EGU22-13359
https://doi.org/10.5194/egusphere-egu22-13359
EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Retrieving riming in arctic mixed phase clouds from collocated remote sensing and in situ aircraft measurements during ACLOUD 

Nina Maherndl1, Maximilian Maahn1, Frederic Tridon2, and Regis Dupuy2
Nina Maherndl et al.
  • 1University of Leipzig, Leipzig, Germany, nina.maherndl@uni-leipzig.de
  • 2University Clermont Auvergne, Clermont-Ferrand, France,f.tridon@opgc.fr

Ice crystal formation and growth processes in mixed-phase clouds (MPCs) are not sufficiently understood. This leads to uncertainties of atmospheric models in representing MPCs. This presentation is centered around riming, which occurs when liquid water droplets freeze onto ice crystals. While it is challenging to observe riming directly, we retrieve a proxy for riming from airborne radar measurements using data collected during the (AC )3 aircraft campaign ACLOUD performed in 2017. For this campaign, two closely collocated aircraft were flying in formation for obtaining collocated in situ and remote sensing observations. We aim to quantify the normalized riming fraction 𝑀 by matching measured to simulated radar reflectivities 𝑍𝑒 . For the latter we use the Passive and Active Microwave radiative TRAnsfer tool (PAMTRA) to calculate 𝑍𝑒 from the in situ observed particle size distributions. Liquid droplets are assumed to be spheres and Mie scattering is applied, while we use the self-similar Rayleigh Gans approximation (SSRGA) for ice crystals. We present an Optimal Estimation algorithm to obtain ice crystal mass size - as well as SSRGA parameters from measured 𝑍𝑒 and in situ parameters. We exploit the fact that mass size and SSRGA parameters depend on 𝑀. We evaluate including empirical relationships derived via model calculations done by an aggregation and riming model as forward operators in the algorithm. Also we use the model calculations directly to restrict the prior information. We validate the obtained 𝑀 values by looking at in situ ice crystal images for selected time periods. We compare our findings to macrophysical cloud properties and meteorological conditions to understand external drivers and variability of riming. This will lead to a better understanding of riming as a key process occurring in arctic MPCs.

How to cite: Maherndl, N., Maahn, M., Tridon, F., and Dupuy, R.: Retrieving riming in arctic mixed phase clouds from collocated remote sensing and in situ aircraft measurements during ACLOUD , EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-13359, https://doi.org/10.5194/egusphere-egu22-13359, 2022.

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