  The effect of turbulence on snow precipitation is not incorporated into
present weather forecasting models. Here we show evidence that turbulence is in
fact a key influence on both fall speed and spatial distribution of settling
snow. We consider three snowfall events under vastly different levels of
atmospheric turbulence. We characterize the size and morphology of the snow
particles, and we simultaneously image their velocity, acceleration, and
relative concentration over vertical planes about 30 m2 in area. We find that
turbulence-driven settling enhancement explains otherwise contradictory trends
between the particle size and velocity. The estimates of the Stokes number and
the correlation between vertical velocity and local concentration indicate that
the enhanced settling is rooted in the preferential sweeping mechanism. When
the snow vertical velocity is large compared to the characteristic turbulence
velocity, the crossing trajectories effect results in strong accelerations.
When the conditions of preferential sweeping are met, the concentration field
is highly non-uniform and clustering appears over a wide range of scales. These
clusters, identified for the first time in a naturally occurring flow, display
the signature features seen in canonical settings: power-law size distribution,
fractal-like shape, vertical elongation, and large fall speed that increases
with the cluster size. These findings demonstrate that the fundamental
phenomenology of particle-laden turbulence can be leveraged towards a better
predictive understanding of snow precipitation and ground snow accumulation.
They also demonstrate how environmental flows can be used to investigate
dispersed multiphase flows at Reynolds numbers not accessible in laboratory
experiments or numerical simulations.
