Accurate predictions of snowfall require good knowledge of the microphysical properties of the snow ice crystals and particles. Shape is an important parameter as it strongly influences the scattering properties of the ice particles, and thus their response to remote sensing techniques such as radar measurements. The fall speed of ice particles is another important parameter for both numerical forecast models as well as representation of ice clouds and snow in climate models, as it is responsible for the rate of removal of ice from these models. The particle mass is also a key quantity as it connects the cloud microphysical properties to radiative properties. The ground-based in-situ instrument Dual Ice Crystal Imager (D-ICI) has been used in Kiruna, Sweden, to determine snow ice particle properties and fall speed simultaneously. D-ICI takes two high-resolution images of the same falling ice particle from two different viewing directions, a top view and a side view. Both images have a pixel resolution of approximately 4 μm/pixel and an optical resolution of approximately 10 μm. The top-view image with its close to vertical viewing direction is used to provide particle size (maximum dimension), cross-sectional area, and shape of the ice particle. This viewing geometry is chosen instead of a horizontal one because shape and size of ice particles as viewed in the vertical direction are more relevant than these properties viewed horizontally as the vertical fall speed is more strongly influenced by the vertically viewed properties. In addition, a comparison with remote sensing instruments that mostly have a vertical or close to vertical viewing geometry is favoured when the particle properties are measured in the same direction. The side-view image with its horizontal viewing direction is used both to aid shape determination as well as to determine fall speed by means of a double exposure. Two bright flashes of a light-emitting diode behind the camera illuminate the falling ice particle and create this double exposure, from which the vertical displacement of the particle is measured and used to determine its fall speed. To add ice particle mass to the data from D-ICI, an empirical relationship between the dimensionless Reynolds and Best numbers can be used. Then, mass of individual ice particles can be derived from measured fall speed, particle size, and cross-sectional area. During four winter seasons, 2014/2015–2017/2018, D-ICI was employed in Kiruna, northern Sweden (67.8N, 20.4E). The dataset presented here has resulted from the D-ICI measurements during this period and consists of the determined snow ice particle properties and the dual images of the same particles. The dataset is the basis of the articles: Vázquez-Martín, S., Kuhn, T., & Eliasson, S. (2021): Shape dependence of snow crystal fall speed, Atmospheric Chemistry and Physics, 21(10), 7545–7565. https://doi.org/10.5194/acp-21-7545-2021 Vázquez-Martín, S., Kuhn, T., & Eliasson, S. (2021). Mass of different snow crystal shapes derived from fall speed measurements, Atmospheric Chemistry and Physics, 21(24), 18669–18688. https://doi.org/10.5194/acp-2021-203 The following description can also be found in the documentation file "Description_of_dataset.txt". Data in Version 2 are identical to the data in Version 1, but they also contain ice particle mass as new variable. The dataset is the basis of the articles: Vázquez-Martín, S., Kuhn, T., & Eliasson, S. (2021): Shape dependence of snow crystal fall speed, Atmospheric Chemistry and Physics, 21(10), 7545–7565. https://doi.org/10.5194/acp-21-7545-2021 Vázquez-Martín, S., Kuhn, T., & Eliasson, S. (2021). Mass of different snow crystal shapes derived from fall speed measurements, Atmospheric Chemistry and Physics, 21(24), 18669–18688. https://doi.org/10.5194/acp-2021-203 ========================== Description of the dataset During four winter seasons, 2014/2015-2017/2018, a Dual Ice Crystal Imager (D-ICI) was employed in Kiruna, northern Sweden (67.8N, 20.4E), to record dual images of naturally falling snow ice particles. For each detected particle, the maximum dimension, cross-sectional area, area