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Two examples of ice crystals imaged in two viewing geometries: top view and side view. The ice crystal shown in panel (a) has a width of approximately 1.2 mm; the one in panel (b) has a width of 0.4 mm. Both ice crystals in panels (a) and (b) use the same scaling; for reference, a size bar with length corresponding to 1 mm (and width of 10 µm) is shown.
Source publication
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...
Contexts in source publication
Context 1
... detection, the ice particle is optically imaged from two different directions. Figure 2 shows examples of the pairs of images resulting for each ice particle. ...
Context 2
... optical axis of the respective imaging optics on the opposite side of the sensing volume (see Fig. 1). While this illumination scheme reveals some details of the inner structure for most snow particles, due to orientation or particle complexity, some parts of the particle can become opaque for the illumination (see, for example, the particles in Fig. 2). This can be considered a limitation of the current illumination setup. However, the details that can be seen on one or both of the high-resolution images (top and side view) will allow shape classification in most cases ( Vázquez-Martín et al., ...
Context 3
... (tumbling) motion. Horizontal winds, which affect other instruments, with an open sampling volume, such as PIP and MASC, do not cause a sideway motion in the enclosed sensing volume of D-ICI. Thus, only a tumbling particle can be responsible for a difference of the horizontal coordinates, and tumbling of ice particles is not often seen (see Sect. 4.2). If it occurs, it is detected by significantly different values of the individual vertical distances measured for a point on the right and left sides of the particle, respectively, so that particles that are tumbling too much may be excluded from analysis of fall speed data. When tumbling, one side of the snow particle falls faster and ...
Citations
... (Del Guasta, 2022) have developed a flatbed scanner (ICE-CAMERA) that has a resolution of 7 µm px −1 and can provide mass estimates by melting the particles, but this approach only works at low snowfall rates. The images of the D-ICI (Dual Ice Crystal Imager, Kuhn and Vázquez-Martín, 2020) have even a resolution of 4 µm px −1 and show particles from two perspectives, but similar to the MASC, the small sampling volume does not allow for the measurement of PSDs with a sufficiently 55 high accuracy. ...
The open source Video In Situ Snowfall Sensor (VISSS) is introduced as a novel instrument for the characterization of particle shape and size in snowfall. The VISSS consists of two cameras with LED backlights and telecentric lenses that allow accurate sizing and combine a large observation volume with relatively high resolution and a design that limits wind disturbance. VISSS data products include per-particle properties and integrated particle size distribution properties such as particle maximum extent, cross-sectional area, perimeter, complexity, and – in the future – sedimentation velocity. Initial analysis shows that the VISSS provides robust statistics based on up to 100,000 particles observed per minute. Comparison of the VISSS with collocated PIP and Parsivel instruments at Hyytiälä, Finland, shows excellent agreement with Parsivel, but reveals some differences for the PIP (Precipitation Imaging Package) that are likely related to PIP data processing and limitations of the PIP with respect to observing smaller particles. The open source nature of the VISSS hardware plans, data acquisition software, and data processing libraries invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.
... Deploying at Kiruna also enabled coincident measurements with the D-ICI, a groundbased in situ instrument developed at LTU to determine snow ice crystal properties and fall speed simultaneously (Kuhn and Vázquez-Martín 2020). The instrument takes highresolution pictures of the same falling ice particle from two different viewing directions. ...
... The D-ICI is a high-resolution in situ instrument used for capturing key parameters of falling snow particles. Vertically and horizontally pointing cameras take images of single particles that pass through the inlet's sensing volume, enabling the simultaneous retrieval of snow crystal properties and fall speed (Kuhn and Vázquez-Martín 2020) for particles within the 20 μm-3.2 mm range. The vertically viewing camera captures the size and shape of the snow particles from single-exposure images. ...
The High-Latitude Measurement of Snowfall (HiLaMS) campaign explored variability in snowfall properties and processes at meteorologically distinct field sites located in Haukeliseter, Norway, and Kiruna, Sweden, during the winters of 2016/17 and 2017/18, respectively. Campaign activities were founded upon the sensitivities of a low-cost, core instrumentation suite consisting of Micro Rain Radar, Precipitation Imaging Package, and Multi-Angle Snow Camera. These instruments are highly portable to remote field sites and, considered together, provide a unique and complementary set of snowfall observations including snowflake habit, particle size distributions, fall speeds, surface snowfall accumulations, and vertical profiles of radar moments and snow water content. These snow-specific parameters, used in combination with existing observations from the field sites such as snow gauge accumulations and ambient weather conditions, allow for advanced studies of snowfall processes. HiLaMS observations were used to 1) successfully develop a combined radar and in situ microphysical property retrieval scheme to estimate both surface snowfall accumulation and the vertical profile of snow water content, 2) identify the predominant snowfall regimes at Haukeliseter and Kiruna and characterize associated macrophysical and microphysical properties, snowfall production, and meteorological conditions, and 3) identify biases in the HARMONIE-AROME numerical weather prediction model for forecasts of snowfall accumulations and vertical profiles of snow water content for the distinct snowfall regimes observed at the mountainous Haukeliseter site. HiLaMS activities and results suggest value in the deployment of this enhanced snow observing instrumentation suite to new and diverse high-latitude locations that may be underrepresented in climate and weather process studies.
