The science behind snowflakes – @theU

The science behind snowflakes – @theU

Tim Garrett has devoted his scientific career to characterizing snowflakes, protein ice particles that form in clouds and change dramatically when they fall to the ground.

Now a University of Utah atmospheric scientist is unraveling the mystery of how snowflakes move in response to atmospheric turbulence that accompanies falling snow using new tools developed on campus. After analyzing more than half a million snowflakes, he was astonished by what his team discovered.

Instead of doing something incomprehensibly complicated, predicting how snowflakes will move turns out to be surprisingly simple.

“How snowflakes fall has attracted a lot of attention for many decades because it is a critical factor for predicting weather and climate change,” Garrett said. “This is related to the speed of the water cycle. How quickly moisture falls from the sky determines the lifespan of storms.

“Messages sent from heaven”

Image credit: Tim Garrett, University of Utah

Graduate student Ryan Szczerbinski examines instruments called Differential Imaging Emissivity Diodes, or DEID, developed by researchers from the University of Utah and installed in Alta near the summit of Little Cottonwood Canyon. The device measures the mass of the hydrometeor and the size and density of the ice flakes.

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Renowned Japanese physicist Okishiro Nakaya called ice crystals “messages sent from the sky” because their delicate structures carry information about temperature and humidity fluctuations in clouds where the basal and prismatic crystalline sides compete to deposit water vapor.

While each snowflake is thought to be unique, how these frozen particles fall through the air — as they accelerate, drift and swirl — follows patterns, according to new research by Garrett and colleagues in the College of Engineering. Snowflake motion has important implications for weather prediction and climate change, even in tropical regions.

“Most precipitation starts with snow. How fast it falls affects predictions about where the rain will fall on Earth, and how long clouds continue to reflect radiation back into outer space,” Garrett said. “It can even affect hurricane track predictions.”

Also participating in the research are Dheeraj Singh and Eric Bardjak of the U of T's Department of Mechanical Engineering.

To study the motion of snowflakes, the team needed a way to measure individual snowflakes, which had been a challenging puzzle for years.

“They have very low masses. They may only weigh 10 micrograms, one-hundredth of a milligram, so they can't be weighed with very high precision,” Garrett said.

Working with the College of Engineering, Garrett developed an instrument called the Differential Emission Inferometer, or DEID, that measures the mass of a hydrometeor and the size and density of icicles. This device has since been commercialized by a company Garrett co-founded called Particle Flux Analytics. The Utah Department of Transportation has deployed equipment in Little Cottonwood Canyon to help predict avalanches, he said.

For Garrett's field trials, his team set them up in Alta, the snowiest place in Utah for the 2020-2021 winter. The instruments were deployed along with measurements of air temperature, relative humidity and turbulence, and were placed directly under a particle tracking system consisting of a laser light sheet and a single-lens reflex camera.

“By measuring the turbulence and the mass, density and size of the snowflakes and observing how they meander in the turbulence, we are able to create a comprehensive picture that could not have been obtained before in a natural way,” Garrett said. environment before.

The results were not what the team expected

Image credit: Tim Garrett, University of Utah

Researchers from the University of Utah are testing an instrument called the Differential Emission Inferometer (DEID), which measures the mass of a hydrometeor and the size and density of snowflakes, in Red Butte Canyon. This equipment is being used in pioneering snowflake research in the mountains of Utah.

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Despite the complex shapes of snowflakes and the uneven movement of air they experience, the researchers found that they could predict how snowflakes would accelerate based on a parameter known as the Stokes number (St), which reflects how quickly particles respond to changes in surrounding air movements.

When the team analyzed the acceleration of individual snowflakes, the average increased in an almost linear manner with the Stokes number. Furthermore, the distribution of these accelerations can be described by a single exponential curve independent of the Stokes number.

The researchers found that the same mathematical pattern could be related to how changing snowflake shapes and sizes affect how quickly it falls, suggesting a fundamental relationship between the way air moves and how snowflakes change as they fall from the clouds to the ground.

“That to me seems almost mysterious,” Garrett said. “There is something deeper going on in the atmosphere that leads to mathematical simplicity rather than the extraordinary complexity we expect from looking at complex snowflake structures swirling chaotically in turbulent air. We just have to look at it the right way and our new tools enable us to see that.”

Garrett's study, titled “A Universal Scaling Law for Lagrangian Snowflake Accelerations in Atmospheric Turbulence,” appeared Dec. 19 in the journal Physics of Fluids, published by the American Institute of Physics. Funding came from the National Science Foundation.

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