Microphysical properties and fall speed measurements of snow ice crystals using the Dual Ice Crystal Imager (D-ICI)

Abstract

Abstract. 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. We describe a new ground-based in situ instrument, the Dual Ice Crystal Imager (D-ICI), to determine snow ice crystal properties and fall speed simultaneously. The instrument takes two high-resolution pictures of the same falling ice particle from two different viewing directions. Both cameras use a microscope-like setup resulting in an image pixel resolution of approximately 4 µm pixel−1. One viewing direction is horizontal and is used to determine fall speed by means of a double exposure. For this purpose, two bright flashes of a light-emitting diode behind the camera illuminate the falling ice particle and create this double exposure, and the vertical displacement of the particle provides its fall speed. The other viewing direction is close-to-vertical and is used to provide size and shape information from single-exposure images. 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 instrument has been tested in Kiruna, northern Sweden (67.8∘ N, 20.4∘ E). Measurements are demonstrated with images from different snow events, and the determined snow ice crystal properties are presented.

Shape dependence of snow crystal fall speed

“…For this study, we use a large subset of the data from Vázquez-Martín et al (2020). Although we excluded measurements with higher wind speeds than 3 m s −1 , the cross-sectional areas as a function of particle size are nonetheless very similar here to results presented in Vázquez-Martín et al (2020). However, for completeness, we briefly analyse and discuss the crosssectional area versus particle size, A vs. D max , for all the shape groups in this section.…”

“…Several studies have classified snow crystal shape according to different classification schemes (Nakaya, 1954;Magono and Lee, 1966;Kikuchi et al, 2013;Vázquez-Martín et al, 2020). Particle shape is essential, not only for investigating growth processes but also because it affects remote sensing measurements, for instance, radar measurements (e.g., Sun et al, 2011;Matrosov et al, 2012;Marchand et al, 2013) or passive measurements of microwave brightness temperatures (Kneifel et al, 2010).…”

“…Figure 1 shows two different snow particles from the side (right) and the top view (left). The images from the top view are used to determine particle size, cross-sectional area, and area ratio by the automated process presented in Kuhn and Vázquez-Martín (2020). For this, first, the background features are removed, then the in-focus particles are detected, and their boundaries traced.…”

“…Consequently, the particle properties, such as particle size, cross-sectional area, and area ratio can be determined. As we have described in Vázquez-Martín et al (2020), the maximum dimension, D max , defined as the smallest diameter that completely encircles the particle boundary in the top-view image, is used to describe the particle size. The cross-sectional area, A, is defined as the area in the top-view image enclosed by the particle boundary based on pixel count.…”

Unsupervised classification of snowflake images using a generative adversarial network and <i>K</i>-medoids classification

“…Among these is the Multi-Angle Snowflake Camera (MASC; Garrett et al, 2012), which employs three cameras positioned at different angles to captures images of snowflakes illuminated by a flash as they fall through its measurement volume. Other similar instruments include the Snowflake Video Imager (SVI; Newman et al, 2009, also known as Particle Video Imager or Particle Imaging Package) and the Dual Ice Crystal Imager (D-ICI; Kuhn and Vázquez-Martín, 2020).…”