Shape dependence of snow crystal fall speed
Improved snowfall predictions require accurate knowledge of the properties of ice crystals and snow particles, such as their size, cross-sectional area, shape, and fall speed. In particular, the shape is an important parameter as it strongly influences the scattering properties of these ice particle...
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ftcopernicus:oai:publications.copernicus.org:acpd90261 2023-05-15T17:04:22+02:00 Shape dependence of snow crystal fall speed Vázquez-Martín, Sandra Kuhn, Thomas Eliasson, Salomon 2020-10-26 application/pdf https://doi.org/10.5194/acp-2020-1056 https://acp.copernicus.org/preprints/acp-2020-1056/ eng eng doi:10.5194/acp-2020-1056 https://acp.copernicus.org/preprints/acp-2020-1056/ eISSN: 1680-7324 Text 2020 ftcopernicus https://doi.org/10.5194/acp-2020-1056 2020-11-02T17:22:14Z Improved snowfall predictions require accurate knowledge of the properties of ice crystals and snow particles, such as their size, cross-sectional area, shape, and fall speed. In particular, the shape is an important parameter as it strongly influences the scattering properties of these ice particles, and thus their response to remote sensing techniques such as radar measurements. The fall speed of ice particles is a critical parameter for the representation of ice clouds and snow in atmospheric numerical models, as it determines the rate of removal of ice from the modelled clouds. They are also required for snowfall predictions alongside other properties such as ice particle size, cross-sectional area, and shape. For example, shape is important as it strongly influences the scattering properties of these ice particles, and thus their response to remote sensing techniques. This work analyses fall speed as a function of shape and other properties using ground-based in-situ measurements. The measurements for this study were done in Kiruna, Sweden during the snowfall seasons of 2014 to 2019, using the ground-based in-situ instrument Dual Ice Crystal Imager (D-ICI). The resulting data consist of high-resolution images of falling hydrometeors from two viewing geometries that are used to determine size (maximum dimension), cross-sectional area, area ratio, orientation, and the fall speed of individual particles. The selected dataset covers sizes from about 0.06 to 3.2 mm and fall speeds from 0.06 to 1.6 m s −1 . The particles are shape-classified into 15 different shape groups depending on their shape and morphology. For these 15 shape groups relationships are studied, firstly, between size and cross-sectional area, then between fall speed and size or cross-sectional area. The data show in general low correlations to fitted fall-speed relationships due to large spread observed in fall speed. After binning the data according to size or cross-sectional area, correlations improve and we can report reliable parameterizations of fall speed vs. size or cross-sectional area for part of the shapes. The effects of orientation and area ratio on the fall speed are also studied, and measurements show that vertically orientated particles fall faster on average. However, most particles for which orientation can be defined fall horizontally. Text Kiruna Copernicus Publications: E-Journals Kiruna |
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English |
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Improved snowfall predictions require accurate knowledge of the properties of ice crystals and snow particles, such as their size, cross-sectional area, shape, and fall speed. In particular, the shape is an important parameter as it strongly influences the scattering properties of these ice particles, and thus their response to remote sensing techniques such as radar measurements. The fall speed of ice particles is a critical parameter for the representation of ice clouds and snow in atmospheric numerical models, as it determines the rate of removal of ice from the modelled clouds. They are also required for snowfall predictions alongside other properties such as ice particle size, cross-sectional area, and shape. For example, shape is important as it strongly influences the scattering properties of these ice particles, and thus their response to remote sensing techniques. This work analyses fall speed as a function of shape and other properties using ground-based in-situ measurements. The measurements for this study were done in Kiruna, Sweden during the snowfall seasons of 2014 to 2019, using the ground-based in-situ instrument Dual Ice Crystal Imager (D-ICI). The resulting data consist of high-resolution images of falling hydrometeors from two viewing geometries that are used to determine size (maximum dimension), cross-sectional area, area ratio, orientation, and the fall speed of individual particles. The selected dataset covers sizes from about 0.06 to 3.2 mm and fall speeds from 0.06 to 1.6 m s −1 . The particles are shape-classified into 15 different shape groups depending on their shape and morphology. For these 15 shape groups relationships are studied, firstly, between size and cross-sectional area, then between fall speed and size or cross-sectional area. The data show in general low correlations to fitted fall-speed relationships due to large spread observed in fall speed. After binning the data according to size or cross-sectional area, correlations improve and we can report reliable parameterizations of fall speed vs. size or cross-sectional area for part of the shapes. The effects of orientation and area ratio on the fall speed are also studied, and measurements show that vertically orientated particles fall faster on average. However, most particles for which orientation can be defined fall horizontally. |
format |
Text |
author |
Vázquez-Martín, Sandra Kuhn, Thomas Eliasson, Salomon |
spellingShingle |
Vázquez-Martín, Sandra Kuhn, Thomas Eliasson, Salomon Shape dependence of snow crystal fall speed |
author_facet |
Vázquez-Martín, Sandra Kuhn, Thomas Eliasson, Salomon |
author_sort |
Vázquez-Martín, Sandra |
title |
Shape dependence of snow crystal fall speed |
title_short |
Shape dependence of snow crystal fall speed |
title_full |
Shape dependence of snow crystal fall speed |
title_fullStr |
Shape dependence of snow crystal fall speed |
title_full_unstemmed |
Shape dependence of snow crystal fall speed |
title_sort |
shape dependence of snow crystal fall speed |
publishDate |
2020 |
url |
https://doi.org/10.5194/acp-2020-1056 https://acp.copernicus.org/preprints/acp-2020-1056/ |
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Kiruna |
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Kiruna |
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Kiruna |
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Kiruna |
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eISSN: 1680-7324 |
op_relation |
doi:10.5194/acp-2020-1056 https://acp.copernicus.org/preprints/acp-2020-1056/ |
op_doi |
https://doi.org/10.5194/acp-2020-1056 |
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