... Although a taxonomy of individual crystals is nowadays established, much more has to be investigated about mass, density, fall speed and orientation and all the dynamical processes occurring while solid hydrometeors fall. A helping hand to the research in this field comes from the rapid development of snow particle imagers, either installed on aircraft to sample clouds and precipitation aloft [10][11][12] or deployed at the ground level [13][14][15][16] . Ground-based imagers observe snowfall just before deposition and can provide information also on the fall speed, orientation and textural characteristics of falling snowflakes. ...
Snowfall information at the scale of individual particles is rare, difficult to gather, but fundamental for a better understanding of solid precipitation microphysics. In this article we present a dataset (with dedicated software) of in-situ measurements of snow particles in free fall. The dataset includes gray-scale (255 shades) images of snowflakes, co-located surface environmental measurements, a large number of geometrical and textural snowflake descriptors as well as the output of previously published retrieval algorithms. These include: hydrometeor classification, riming degree estimation, identification of melting particles, discrimination of wind-blown snow, as well as estimates of snow particle mass and volume. The measurements were collected in various locations of the Alps, Antarctica and Korea for a total of 2’555’091 snowflake images (or 851’697 image triplets). As the instrument used for data collection was a Multi-Angle Snowflake Camera (MASC), the dataset is named MASCDB. Given the large amount of snowflake images and associated descriptors, MASCDB can be exploited also by the computer vision community for the training and benchmarking of image processing systems.
... 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 images are taken when the snow particles fall into the inlet and consequently fall down the sampling tube and traverse the optical cell. ...
... 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 images are taken when the snow particles fall into the inlet and consequently fall down the sampling tube and traverse the optical cell. ...
... In the centre of the optical cell is the sensing volume. If particles are falling through the sensing volume they are detected by the detecting optics (for a detailed description see Kuhn and Vázquez-Martín, 2020). Upon detection, the particles are optically imaged simultaneously from two different viewing directions. ...
Meteorological forecast and climate models require good knowledge of the
microphysical properties of hydrometeors and the atmospheric snow and ice
crystals in clouds, for instance, their size, cross-sectional area, shape,
mass, and fall speed. Especially shape is an important parameter in that it
strongly affects the scattering properties of ice particles and consequently
their response to remote sensing techniques. The fall speed and mass of ice
particles are other important parameters for both numerical forecast models
and the representation of snow and ice clouds in climate models. In the
case of fall speed, it is responsible for the rate of removal of ice from
these models. The particle mass is a key quantity that connects the cloud
microphysical properties to radiative properties. Using an empirical
relationship between the dimensionless Reynolds and Best numbers, fall speed
and mass can be derived from each other if particle size and cross-sectional
area are also known.
In this study, ground-based in situ measurements of snow particle
microphysical properties are used to analyse mass as a function of shape and
the other properties particle size, cross-sectional area, and fall speed. The
measurements for this study were done in Kiruna, Sweden, during snowfall
seasons of 2014 to 2019 and using the ground-based in situ Dual Ice
Crystal Imager (D-ICI) instrument, which takes high-resolution side- and top-view images
of natural hydrometeors. From these images, particle size (maximum dimension),
cross-sectional area, and fall speed of individual particles are
determined. The particles are shape-classified according to the scheme
presented in our previous study, in which particles sort into 15 different
shape groups depending on their shape and morphology. Particle masses of
individual ice particles are estimated from measured particle size,
cross-sectional area, and fall speed. The selected dataset covers sizes from
about 0.1 to 3.2 mm, fall speeds from 0.1 to 1.6 m s−1,
and masses from 0.2 to 450 µg. In our previous
study, the fall speed relationships between particle size and cross-sectional
area were studied. In this study, the same dataset is used to determine the
particle mass, and consequently, the mass relationships between particle size,
cross-sectional area, and fall speed are studied for these 15 shape groups.
Furthermore, the mass relationships presented in this study are compared with
the previous studies.
For certain crystal habits, in particular columnar shapes, the maximum
dimension is unsuitable for determining Reynolds number. Using a selection of
columns, for which the simple geometry allows the verification of an empirical
Best-number-to-Reynolds-number relationship, we show that Reynolds number and
fall speed are more closely related to the diameter of the basal facet than
the maximum dimension. The agreement with the empirical relationship is
further improved using a modified Best number, a function of an area ratio
based on the falling particle seen in the vertical direction.
