, and an asymmetric slab boundary layer (SBL) model is used to simulate the frictional boundary layer (BL) underneath the free atmosphere. The interaction is one way in that the overlying cyclonic flow drives the BL, but the BL pumping does not feed back to the overlying flow. The nonlinear‐balanced pressure field from the NBM drives the winds in the SBL model, which then causes BL convergence and pumping near the eyewall. The strong updrafts at the edge of the major axis for the elliptic vortex in the BL are induced by the larger convergent radial wind from the asymmetric distribution of the pressure fields of the free atmosphere with noncircular vortex. The large radial inflow maintains the supergradient wind at the edge of the elliptical vortex. The results emphasize the cyclonic rotation of the WN2 feature of strong updrafts at the top of the BL from the local shock‐like BL radial wind structure. Similar radial profiles and strong BL top updrafts occur at the edges of higher‐order polygonal eyewalls with the magnitude of the peak updraft decreasing as the wave number structure of the vortex increases.

Lightning plays a unique role in the Earth system. From a weather point of view, lightning flashes associated with individual thunderstorms are of serious concern for public safety (Curran et al., 2000). On a global scale, lightning is a key component of the climate system. Lightning has profound impacts on the atmospheric composition through lightning-produced nitrogen oxides (LNOx). Studies have suggested that LNOx can significantly affect the ozone concentration in the troposphere and the stratosphere, hence influence Earth's climate through altering the radiation budget (S. C. Liu et al., 1987;Schumann & Huntrieser, 2007). Lightning is also known to be one of the major causes of wildfires (Ahrens, 2013), providing a source of biomass burning aerosols and thereby influencing the carbon cycle (Price & Rind, 1994b).Given the known impacts of lightning on the Earth system, Earth System Models (ESMs) need to accurately represent lightning and its future change to yield reliable future climate projections. Unfortunately, however, the future projection of lightning frequency remains largely uncertain (e.g.,