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Retrieval of ice crystal shapes in mesoscale convective systems, from in-situ measurements

Urheber*innen

Bazantay,  Clément
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Schwarzenboeck,  Alfons
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Jaffeux,  Louis
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Febvre,  Guy
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

Bouniol,  Dominique
IUGG 2023, General Assemblies, 1 General, International Union of Geodesy and Geophysics (IUGG), External Organizations;

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Zitation

Bazantay, C., Schwarzenboeck, A., Jaffeux, L., Febvre, G., Bouniol, D. (2023): Retrieval of ice crystal shapes in mesoscale convective systems, from in-situ measurements, XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG) (Berlin 2023).
https://doi.org/10.57757/IUGG23-0820


Zitierlink: https://gfzpublic.gfz-potsdam.de/pubman/item/item_5016659
Zusammenfassung
Individual crystal imagery (order of magnitude: 100-10000 particles per second) with state-of-art binary optical array probes (OAP, e.g. 2DS and PIP) was continuously improved and operated with complementary cloud instruments to document size, fall speed, mass, and density of ice crystals, also co-existence of supercooled water droplets. However, the full potential of these images still has to be exploited in terms of quantitative ice particle morphological analysis.<pIn this study, we investigate the link between ice crystal morphological shape and associated growth regimes (vapor diffusion, aggregation growth, and riming) which are active in space and time during the cloud life cycle. The study utilizes the dataset from the HAIC-Cayenne 2015 flight campaign with corresponding satellite observations. First, we characterize the environmental conditions of sampled mesoscale convective systems (MCSs) to determine the convective, stratiform, and cirriform parts of MCSs. Then, number fraction of morphological classes during these flights are produced, thereby applying the recently developed Convolutional Neural Network tool for sophisticated automatic ice crystal classification (Jaffeux et al., 2022). The statistical analyses on crystal shapes should help in identifying which morphologies are the most abundant during the spatiotemporal evolution of cloud ice. This work should help to better quantify the three principal growth processes’ mass contributions to individual morphological classes.