B08: Characterising the spatial variability of ice water content in and below mixed-phase clouds

The processes determining spatial variability of ice water content (IWC) in mixed-phase clouds (MPCs) are not sufficiently understood. Therefore, we propose a project targeted at understanding and quantifying these processes. While it is challenging to observe MPC processes directly, we will advance techniques for quantifying IWC and snowfall rate (SR) with low uncertainty from airborne radar measurements so that we are able to observe fingerprints of the dominating processes. We will use data collected during the (AC)³ aircraft campaigns with a particular focus on the ACLOUD campaign performed in 2017, and the upcoming HALO-(AC)³ campaign planned within the current phase of (AC)³. For these campaigns, at least two closely collocated aircraft are flying in formation for obtaining collocated in-situ and remote sensing observations. We will use these rare data collected during tandem flights to develop a seamless Bayesian Optimal Estimation retrieval for obtaining IWC and SR from combined radar and in situ measurements along a flight track ’curtain’. We will develop a novel retrieval approach where the in situ data are exploited not only for the observation point where they were obtained, but for the whole curtain by scaling their weight proportional to autocorrelation lengths of microphysical properties. By this, we can consider how the information content of the in situ instruments is reduced with increasing distance between in situ and remote sensing observation volume. Such a retrieval can combine all available information from radar and in situ observations and will close an important gap in our ability to observe the vertical and horizontal spatial variability of IWC in clouds with high accuracy and high spatial resolution. Based on the improved observations, we will link the observed IWC variability to other microphysical and macrophysical cloud properties (among others, dominating particle growth process, cloud type, liquid water content, cloud depth, cloud top phase variability, surface coupling). A particular emphasis will be put on vertical IWC variability and the resulting impact on precipitation mass fluxes. For this, we can rely on the extensive supporting aircraft data sets, but also on ground-based observations in Ny-Ålesund and during the PASCAL campaign. By this, we will identify the processes most relevant for IWC sources and sinks as well as the spatial scales on which these processes are active. Model simulations using ICON-LEM will be analysed for quantifying differences in the representation of IWC. By comparing to the same microphysical and  macrophysical cloud classification used in the observations, we will identify which model MPCs parameterisations need to be improved.


Spatial variability of ice water content (IWC) in and below MPCs is regulated by the spatial variability of surface properties and cloud top thermodynamic phase in addition to macrophysical properties such as cloud type, liquid water path, cloud depth, moisture availability and surface coupling. Correlating these properties to IWC variability will allow to identify the dominating processes.

In testing the hypothesis, we address the following overarching questions:

  • How can we combine in situ and remote sensing aircraft measurements to obtain spatial variability of IWC in and below clouds with minimal uncertainties?
  • What determines the vertical and horizontal gradients of IWC in the Arctic atmosphere and how do these gradients differ depending on the observed dominant ice formation processes and boundary conditions such as surface fluxes, cloud phase variability at cloud top or liquid water path?
  • How do the observed IWC gradients and ice mass fluxes differ from those present in the ICON-LEM model for similar cloud types and forcing?

Role within (AC)³



Nina Maherndl


University of Leipzig
Leipzig Institute for Meteorology (LIM)
Stephanstr. 3
04103 Leipzig


++49 (0) 341 97 32880



Dr. Maximilian Maahn

Principal Investigator

University of Leipzig
Leipzig Institute for Meteorology (LIM)
Stephanstr. 3
04103 Leipzig


++49 (0) 341 97 32853




Maherndl, N., M. Maahn, M. Moser, J. Lucke, M. Mech, N. Risse, and I. Schirmacher, 2023: Quantifying riming from airborne data during HALO-(AC)³. Atmos. Meas. Tech., submitted

Maherndl, N., Maahn, M., Tridon, F., and Dupuy, R., 2022: 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.

Maherndl, N.; Maahn, M.; Tridon, F.; Leinonen, J.; Ori, D. & Kneifel, S., 2023: Data set of simulated rimed aggregates for “A riming-dependent parameterization of scattering by snowflakes using the self-similar Rayleigh-Gans approximation”, Zenodo, https://doi.org/10.5281/zenodo.7757034


Maahn, M.; Moisseev, D.; Steinke, I.; Maherndl, N. & Shupe, M. D., 2023: Introducing the Video In Situ Snowfall Sensor (VISSS), EGUsphere, 2023, 1-27, https://doi.org/10.5194/egusphere-2023-655, [preprint]

Maherndl, N.; Maahn, M.; Moser, M.; Lucke, J.; Mech, M. & Risse, N., 2023: Airborne observations of riming in arctic mixed-phase clouds during HALO-(AC)3, EGU General Assembly 2023, 24–28 Apr 2023, EGU23-5000, https://doi.org/10.5194/egusphere-egu23-5000

Maherndl, N.; Maahn, M.; Tridon, F.; Leinonen, J.; Ori, D. & Kneifel, S., 2023: A riming-dependent parameterization of scattering by snowflakes using the self-similar Rayleigh–Gans Approximation, Q.J.R. Meteorol. Soc., [submitted to Q.J.R. Meteorol. Soc.]