... Figure 1 shows two different snow particles from the side (right) and the top view (left). The images from the top view are 95 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, 100 A, is defined as the area in the top-view image enclosed by the particle boundary based on pixel count. ...
... 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). ...
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.
... The datasets collected so far by various groups (e.g., Gaustad et al., 2015;Notaroš et al., 2016;Praz et al., 2017;Genthon et al., 2018) show that the detailed images obtained by the MASC provide a signature of the processes that led to the formation of each snowflake. The MASC can discern processes such as various modes of deposition growth like columns, plates and dendrites, as well as aggregation, riming and melting (for an overview of these, see, e.g., Lamb and Verlinde, 2011). As these processes depend on the environmental conditions in which the snowflake grew, the MASC can provide information about the relative occurrence of these conditions in a specific snowfall event and, over longer timescales, the local climate. ...
The increasing availability of sensors imaging cloud and precipitation particles, like the Multi-Angle Snowflake Camera (MASC), has resulted in datasets comprising millions of images of falling snowflakes. Automated classification is required for effective analysis of such large datasets. While supervised classification methods have been developed for this purpose in recent years, their ability to generalize is limited by the representativeness of their labeled training datasets, which are affected by the subjective judgment of the expert and require significant manual effort to derive. An alternative is unsupervised classification, which seeks to divide a dataset into distinct classes without expert-provided labels. In this paper, we introduce an unsupervised classification scheme based on a generative adversarial network (GAN) that learns to extract the key features from the snowflake images. Each image is then associated with a distribution of points in the feature space, and these distributions are used as the basis of K-medoids classification and hierarchical clustering. We found that the classification scheme is able to separate the dataset into distinct classes, each characterized by a particular size, shape and texture of the snowflake image, providing signatures of the microphysical properties of the snowflakes. This finding is supported by a comparison of the results to an existing supervised scheme. Although training the GAN is computationally intensive, the classification process proceeds directly from images to classes with minimal human intervention and therefore can be repeated for other MASC datasets with minor manual effort. As the algorithm is not specific to snowflakes, we also expect this approach to be relevant to other applications.
The open-source Video In Situ Snowfall Sensor (VISSS) is introduced as a novel instrument for the characterization of particle shape and size in snowfall. The VISSS consists of two cameras with LED backlights and telecentric lenses that allow accurate sizing and combine a large observation volume with relatively high pixel resolution and a design that limits wind disturbance. VISSS data products include various particle properties such as maximum extent, cross-sectional area, perimeter, complexity, and sedimentation velocity. Initial analysis shows that the VISSS provides robust statistics based on up to 10 000 unique particle observations per minute. Comparison of the VISSS with the collocated PIP (Precipitation Imaging Package) and Parsivel instruments at Hyytiälä, Finland, shows excellent agreement with the Parsivel but reveals some differences for the PIP that are likely related to PIP data processing and limitations of the PIP with respect to observing smaller particles. The open-source nature of the VISSS hardware plans, data acquisition software, and data processing libraries invites the community to contribute to the development of the instrument, which has many potential applications in atmospheric science and beyond.
Ice particle terminal fall velocity ( V t ) is fundamental for determining microphysical processes, yet remains extremely challenging to measure. Current theoretical best estimates of V t are functions of Reynolds number. The Reynolds number is related to the Best number, which is a function of ice particle mass, area ratio ( A r ) and maximum dimension ( D max ). These estimates are not conducive for use in most models since model parameterizations often take the form V t =αD max β , where ( α,β ) depend on habit and D max . A previously developed framework is used to determine surfaces of equally plausible ( α,β ) coefficients whereby ice particle size/shape distributions are combined with V t best estimates to determine mass- ( V M ) or reflectivity-weighted ( V Z ) velocities that closely match parameterized V M,SD or V Z,SD calculated using the ( α,β ) coefficients using two approaches.
The first uses surfaces of equally plausible ( a,b ) coefficients describing mass (M)-dimension relationships (i.e., M=aD max b ) to calculate mass- or reflectivity-weighted velocity from size/shape distributions that are then used to determine ( α,β ) coefficients. The second investigates how uncertainties in A r , D max , and size distribution N(D) affect V M or V Z . For seven of nine flight legs flown 20/23 May 2011 during MC3E, uncertainty from natural parameter variability – namely the variability in ice particle parameters in similar meteorological conditions – exceeds uncertainties arising from different A r assumptions or D max estimates. The combined uncertainty between A r , D max and N(D) produced smaller variability in ( α,β ) compared to varying M(D) , demonstrating M(D) must be accurately quantified for model fall velocities. Primary sources of uncertainty vary considerably depending on environmental conditions.
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.
