Utah research may change everything we know about snowfall

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SALT LAKE CITY — Most people already know that almost no snowflakes are alike, but a new study by researchers from the University of Utah offers new insight into how and why all individual snowflakes fall the way they do.

Their findings, Published in the peer-reviewed scientific journal Fluid Physics Last week, he noted that despite the “complexity of snowflake structures and the irregular nature of turbulence,” a snowflake's acceleration, or how fast snow falls, can be “uniquely determined” by a mathematical equation.

“It suggests that there is something lurking in the atmosphere that is actually quite simple, and I'm not quite sure what it is but our results suggest that there may be ways to somehow describe one of the most difficult aspects of atmospheric science,” said Tim Garrett, professor of atmospheric sciences at University of Utah, and one of the study's co-authors: “Maybe this could be addressed through a computer model in a fairly straightforward way.”

The findings could open the door to a better understanding of snowstorms and avalanches, and improved forecasting in the future.

Snow falling and movement

It took more than a decade to prepare the results of the study. Garrett began measuring how quickly snowflakes fell in Alta when he decided to dig deeper into the topic. He thought this was the perfect topic to explore given his interest in the physics of motion and how Utahns generally like to talk about snow.

This led to early observations that snowflakes were not falling quite the way they were supposed to based on traditional weather and climate models, which were based on equipment that only took into account snowfall in still air. Snow falls in more unique ways than the models suggested, which isn't at all surprising.

“Even though atmospheric scientists don't admit it, of course, everyone knows that snowflakes spin in the air,” he told KSL.com, recalling the moment.

So, he recruited Dheeraj Singh and Eric Bardjak, two researchers from the university's Department of Mechanical Engineering, to help solve the relationship between snowfall and atmospheric turbulence. They invented – and patented – a tool called a Differential emissivity measurement scale To measure the mass, volume and density of snowflakes to solve this scientific mystery.

With the help of a National Science Foundation grant, the team set up the device at a site in Little Cottonwood Canyon during the 2020-2021 winter season. They studied air temperature, relative humidity, turbulence and other weather factors, and analyzed more than 500,000 snowflakes. All this information provided a “comprehensive picture” that had never been seen before, Garrett said.

What they found when they put all this information together was that they were able to predict how quickly snow would fall using the flake's Stokes number, a dimensionless number that helps scientists understand how particles interact with changes in flow such as atmospheric turbulence. The Stokes number is usually higher for rain and lower for snow, which is why they fall differently.

“As a result, snow tends to be exposed to turbulent air currents, while rain tends to fall directly through them,” Garrett said. “What we ended up finding was that as long as we knew the Stokes number, this one dimensionless number, the snowflake world we lived in was in some ways our oyster. And that was enough information for us to describe how often snowflakes A certain level of acceleration.

The researchers also point out, citing decades-old previous research, that updrafts in clouds influence how snowflakes form. Garrett explains that the addition of new knowledge means it may be possible to fully determine snowfall by measuring cloud turbulence.

why does it matter

This may have many implications in the future. For example, how snowflakes fall is a “critical parameter” for weather forecasting, because the rate at which moisture falls from clouds is traditionally a measure of how long a storm will last, Garrett said in a statement before the study was released.

He explained to KSL.com that the new study “doesn't immediately take us” to an answer on how to better predict the length and intensity of storms, but it could provide new insights into the relationship between snowfall and wind. This could lead to future meteorological breakthroughs.

“If this is the case, and we can show in the future that this is indeed supported, this could lead to very significant improvements in storm modeling,” he said. “Currently, one of the biggest challenges weather models face is predicting the types of snowflakes that form in clouds. Our results suggest that some of the difficulties… may eventually be 'less complex'.”

This can be limited to measuring air movement in clouds only.

Meanwhile, the differential emission meter, the tool that led to this discovery, is already being used in other impactful ways. The Utah Department of Transportation has purchased a few devices to help it predict avalanches in places like Little Cottonwood Canyon, because they instantly measure snow density, which is often a factor in avalanches.

The work is not done either. Garrett says he and his colleagues have collected more data than they have time to decipher; However, he plans to continue scrutinizing it and conducting experiments to better understand snowfall.

He also hopes everyone can find beauty in how snowflakes dance in the air as they fall this winter as he and others uncover its secrets.

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