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In the framework of the European ICE GENESIS project (https://www.ice-genesis.eu/), a field experiment was conducted in the Swiss Jura in January 2021 in order to characterize snow microphysical properties and document snow conditions for aviation industry purposes. Complementary to companion papers reporting on snow properties, this study presents an investigation on mixed-phase conditions sampled during the ICE GENESIS field campaign. Using in situ measurement of the liquid and total water content, the ice mass fraction is calculated and serves as a criteria to identify mixed-phase conditions. In the end, mixed phase conditions were identified in almost 30 % of the 3800 km long cloud samples included in the ICE GENESIS dataset. The data suggests that the occurrence of mixed-phase does not clearly depend on temperature in the 0 to -10 °C range, but varies significantly from one cloud system to another.

The PLANET System was used for real-time satellite data transmission during the HAIC-HIWC Darwin field campaign (January to March 2014). The basic system was initially providing aircraft tracking, chat, weather text messages (METAR, TAF, etc.), and aeronautical information (NOTAMs) in a standalone application. In the framework of the HAIC project, many improvements were made in order to fulfill requirements of the onboard and ground science teams for the field campaign. The aim of this paper is to present the main improvements of the system that were implemented for the Darwin field campaign. New features of the system are related to the hardware component, the communication protocol, weather and tracking display, geomarkers on the map, and image processing and compression before onboard transfer.

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.