Environments Associated with Hail Production in Organized Multicellular Systems in Subtropical South America

Subtropical South America (SSA) is home to some of the most intense convection in the world as determined by depth of convection, size of organized convective systems, lightning flash rates, and satellite-estimated hail production. Unsurprisingly, hail frequently causes over US$100 million a year in damage annually in Argentina alone. Yet, understanding of how hailfall is produced in SSA is highly limited. Recent research using TRMM-based hail detection and convective mode classification has suggested these SSA hail-producing storms differ significantly from their counterparts in the U.S.: they remain close to the nearby elevated terrain, suggesting an orographic link, and they tend to be organized or linear multicellular convective modes as opposed to the discrete supercells that more commonly produce hail in the U.S. The availability of GPM data over South American now offers the opportunity to revisit recently established SSA hail and convective mode climatologies, using the finer spatial resolution of the GPM data along with recent advancements in satellite-based hail detection algorithms. The addition of high-resolution GOES data offers an opportunity to directly interrogate convective updraft characteristics.

This study updates recent TRMM-based SSA hail climatologies using the Bang and Cecil (2019) hail detection algorithm for GPM. Similar annual and diurnal cycle frequencies were found, with peak hail detection noted in the overnight hours. Precipitation features with a high probability of hail were still found to remain closer to orographical features, and were almost entirely classified as organized multicellular systems. All of these results, while similar to TRMM-based studies, are contrary to more typical U.S. hail-producing storms, suggesting potential fundamental environmental differences.

To explore these environmental differences, hourly ERA5 reanalysis data were obtained for each precipitation feature the hail detection algorithm was confident either did, or did not, contain hail. Data from an hour prior to storm initiation through to storm dissipation were used to interrogate both preconvective and evolving environmental conditions. Storm duration of each precipitation feature was determined using GOES ABI 10.3-mm brightness temperatures (T_b). GOES T_b objects associated with each GPM hail detection were identified in the data by finding contiguous regions of T_b less than 210K at the time of detection, and then expanded both forward and backward in time. The existence of these objects determined storm duration. Resulting instability and shear fields in the reanalysis data, following the global hail environmental analysis of Zhou et al. (2021), are mined for common environmental patterns both prior to convective initiation and over the duration of each system.