Near‐Storm Environmental Relationships With Tropical Oceanic Convective Structure Observed During NASA CPEX and CPEX‐AW

Abstract

AbstractDeep tropical oceanic convection (TOC) is a prevailing component of the tropical atmosphere and plays a significant role in modulating global weather and climate. Despite its importance, prediction challenges remain, partly attributed to a lack of understanding of how TOC relates to its near‐storm environments. Prior studies suggest location‐dependent relationships between TOC structure and associated environments, necessitating targeted regional studies. The NASA 2017 Convective Processes Experiment (CPEX) and 2021 CPEX—Aerosols & Winds (CPEX‐AW) field campaigns collected high‐resolution measurements of convective storms and their environments in the Gulf of Mexico, Caribbean, and western Atlantic basins, providing a rare opportunity to investigate near‐storm environmental relationships with 3‐D TOC structure where in situ non‐tropical cyclone‐related deep TOC research is comparatively lacking. Collocated CPEX and CPEX‐AW airborne observations from the multi‐wavelength Airborne Precipitation Radar, Doppler Aerosol Wind Lidar, and dropsondes revealed large near‐storm environmental variability across TOC of similar convective type (i.e., isolated, organized) and within individual convective systems. However, trends still emerged amongst the large environmental variability. Horizontal TOC structure was most consistently linked to planetary boundary layer and mid‐tropospheric near‐storm environments, with organized TOC being associated with generally greater relative humidity (RH) and vertical speed shear than isolated TOC. TOC intensity was linked to upper tropospheric (i.e., above melting level) near‐storm environments, with isolated TOC intensity most consistently associated with upper tropospheric CAPE and organized TOC intensity associated with upper tropospheric RH. Mesoscale low‐level convergence was also linked to greater organized TOC intensity, motivating further research using these unique data sets.