Research Highlights

Accurately predicting impacts from storms depends on accurately simulating their growth as a function of atmospheric conditions. Using a model setup like those used for operational forecasting, results show that total storm rainfall over a large area is reasonably predicted. However, heavy rain rates were too frequent and light rain rates were too infrequent at a local scale when compared to observations, meaning the balance between rainfall frequency and intensity is incorrectly predicted. This is caused by an excessive number of simulated storms, a model bias that worsens as the atmosphere becomes more stable. Increasing model resolution to better resolve storm circulations does not reduce these biases, indicating model representation of precipitation formation and growth in storms requires improvement.

Detailed data of what is in the atmosphere is often very complex, containing thousands of chemicals without known identities or properties. By developing new automated tools for analyzing certain types of data, this research will substantially improve the ability to make sense of these data and extract new details about the composition of the atmosphere.

Aerosols are known to affect cloud properties, including their formation, growth, and precipitation, which in turn influences climate over long time scales. Aerosol-cloud-interactions (ACI) depend on how their properties change together, yet few measurements capture this variability, especially in the presence of convective cloud populations that can be observed routinely by satellites. Models are often challenged because they assume aerosols are constant, which potentially leads to erroneous estimates of the impact of ACI. Furthermore, ACI pathways in convective clouds are complex and remain highly uncertain.   To address the data gap and better understand the interactions of convective clouds and the surrounding environment, extensive in situ and remote-sensing measurements were collected during the Cloud, Aerosol, and Complex Terrain Interactions (CACTI) field campaign conducted between October 2018 and April 2019 over the Sierras de Córdoba range of central Argentina. The field campaign aimed to understand how convective clouds interact with environmental conditions, thermodynamics, aerosols, and surface properties. In contrast with previous studies that focused on clouds, this study describes measurements of aerosol number, size, composition, mixing state, and cloud condensation nuclei collected during CACTI.