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

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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 the autocorrelation lengths of the 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 classifications used for the observations, we will identify which model MPCs parameterisations need to be improved.