ratio, and aspect ratio are determined from the top-view image. The particle fall speed is determined from the side-view image, which is for this purpose exposed twice. Additionally, the particle’s shape is determined by looking at both top-view and side-view images. Details of the methods can be found in Kuhn et al. (2020) and Vázquez-Martín et al. (2020). Furthermore, an empirical relationship between the dimensionless Reynolds and Best numbers is used to derive mass of individual ice particles from measured fall speed, maximum dimension, and cross-sectional area (Vázquez-Martín et al., 2021a). The dataset consists in these particle properties together with the acquired dual images. The particle properties are listed in a text file called “particle_data.txt”. This file also contains the filenames of the dual images. The two filenames refer to a cut-out of the particle in the top view and the entire side-view with the double-exposed falling particle. The filenames are constructed from the date and time (including ms) of acquisition. The cut-out top-view filenames have an additional particle index, which numbers particles consecutively in case several particles were found on the same top-view image. An example cut-out top-view filename is “20141019_070207_020_1.tiff”, an example side-view filename is “20141019_070207_028.tif”. The particle shape in the file “particle_data.txt” denotes a unique numeric shape identifier. Each shape identifier corresponds to one shape from the classification scheme described by Vázquez-Martín et al. (2020) as listed in the file “shapes.txt”. The shape dependence is analysed by Vázquez-Martín et al. (2021b) using 15 different shape groups. Each shape, and consequently each particle, belongs to one of these shape groups as shown in the file “shape_groups.txt”. The two folders “Top” and “Side” contain all top- and side-view image files, respectively. The folder “Top_particle_cutout” contains the cut-out top-view files. References: Kuhn, T. and Vázquez-Martín, S. (2020). Microphysical properties and fall speed measurements of snow ice crystals using the Dual Ice Crystal Imager (D-ICI), Atmospheric Measurement Techniques, 13, 1273–1285, https://doi.org/10.5194/amt-13-1273-2020 Vázquez-Martín, S., Kuhn, T., and Eliasson, S. (2020). Shape Dependence of Falling Snow Crystals’ Microphysical Properties Using an Updated Shape Classification, Applied Sciences, 10, 1163, https://doi.org/10.3390/app10031163 Vázquez-Martín, S., Kuhn, T., & Eliasson, S. (2021a). Mass of different snow crystal shapes derived from fall speed measurements, Atmospheric Chemistry and Physics, 21(24), 18669–18688. https://doi.org/10.5194/acp-2021-203 Vázquez-Martín, S., Kuhn, T., & Eliasson, S. (2021b). Shape dependence of snow crystal fall speed, Atmospheric Chemistry and Physics, 21(10), 7545–7565. https://doi.org/10.5194/acp-21-7545-2021

Instrumentet Dual Ice Crystal Imager (D-ICI) mäter samtidigt mikrofysikaliska egenskaper och fallhastighet av snöpartiklar. D-ICI tar bilder av partiklar från två olika riktningar med en upplösning av 4μm/pixel. Mätningar med D-ICI i Kiruna, norra Sverige (67.8N, 20.4E), under vintrarna 2014/2015 till 2017/2018 presenteras. Både bilder och egenskaper framtagna av dessa ingår i datasetet. Datasetet ligger till grund för artiklarna Vázquez-Martín, S., Kuhn, T., & Eliasson, S. (2021): Shape dependence of snow crystal fall speed, Atmospheric Chemistry and Physics, 21(10), 7545–7565. https://doi.org/10.5194/acp-21-7545-2021 Vázquez-Martín, S., Kuhn, T., & Eliasson, S. (2021). Mass of different snow crystal shapes derived from fall speed measurements, Atmospheric Chemistry and Physics, 21(24), 18669–18688. https://doi.org/10.5194/acp-2021-203 För mer information se den engelska katalogsidan: https://snd.gu.se/en/catalogue/study/2021-125 Datasetet består av snöpartiklar i form av deras egenskaper samt deras bilder från två kameror. Det har tagits fram från mätningar med ett D-ICI instrument i Kiruna, norra Sverige (67.8N, 20.4E), under vintrarna 2014/2015 till 2017/2018. För mer information se den engelska katalogsidan: https://snd.gu.se/en/catalogue/study/2021-125#dataset