Novel Strategies for Radar Scanning and Convection Tracking to Study Isolated Deep Convection

Deep convective clouds (DCCs) redistribute energy and moisture in Earth’s atmosphere. Their occurrence and intensity depend on environmental conditions with the potential for aerosol invigoration effects. Isolating the aerosol and environmental effects remains challenging due to limited observations of DCC properties across various environmental regimes. Novel observations from the Atmospheric Radiation Measurement (ARM) user facility are used to track the evolution of DCCs and study cloud processes across different lifecycle stages. A convection tracking algorithm (Tracking and Object-Based Analysis of Clouds, tobac) is applied to radar observations from Green Ocean Amazon Field Experiment (GoAmazon2014/15) in Brazil and the TRacking Aerosol Convection interactions ExpeRiment (TRACER) in Texas.

During GoAmazon2014/15, vertical profiles of Z and vertical velocity (w) from a Radar Wind Profiler were used to examine the lifecycle of updraft/downdraft strength and mass flux (MF). Mature DCCs had the strongest updrafts and highest Z and net MF across lifecycle stages. DCCs were more frequent during the wet season but the dry season had the strongest, fastest moving, and largest DCCs. Stronger convection and greater upward MF were observed during the first (second) half of DCC lifetime during the dry (wet) season. During TRACER, a 2^nd generation C-band Scanning ARM Precipitation Radar (CSAPR2) performed RHI and PPI scans based on a machine learning-based algorithm guided by real-time cell tracking. The CSAPR2 performed multiple scans within every 2 minutes to track individual convective cells. Tracking output from CSAPR2 is placed in context of tracking output of NEXRAD retrievals with lower temporal resolutions but greater spatial coverage. Convection tracking procedures and output are carefully examined to guide the future development of convection tracking.