Shape dependence of snow crystal fall speed

Abstract

Abstract. 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. 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. Fall speed is 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 analyzes fall speed as a function of particle size (maximum dimension), cross-sectional area, and shape 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 particle 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. Relationships between particle size, cross-sectional area, and fall speed are studied for different shapes. 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. particle size or cross-sectional area for part of the shapes. For most of these shapes, the fall speed is better correlated with cross-sectional area than with particle size. The effects of orientation and area ratio on the fall speed are also studied, and measurements show that vertically oriented particles fall faster on average. However, most particles for which orientation can be defined fall horizontally.

Mass of different snow crystal shapes derived from fall speed measurements

“…Then, the m vs. D max , m vs. A, and v vs. m relationships are fitted to the median masses vs. median maximum dimensions, median masses vs. median cross-sectional areas, and median fall speeds vs. median masses, respectively. Vázquez-Martín et al (2021) found that about 40 particles in a shape group (currently the lowest number in our dataset is 37) is the limit where binning can still be used. The advantages of binning become prominent only at larger numbers of particles.…”

“…The dataset consists of 2461 high-resolution dual images of falling natural snow crystals and other hydrometeors. The same dataset has been used in Vázquez-Martín et al (2021). The data have been collected using D-ICI, the ground-based in situ instrument described in Kuhn and Vázquez-Martín (2020), at a site in Kiruna, Sweden (67.83 • N, 20.41 • E), described in Vázquez-Martín et al (2020) during multiple snowfall seasons, the winters of 2014/15 to 2018/19.…”

“…The particles are classified according to their shape and sorted into 15 different shape groups as described in Vázquez-Martín et al (2020). A complete description of the dataset and data processing methods is given by Vázquez-Martín et al (2021).…”

“…As seen in Vázquez-Martín et al (2021), using binned data instead of individual data reduces the data spread so that fit functions based on binned data are more robust than fit functions based on individual data. Therefore, also here the data are first binned into a suitable number of bins before fitting Eqs.…”

“…In this study, the Re-X relationship together with the modified Best number (Heymsfield and Westbrook, 2010) is used to determine masses of individual particles from measured maximum dimensions, cross-sectional areas, and fall speeds given by the dataset of our previous study, Vázquez-Martín et al (2021), that also includes particle shape. We analyse mass relationships as functions of maximum dimension, cross-sectional area, and fall speed for different snow particle shapes.…”