Deep Convective Microphysics Experiment (DCMEX) coordinated aircraft and ground observations: microphysics, aerosol, and dynamics during cumulonimbus development

Cloud feedbacks associated with deep convective anvils remain highly uncertain. In part, this uncertainty arises from a lack of understanding of how microphysical processes influence the cloud radiative effect. In particular, climate models have a poor representation of microphysics processes, thereby encouraging the collection and study of observation data to enable better representation of these processes in models. As such, the Deep Convective Microphysics Experiment (DCMEX) undertook an in situ aircraft and ground-based measurement campaign of New Mexico deep convective clouds during July–August 2022. The campaign coordinated a broad range of instrumentation measuring aerosol, cloud physics, radar, thermodynamics, dynamics, electric fields, and weather. This paper introduces the potential data user to DCMEX observational campaign characteristics, relevant instrument details, and references to more detailed instrument descriptions. Also included is information on the structure and important files in the dataset in order to aid the accessibility of the dataset to new users. Our overview of the campaign cases illustrates the complementary operational observations available and demonstrates the breadth of the campaign cases observed. During the campaign, a wide selection of environmental conditions occurred, ranging from dry, northerly air masses with low wind shear to moist, southerly air masses with high wind shear. This provided a wide range of different convective growth situations. Of 19 flight days, only 2 d lacked the formation of convective cloud. The dataset presented (https://doi.org/10.5285/B1211AD185E24B488D41DD98F957506C; Facility for Airborne Atmospheric Measurements et al., 2024) will help establish a new understanding of processes on the smallest cloud- and aerosol-particle scales and, once combined with operational satellite observations and modelling, can support efforts to reduce the uncertainty of anvil cloud radiative impacts on climate scales.