Causal Estimation of Aerosol Effects on Rainfall Core Depth in Isolated Deep Convective Clouds: Findings from TRACER

This study aims to investigate the causal effects of aerosols on the depth of the rainfall cores of deep convective clouds (DCCs) observed during the summer months of 2022 in the Houston region. We apply a causal machine learning model, g-computation, to a comprehensive dataset collected during the ARM TRACER field campaign to quantify the aerosol-DCC interactions. The focus is placed on isolated DCCs observed under anticyclonic weather conditions with low wind shear and moderate humidity. DCC cases within 20 to 60 km from the ARM M1 site are considered in the study to investigate the sensitivity to horizontal resolution that may be used to study similar topics with regional to climate models. The analyses find that polluted environments can lead to an increase in 30-dBZ echo-top heights (ETH) in DCCs, with maximum magnitudes reaching 1 km, after accounting for covarying meteorological variables. Note that our findings are specific to the geographic and climatic conditions of the Houston region and may not be generalizable to other regions with different environmental conditions.