Maahn, M., 2023: Video In Situ Snowfall Sensor (VISSS) data processing library V2023.1.6, Zenodo, https://doi.org/10.5281/zenodo.7650394

Maahn, M., 2023: Video In Situ Snowfall Sensor (VISSS) data acquisition software V0.3.1, Zenodo, https://doi.org/10.5281/zenodo.7640801

Maahn, M.; Haseneder-Lind, R. & Krobot, P., 2023: Hardware Design of the Video In Situ Snowfall Sensor v2 (VISSS2), Zenodo, https://doi.org/10.5281/zenodo.7640821

Wendisch, M.; Brückner, M.; Crewell, S.; Ehrlich, A.; Notholt, J.; Lüpkes, C.; Macke, A.; Burrows, J. P.; Rinke, A.; Quaas, J.; Maturilli, M.; Schemann, V.; Shupe, M. D.; Akansu, E. F.; Barrientos-Velasco, C.; Bärfuss, K.; Blechschmidt, A.-M.; Block, K.; Bougoudis, I.; Bozem, H.; Böckmann, C.; Bracher, A.; Bresson, H.; Bretschneider, L.; Buschmann, M.; Chechin, D. G.; Chylik, J.; Dahlke, S.; Deneke, H.; Dethloff, K.; Donth, T.; Dorn, W.; Dupuy, R.; Ebell, K.; Egerer, U.; Engelmann, R.; Eppers, O.; Gerdes, R.; Gierens, R.; Gorodetskaya, I. V.; Gottschalk, M.; Griesche, H.; Gryanik, V. M.; Handorf, D.; Harm-Altstädter, B.; Hartmann, J.; Hartmann, M.; Heinold, B.; Herber, A.; Herrmann, H.; Heygster, G.; Höschel, I.; Hofmann, Z.; Hölemann, J.; Hünerbein, A.; Jafariserajehlou, S.; Jäkel, E.; Jacobi, C.; Janout, M.; Jansen, F.; Jourdan, O.; Jurányi, Z.; Kalesse-Los, H.; Kanzow, T.; Käthner, R.; Kliesch, L. L.; Klingebiel, M.; Knudsen, E. M.; Kovács, T.; Körtke, W.; Krampe, D.; Kretzschmar, J.; Kreyling, D.; Kulla, B.; Kunkel, D.; Lampert, A.; Lauer, M.; Lelli, L.; von Lerber, A.; Linke, O.; Löhnert, U.; Lonardi, M.; Losa, S. N.; Losch, M.; Maahn, M.; Mech, M.; Mei, L.; Mertes, S.; Metzner, E.; Mewes, D.; Michaelis, J.; Mioche, G.; Moser, M.; Nakoudi, K.; Neggers, R.; Neuber, R.; Nomokonova, T.; Oelker, J.; Papakonstantinou-Presvelou, I.; Pätzold, F.; Pefanis, V.; Pohl, C.; van Pinxteren, M.; Radovan, A.; Rhein, M.; Rex, M.; Richter, A.; Risse, N.; Ritter, C.; Rostosky, P.; Rozanov, V. V.; Donoso, E. R.; Saavedra-Garfias, P.; Salzmann, M.; Schacht, J.; Schäfer, M.; Schneider, J.; Schnierstein, N.; Seifert, P.; Seo, S.; Siebert, H.; Soppa, M. A.; Spreen, G.; Stachlewska, I. S.; Stapf, J.; Stratmann, F.; Tegen, I.; Viceto, C.; Voigt, C.; Vountas, M.; Walbröl, A.; Walter, M.; Wehner, B.; Wex, H.; Willmes, S.; Zanatta, M. & Zeppenfeld, S., 2023: Atmospheric and Surface Processes, and Feedback Mechanisms Determining Arctic Amplification: A Review of First Results and Prospects of the (AC)³ Project, Bull. Am. Meteorol. Soc., American Meteorological Society, 104, E208–E242, https://doi.org/10.1175/bams-d-21-0218.1


Maahn, M., Goren, T., Shupe, M. D., and de Boer, G., 2021. Liquid containing clouds at the North Slope of Alaska demonstrate sensitivity to local industrial aerosol emissions. Geophys. Res. Lett., 48, e2021GL094307. https://doi.org/10.1029/2021GL094307

Project Poster