During Dangerous Season Researchers Improve Predictions For Certain Avalanches
This winter season has been a deadly one for avalanches. Many of those avalanches were caused by a single type of snow condition known as a deep persistent slab, which is dangerous and hard to predict. New research may help with that.
Andrew Schauer is an avalanche forecaster at the Chugach National Forest in Alaska. He said the avalanches occur because there is an old, weak layer of snow buried underneath newer snow. As snow accumulates on top of the weak layer, it becomes less likely to trigger an avalanche, but more dangerous if it does.
"The likelihood might be getting a little bit smaller, and we're left as forecasters in this difficult forecasting situation where it's unlikely you're going to trigger something, but if you do, it can the kind of thing that's big enough to tear down trees and break buildings and definitely injure or kill a person," said Schauer.
Schauer and his team analyzed the weather and climate in three locations: Bridger Bowl, Montana, Jackson, Wyoming, and Mammoth Mountain, California. They used machine learning to classify the weather and identify which specific patterns preceded avalanches.
The researchers found that winter seasons with less snow had more of the deep slab avalanches. However, in the three days before a deep persistent slab avalanche, there were actually more storms and snow than average.
Schauer said this technique and more like it will improve avalanche predictions and backcountry safety.
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