Understanding the History of Two Complex Ice Crystal Habits Deduced From a Holographic Imager

Pasquier, Julie Thérèse; Henneberger, Jan; Korolev, Alexei; Ramelli, Fabiola; Wieder, Jörg; Lauber, Annika; Li, G.; David, Robert O.; Carlsen, Tim; Gierens, Rosa; Maturilli, Marion; Lohmann, Ulrike

doi:10.1029/2022gl100247

Cited by 2 publications

(2 citation statements)

References 64 publications

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“…Following this initial categorization, each ice crystal was classified into one of seven basic habits: 'column', 'plate', 'lollipop' (Pasquier et al, 2022a), 'Columns on Capped-Columns' (CPC, Pasquier et al 2023), 'irregular', 'frozen droplets', and 'small'.…”

Section: Data Descriptionmentioning

confidence: 99%

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IceDetectNet: A rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme

Zhang,

Li,

Ramelli

et al. 2024

Preprint

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Abstract. The shape of ice crystals affects their radiative properties, growth rate, fall speed, and collision efficiency and thus, plays a significant role in cloud optical properties and precipitation formation. Ambient conditions like temperature and humidity determine the basic habit of ice crystals, while microphysical processes such as riming and aggregation further shape them, resulting in a diverse set of ice crystal shapes and densities. Current classification algorithms face two major challenges: (1) ice crystals are often classified as a whole (on the image scale), necessitating identification of the dominant component of aggregated ice crystals, and (2) single-label classifications lead to information loss because of the compromise between basic habit and microphysical process information. To address these limitations, here we present a two-pronged solution: a rotated object detection algorithm (IceDetectNet) that classifies each component of an aggregated ice crystal individually, and a multi-label classification scheme that considers both basic habits and physical processes simultaneously. IceDetectNet was trained and tested on two independent datasets obtained by a holographic imager during the NASCENT campaign in Ny-Ålesund, Svalbard, in November 2019 and April 2020. The algorithm correctly classifies 92 % of the ice crystals as either aggregate or non-aggregate and achieved an overall accuracy of 86 % for basic habits and 82 % for microphysical processes classification. On the component scale, IceDetectNet demonstrated high detection and classification accuracy across all sizes, indicating its ability to effectively classify individual components of aggregated ice crystals. Furthermore, the algorithm demonstrated good generalization ability by classifying ice crystals from an independent test dataset with overall accuracies above 70 %. IceDetectNet can provide a deeper understanding of ice crystal shapes, leading to better estimates of ice crystal mass, fall velocity, and radiative properties and thus, has the potential to improve precipitation forecasts and climate projections.

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Section: Data Descriptionmentioning

confidence: 99%

“…Columns on capped-columns (CPC) * Ice crystals that contain both columnar and plate-like features, often resembling an 'H'. These ice crystals are formed when growing in both the column and plate temperature regimes (Pasquier et al, 2023).…”

Section: Propertymentioning

confidence: 99%

IceDetectNet: A rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme

Zhang,

Li,

Ramelli

et al. 2024

Preprint

View full text Add to dashboard Cite

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Introducing the Video In Situ Snowfall Sensor (VISSS)

Maahn,

Moisseev,

Steinke

et al. 2024

Atmos. Meas. Tech.

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Abstract. The open-source Video In Situ Snowfall Sensor (VISSS) is introduced as a novel instrument for the characterization of particle shape and size in snowfall. The VISSS consists of two cameras with LED backlights and telecentric lenses that allow accurate sizing and combine a large observation volume with relatively high pixel resolution and a design that limits wind disturbance. VISSS data products include various particle properties such as maximum extent, cross-sectional area, perimeter, complexity, and sedimentation velocity. Initial analysis shows that the VISSS provides robust statistics based on up to 10 000 unique particle observations per minute. Comparison of the VISSS with the collocated PIP (Precipitation Imaging Package) and Parsivel instruments at Hyytiälä, Finland, shows excellent agreement with the Parsivel but reveals some differences for the PIP that are likely related to PIP data processing and limitations of the PIP with respect to observing smaller particles. The open-source nature of the VISSS hardware plans, data acquisition software, and data processing libraries invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.

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Understanding the History of Two Complex Ice Crystal Habits Deduced From a Holographic Imager

Cited by 2 publications

References 64 publications

IceDetectNet: A rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme

IceDetectNet: A rotated object detection algorithm for classifying components of aggregated ice crystals with a multi-label classification scheme

Introducing the Video In Situ Snowfall Sensor (VISSS)

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