Dependence of Convective Cloud Microphysical Properties on Environmental Conditions during the TRACER and ESCAPE Field Campaigns: A Synergistic Approach of Observations, Machine Learning, and Parcel Models

The sensitivity of convective clouds to aerosols and their interactions with environment, combined with limited observational constraints in parameterizations, introduces significant uncertainties in atmospheric models. This study investigates the dependence of convective cloud microphysical properties on environmental conditions using a synergistic approach that combines unique observations from the Tracking Aerosol Convection Interactions Experiment (TRACER) and Experiment of Sea Breeze Convection, Aerosols, Precipitation, and Environment (ESCAPE) field campaigns, machine learning techniques, and parcel model simulations with a superdroplet microphysics scheme. A random forest algorithm identifies in situ vertical velocity w, temperature T, and surface fine-mode aerosol mass concentration as the three most important environmental conditions influencing cloud properties including liquid water content (LWC), number concentration for particles with D_max < 50 μm (N_c,<50), 50 μm ≤ D_max ≤ 3000 μm(N_c,50–3000), and droplet effective diameter D_e. The results show that LWC, N_c,<50,andN_c,50–3000 significantly increase with w in updrafts. Across w bins, as T decreases, LWC, D_e,andN_c,50–3000 increase, while N_c,<50 decreases, which are closely linked to the distance above cloud bases. Warmer cloud bases yield higher LWC, greater N_c,50–3000, and smaller N_c,<50, while polluted environments produce greater N_c,<50. Parcel model simulations successfully replicate these observed dependencies. The simulation results indicate that warmer cloud bases enhance condensation generating larger droplets, and differences in droplet sizes are then amplified through collision–coalescence, resulting in a greater N_c,50–3000. Polluted conditions result in a greater N_c,<50 primarily due to enhanced cloud condensation nuclei activation despite increased collision–coalescence rates compared to pristine conditions. This study provides observed quantitative patterns characterizing cloud microphysical properties as a function of key environmental parameters, offering valuable constraints for improving physics parameterizations and numerical models. SIGNIFICANCE STATEMENT: This study explores how microscale characteristics of convective clouds change under varying meteorological and aerosol conditions. By analyzing data from two major field campaigns, combined with advanced machine learning and physics-based models, we obtained the quantitative patterns linking cloud microscale characteristics with key environmental factors. We also revealed the mechanisms by which warmer cloud bases produce larger droplets, while polluted environments lead to an increase in smaller droplets. These insights provide critical guidance for improving weather and climate models, helping to reduce forecast uncertainties. This study also highlights the value of integrating observations, machine learning, and numerical modeling to advance our understanding of cloud physics.