Abstract
We present ground-based in situ snow measurements in Kiruna, Sweden, using the ground-based in situ instrument Dual Ice Crystal Imager (D-ICI). D-ICI records dual high-resolution images from above and from the side of falling natural snow crystals and other hydrometeors with particle sizes ranging from 50 μ m to 4 mm. The images are from multiple snowfall seasons during the winters of 2014/2015 to 2018/2019, which span from the beginning of November to the middle of May. From our images, the microphysical properties of individual particles, such as particle size, cross-sectional area, area ratio, aspect ratio, and shape, can be determined. We present an updated classification scheme, which comprises a total of 135 unique shapes, including 34 new snow crystal shapes. This is useful for other studies that are using previous shape classification schemes, in particular the widely used Magono–Lee classification. To facilitate the study of the shape dependence of the microphysical properties, we further sort these individual particle shapes into 15 different shape groups. Relationships between the microphysical properties are determined for each of these shape groups.
Highlights
The shape of ice particles is an important characteristic that affects the radiative impact of clouds.Accurate knowledge of the microphysical properties of clouds, including particle shape, is important in order to assure accurate cloud parameterizations in climate and meteorological forecast models, e.g., that presented in References [1,2]
This paper presents data and images falling snow otherthat hydrometeors in the size range from 50 μm to 4 mm by the Dual Ice Crystal Imager (D-ICI) during snowfall events in Kiruna, Sweden from 2014 to 2019
WeWe separated into many manydifferent differentshapes shapes following largely the Magono–Lee separatedthe the particles particles into following largely the Magono–Lee classification scheme
Summary
The shape of ice particles is an important characteristic that affects the radiative impact of clouds. Accurate knowledge of the microphysical properties of clouds, including particle shape, is important in order to assure accurate cloud parameterizations in climate and meteorological forecast models, e.g., that presented in References [1,2]. In order to retrieve quantities such as cloud water path or cloud effective radius, the underlying assumptions of particle shape, size, and distribution have a massive impact on the retrieval itself [3,4]. The unavoidable sensitivity of satellite retrievals to assumptions on ice particle properties, such as particle size, area, and the shape of snow crystals, is one of the dominating sources of uncertainties in cloud retrievals [5].
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