Every snowflake is unique, displaying endless varieties through its intricate structures. While snowflakes are vastly different from each other, the process of their descent towards the ground shares certain similarities and can even be predicted scientifically. After tracking the trajectory of more than half a million snowflakes, researchers have unveiled a universal mathematical model that can accurately describe the rotational path of snowflakes in the air.
An atmospheric scientist at the University of Utah, who has studied snowflakes for more than ten years and recently published a novel study as the corresponding author, indicated that although snowflakes are delicate and short-lived, their falling speed is an important parameter for predicting weather and understanding climate patterns. This is especially apparent in tropical areas, where the majority of precipitation originates from snowflakes, regardless of where they ultimately land.
Scientists often study the motion of snowflakes in controlled laboratory conditions, but this does not truly simulate their complex dynamics in nature. Thus, one challenge that atmospheric scientists have faced for decades is how to accurately observe the falling of snowflakes in natural environments. To address this problem, they developed an instrument in collaboration with engineers to precisely measure the various physical characteristics of a single snowflake that has landed on a heated plate. With this instrument placed under a laser plane and camera, the research team was able to track how each snowflake moves through the turbulence of outdoor air.
“We were able to let the atmosphere express itself without any intervention by scientists. I believe that is precisely why we found the motion of snowflakes to be so simple and yet so elegant.”
The research team discovered that the average acceleration of snowflakes—which is influenced by their degree of rotation—shows a linear relationship with the so-called Stokes number, a parameter that describes the reactivity of an object to changes in air turbulence. This suggests that fluffy, wide snowflakes rotate more easily compared to slender, streamlined ones. This finding allows scientists to predict the rotational patterns of a single snowflake as it falls through the air.
What surprised the research group even more was that despite the variety of air turbulence, shapes, and sizes of snowflakes, the average distribution of snowflake rotations fit an almost perfect exponential curve, revealing a consistent mathematical pattern. The principle behind this pattern is not yet known; however, it may be related to the way air turbulence drives changes in the shape and size of snowflakes, further affecting their response to turbulence.
An expert mentioned, while the results of the study are exciting, further research is needed to assess the universality of this mathematical model. Plans are underway to test it under various conditions, such as at different altitudes and over surfaces with differing degrees of roughness.
If this pattern is proven to be universally applicable, Garrett believes, “then the simplicity suggests there will be a straightforward explanation; we just need to find it.”
