  The detailed characterization of snow particles is critical for understanding
the snow settling behavior and modeling the ground snow accumulation for
various applications such as prevention of avalanches and snowmelt-caused
floods, etc. In this study, we present a snow particle analyzer for
simultaneous measurements of various properties of fresh falling snow,
including their concentration, size, shape, type, and density. The analyzer
consists of a digital inline holography module for imaging falling snow
particles in a sample volume of 88 cm3 and a high-precision scale to measure
the weight of the same particles in a synchronized fashion. The holographic
images are processed in real-time using a machine learning model and
post-processing to determine snow particle concentration, size, shape, and
type. Such information is used to obtain the estimated volume, which is
subsequently correlated with the weight of snow particles to estimate their
density. The performance of the analyzer is assessed using monodispersed
spherical glass beads and irregular salt crystals with known density, which
shows <5% density measurement errors. In addition, the analyzer was tested in a
number of field deployments under different snow and wind conditions. The
system is able to achieve measurements of various snow properties at single
particle resolution and statistical robustness. The analyzer was also deployed
for four hours of operation during a snow event with changing snow and wind
conditions, demonstrating its ability for long-term and real-time monitoring of
the time-varying snow properties in the field.
