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This study presents the results of the ICE GENESIS 2021 Swiss Jura Flight Campaign in a way that is readily usable for ice accretion modelling and aims at improving the description of snow particles for model inputs. 2D images from two OAP probes, namely 2D-S and PIP, have been used to extract 3D shape parameters in the oblate spheroid assumption, as there are the diameter of the sphere of equivalent volume as ellipsoid, sphericity, orthogonal sphericity, and an estimation of bulk density of individual ice crystals through a mass-geometry parametrization. Innovative shape recognition algorithm, based on Convolutional Neural Network, has been used to identify ice crystal shapes based on these images and produce shape-specific mass particle size distributions to describe cloud ice content quantitatively in details. 3D shape descriptors and bulk density have been extracted for all the data collected in cloud environments described in the regulation as icing conditions.

In response to the ice crystal icing hazard identified twenty years ago, aviation industry, regulation authorities, and research centers joined forces into the HAIC-HIWC international collaboration launched in 2012. Two flight campaigns were conducted in the high ice water content areas of tropical mesoscale convective systems in order to characterize this environment conducive to ice crystal icing. Statistics on cloud microphysical properties, such as Ice Water Content (IWC) or Mass Median Diameter (MMD), derived from the dataset of in situ measurements are now being used to support icing certification rulemaking and anti-icing systems design (engine and air data probe) activities. This technical paper focuses on methodological aspects of the derivation of MMD. MMD are estimated from PSD and IWC using a multistep process in which the mass retrieval method is a critical step.