Airborne observations of riming in arctic mixed-phase clouds during HALO-(AC)3

Ice crystal formation and growth processes in mixed-phase clouds (MPCs) are not sufficiently understood leading to uncertainties of atmospheric models in representing MPCs. One of these processes is riming, which occurs when liquid water droplets freeze onto ice crystals. Riming plays a key role in precipitation formation in MPCs by efficiently converting liquid cloud water into ice. However, riming is challenging to observe directly and there are only few studies quantifying riming in Arctic MPCs.

In this study, we derive the normalized rime mass 𝑀 to quantify riming. We use airborne data collected during the (AC)³  field campaign HALO-(AC)³  performed in 2022. For this campaign, two aircraft were flying in formation collecting closely spatially collocated and almost simultaneous in situ and remote sensing observations. We aim to quantify 𝑀 by two methods. First, we present an Optimal Estimation algorithm to retrieve 𝑀 from measured radar reflectivities. We find 𝑀 by matching measured with simulated radar reflectivities 𝑍_𝑒obtained from in situ particle number concentration observations. As forward operators, we use the Passive and Active Microwave radiative TRAnsfer tool (PAMTRA) and empirical relationships of 𝑀 and particle properties. The latter are derived via aggregation and riming model calculations. Second, we derive 𝑀 from in situ measured particle shape. We calculate the complexity 𝜒 of in situ measured particles, which relates particle perimeter to area. We then derive 𝑀 from empirical relationships that were again obtained from synthetic particles. We compare the obtained 𝑀 derived by both methods and evaluate the occurrence of riming in terms of meteorological conditions and macrophysical cloud properties to understand external drivers and variability of riming. This will lead to a better understanding of riming and thereby helps to improve modelling of this important arctic MPC process.