University of Utah tracks motion of falling snowflakes

04 Jan 2024

Laser light sheet and SLR camera scan the air to map lanes where snow is glistening.

A project at the University of Utah has studied the motion of snowflakes moving through turbulent air as they fall to the ground, a complex and dynamic environment.

As well as a better understanding of how snow cover gathers at ground level, the findings could lead to new data about snowflake fall speed, a parameter of interest for predicting weather patterns and assessing climate change.

"Even in the tropics, precipitation often starts its lifetime as snow," commented Timothy Garrett from Utah's Aerosol Cloud Climate Systems Group.

"How fast precipitation falls greatly affects storm lifetimes and trajectories and the extent of cloud cover that may amplify or diminish climate change. Just small tweaks in model representations of snowflake fall speed can have important impacts on both storm forecasting and how fast climate can be expected to warm for a given level of elevated greenhouse gas concentrations."

The research, published in Physics of Fluids, employed a novel experimental set-up designed to obtain both the vertical velocity and acceleration statistics of individual snowflakes settling in atmospheric surface-layer turbulence, parameters which have previously proven challenging to measure.

In 2021 the Utah group developed the differential emissivity imaging disdrometer (DEID), an instrument designed to measure the mass, size and density of falling snowflakes, or "hydrometeors." This device observes a flake falling onto a heated metal plate with an infrared camera, imaging the flake's spatial dimensions before it melts and then calculating its mass via the loss of heat from the hotplate.

The DEID is now commercialised by a Utah spin-out, Particle Flux Analytics, which also markets the multi-angle snowflake camera (MASC) taking 10 to 30 micron resolution photographs of hydrometeors from three angles; and SnowPixel, a thermodynamic sensor array designed to assess snowflake precipitation as part of meteorological sensor networks.

Particle tracking finds simplicity within complex patterns

The new study built on the previous use of DEID by positioning the instrument directly beneath a particle tracking system. This used a laser light sheet and a single-lens reflex camera to follow the path of flakes as they crossed the light sheet on their way down.

To test the system the project spent the winter of 2021-22 at Alta, the snowiest place in Utah, where nature delivered 900 inches of snow for it to study.

Despite the intricate shapes of snowflakes and the uneven air movements they encounter, the researchers found they could predict how snowflakes would accelerate based on the Stokes number, a flow parameter reflecting how quickly the particles respond to changes in the surrounding air movements.

The same mathematical pattern was also involved in how different snowflake shapes fall at different rates, suggesting a fundamental connection between the way the air moves and how snowflakes change as they descend from the clouds to the ground.

“Snowflakes are complicated and turbulence is irregular, so the simplicity of the problem is actually quite mysterious," said Garrett.

"There is something deeper going on in the atmosphere that leads to mathematical simplicity rather than the extraordinary complexity we would expect from looking at complicated snowflake structures swirling chaotically in turbulent air. We just have to look at it the right way, and our new instruments enable us to see that."

University of Utah video