Community Science Project: Snowflake ID: Help Scientist Identify Snowflakes To Better Understand Climate Change.

For my community science project, I explored the Snowflake Identification project. Scientists and other climate-related researchers attempt to study snowflakes in their most raw of conditions. They have already discovered, that no two snowflakes are alike. Although this is established, it wasn’t so apparent that we were studying snowflakes in their already altered form, not fully seeing them for what they are, what they can mean, and how they impact climate change and natural disasters, such as avalanches and blizzards.

One winter in Vermont,1885, 20-year-old, Wilson Bentley who is fascinated by snowflakes, decided to attach a camera to a microscope to capture what they look like. Finally, after countless attempts, he manages to obtain a clear picture of one. This discovery set off an avalanche of research, so to speak. He started the initial series of cataloged snowflake specimens, each flake being perfectly symmetrical, but never two of the same.

Simultaneously following his pursuit, German meteorologist Gustav Hellmann and his assistant, Richard Neuhauss cataloged pictures of snowflakes as well. However, their findings were completely different. They discovered that not only are they unique to one another, but some of them are irregular in shape, opposed to the perfectly symmetrical ones previously noted.

Presently, in Utah, NSF-funded engineer Cale Fallgatter, accompanied by atmospheric scientist Tim Garret has reinvented the photo-taking process for snowflakes, in order to better understand weather patterns derived from the clouds in which they come from. Using their revolutionary highspeed camera, which they named the Present Weather Imager or PWI, they are able to capture freefalling snowflakes in all their majesty from their journey out of the clouds, through the atmosphere, and into dissipation, in most cases before they even hit the ground. The single industrial-grade 1.3-megapixel camera uses an infra-red sensor to capture the movement. This camera provides data on snowflakes, shape size orientation, and the speed at which they fall.

All of this information helps construct weather patterns to better predict and prepare for severe incremental weather, such as blizzards, avalanches, and other hazardous conditions like hail. This information is imperative to meteorologists and other atmospheric scientists because of its predictive implications.

I helped identify a few of the captured snowflakes through their snowflake identifier on the Zooniverse. It was pretty cool to determine the lack of presents of rimming, the degree of graupel, melting or dissipation, temperature, and how dense or light each flake was. I understand why it is important to comprehend the given properties of snowflakes and why it matters to the scientific and non-scientific communities to gain a better perspective behind weathering and how evaluating the natural forces beyond our control is needed.

One thought on “Community Science Project: Snowflake ID: Help Scientist Identify Snowflakes To Better Understand Climate Change.

  1. Latesha,
    this was an interesting read as there hasn’t been any snow accumulation in NYC this winter so far, so I wonder what extent global warming may have on that outcome. I find the use of a 1.3 MP camera interesting, given the seemingly wider range of higher resolution cameras available that have more MP. On that note, while 1.3 MP is enough to capture decent resolution video, I wonder what the camera placement was to properly achieve the right angles to capture snowflakes. This question of optics is due to the fact that the event I attended also was similar but tracked smoke plumes from industrial sites, which could be noticed from a wider field of view, and even in more pixelated video streams. To capture a snowflake would need a fairly precise focus setting, and I wonder how the researchers set up their camera to do so, especially given that snowfall is not something one can naturally control. Thanks for sharing!

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