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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 (10.5285/B1211AD185E24B488D41DD98F957506C;) 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. [ABSTRACT FROM AUTHOR]

Cloud droplet number concentration (Nd) is of central importance to observation- based estimates of aerosol indirect effects, being used to quantify both the cloud sensitivity to aerosol and the base state of the cloud. However, the derivation of Nd from satellite data depends on a number of assumptions about the cloud and the accuracy of the retrievals of the cloud properties from which it is derived, making it prone to systematic biases. A number of sampling strategies have been proposed to address these biases by selecting the most accurate Nd retrievals in the satellite data. This work compares the impact of these strategies on the accuracy of the satellite retrieved Nd, using a selection of insitu measurements. In stratocumulus regions, the MODIS Nd retrieval is able to achieve a high precision (r2 of 0.5-0.8). This is lower in other cloud regimes, but can be increased by appropriate sampling choices. Although the Nd sampling can have significant effects on the Nd climatology, it produces only a 20 % variation in the implied radiative forcing from aerosol-cloud interactions, with the choice of aerosol proxy driving the overall uncertainty. The results are summarised into recommendations for using MODIS Nd products and appropriate sampling. [ABSTRACT FROM AUTHOR]

We present the In-situ/Remote sensing aerosol Retrieval Algorithm (IRRA) that combines airborne in-situ and lidar remote sensing data to retrieve verti-cal profiles of ambient aerosol optical, microphysical and hygroscopic proper-ties, employing the ISORROPIA II model for acquiring the hygroscopic growth. Here we apply the algorithm on data collected from the Facility for Airborne Atmospheric Measurements (FAAM) BAe-146 research aircraft dur-ing the ACEMED campaign in Eastern Mediterranean: vertical profiles of aerosol microphysical properties have been derived successfully for an aged smoke plume near the city of Thessaloniki with typical lidar ratios of ~60-80 & sr at 532 & nm, along with single scattering albedos of ~ & 0.9-0.95 at 550 & nm. The aerosol layer reaches the 3.5 & km with aerosol optical depth at ~ & 0.4 at 532 & nm. Our analysis shows that the smoke particles are highly hydrated above land, with 55 & % and 80 & % water volume content for ambient relative humidity of 80 & % and 90 & %, respectively. The proposed methodology is highly advantageous for aerosol characterization in humid conditions and can find valuable appli-cations in aerosol-cloud interaction schemes. Moreover, it can be used for the validation of active space-borne sensors, as is demonstrated here for the case of CALIPSO. [ABSTRACT FROM AUTHOR]