The ability to predict avalanches is done by experts using data from local weather stations and field observations from ski and cross-country operators, avalanche instructors for transportation and industry, and volunteers who manually test the snowpack.

However, according to a new study published in the journal Cold Regions Science and Technology, simulated snow cover models developed by a team of Canadian researchers can detect and track thin layers of snow.

This provides a unique way to detect avalanche hazards and gives forecasters an additional reliable tool when local data is insufficient or unavailable.

Avalanches have been deadly since the beginning of time. However, forecasting models have existed for several decades and are constantly improving, but they are not applied effectively.

Today, simulations developed by researchers allow to determine the risk of avalanches, whether natural or artificial, for all types of problems. Fresh snow, wet snow, wind gusts, persistent light layers.

“Describing typical situations you might encounter is a great way to communicate avalanche risk,” says meteorologist. Simon Horton. “In many situations, however, there is a fair amount of uncertainty in the human assessment of the phenomena that these types of landscapes they will be able to produce.”

Certainly, having more automated solutions that can help predict potential hazards can help forecasters prepare more accurate and precise forecasts.

The results of the study showed that the model developed is consistent with the actual frequencies of avalanches observed in Canada over the past 16 years. More importantly, it showed that the approach has the potential to support avalanche forecasting in the future.

 

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unnamed - 2022-01-25T111402.786

New algorithms to anticipate avalanches

 

The ability to predict avalanches is done by experts using data from local weather stations and field observations from ski and cross-country operators, avalanche instructors for transportation and industry, and volunteers who manually test the snowpack.

However, according to a new study published in the journal Cold Regions Science and Technology, simulated snow cover models developed by a team of Canadian researchers can detect and track thin layers of snow.

This provides a unique way to detect avalanche hazards and gives forecasters an additional reliable tool when local data is insufficient or unavailable.

Avalanches have been deadly since the beginning of time. However, forecasting models have existed for several decades and are constantly improving, but they are not applied effectively.

Today, simulations developed by researchers allow to determine the risk of avalanches, whether natural or artificial, for all types of problems. Fresh snow, wet snow, wind gusts, persistent light layers.

"Describing typical situations you might encounter is a great way to communicate avalanche risk," says meteorologist. Simon Horton. "In many situations, however, there is a fair amount of uncertainty in the human assessment of the phenomena that these types of landscapes they will be able to produce."

Certainly, having more automated solutions that can help predict potential hazards can help forecasters prepare more accurate and precise forecasts.

The results of the study showed that the model developed is consistent with the actual frequencies of avalanches observed in Canada over the past 16 years. More importantly, it showed that the approach has the potential to support avalanche forecasting in the future.

 

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