On winter mornings, the Forest Service Avalanche Center for the Sierra publishes the avalanche forecast for the day. This is useful information for anyone traveling in the backcountry.
Often a draft of the forecast is started the afternoon before after a day of skiing. It may be updated early in the morning with the benefit of that day’s weather forecast and any late arriving snow or avalanche observations. The snow and avalanche data is built on observations from Sierra Avalanche Center field staff who spend their days skiing. It is augmented with observations, photos and videos shared to the Sierra Avalanche Center website by the public.
The Forest Service Avalanche Center would not exist without community donations, despite being a government operation. Northstar, Heavenly, and Kirkwood have consistently directed significant support to the Sierra Avalanche Center through their donation of lift tickets to the Forest Service.
Northstar, Heavenly, and Kirkwood resorts have been the avalanche center’s largest business supporters for multiple years. This money goes back into supporting the Avalanche Center field staff, paying for skiing and observing, equipment, the website, and our ongoing efforts to increase avalanche awareness by promoting the center.
The Sierra Avalanche Center staff and volunteer board thanks to Northstar, Heavenly, Kirkwood and the Vail Resorts EpicPromise Team for their continued support.
(*) It should be noted that these large ski operations depend on Forest Service permits for their use of public lands.
COOKE CITY — Avalanche forecasters trigger an avalanche: Even if you’re an expert, you can never be too safe in the backcountry. It’s something two avalanche forecasters learned firsthand last week after they triggered a slide near Cooke City.
Last Friday two Forest Service avalanche forecasters from the Gallatin National Forest Avalanche Center had a close call on Fisher Mountain.
“It’s incredibly terrifying,” said the director of the Gallatin National Forest Avalanche Center, Doug Chabot, on Wednesday.
The two forecasters had snowmobiled to an area on the mountain to dig some snow pits. “One of them rode and then the second person came, and when the second person was coming, he triggered the avalanche from below,” said Chabot.
The second rider was below the slope. “And he was able to propagate this fracture up the hill, which then caught him. Luckily the other guy, the first guy, he was safe,” Chabot said.
The first rider called for help on the radio and then rushed down to his colleague.
“And luckily he wasn’t buried. He had snow up to his thighs, but it was a really close call because it was a big avalanche,” said Chabot.
They were able to dig out the rider and his snowmobile, and thankfully no one was hurt but it’s a reminder that there are no rules when it comes to Mother Nature.
“December and January can be quite bad for avalanches in Montana. And the reason for that is, is we’re forming many weak layers in the snowpack right now,” Chabot said.
Chabot said the snowpack is relatively thin, usually only two to three feet thick in most places. Thin snowpacks can change really fast. At some point, the weak layer may break and that’s what triggers avalanches.
“But the ones that kill people in the United States are overwhelmingly, either the victim or someone in their party triggered the avalanche,” said Chabot. Like these guys.
Avalanche Warnings in Switzerland to be modified starting this winter. The SLF is enhancing the precision by subdividing the danger levels. It will indicate whether the danger is towards the bottom end, in the middle or towards the top end of the reported level.
The SLF (Swiss Institute for Avalanche Research) says the new system will provide “users with more precise information for assessing the risk”.
The five danger levels themselves remain unchanged.
The Avalanche Warnings in Switzerland will have subdivisions of the danger levels to indicate where the danger is in the level:
Towards the bottom end (-)
More or less in the middle (=)
Towards the top end (+)
This subdivision is only applied for dry avalanches, and only starting at Level 2.
The purpose is to make it easier for users to interpret the Avalanche Warnings in Switzerland correctly.
Other national avalanche forecasting bodies are watching the change closely.
Avalanche warning scale
The SLF explains the new situation in a series of questions and answers:
Why this change?
The SLF has been hearing calls for a more precise representation of the avalanche danger for several years.
For those who engage in snow sports, Level 3 in particular spans a very broad spectrum.
The avalanche forecasters likewise have been wanting to pinpoint the danger with greater precision for some time.
These wishes have now been satisfied with the introduction of the subdivisions.
Why not simply add a danger level?
Additional danger levels would not be feasible in the international arena and would serve little purpose in any case.
According to avalanche forecaster Kurt Winkler, “One advantage of the new system is that it takes findings of psychological research into account.”
The human brain cannot differentiate more than five to seven defined classes.
The danger levels, which are clearly defined, are already aligned with this understanding and are thus being retained.
The classification reflects logical and therefore slow thought processes.
But we can also determine, without any difficulty, a relative ranking within the individual classes.
We make subdivisions of this kind intuitively and thus quickly.
Does the new approach work in practice?
The SLF’s avalanche forecasters have already been allocating subdivisions to all danger assessments over a period of six winters, but has refrained from publishing them.
This large data set was then statistically analyzed in order to review the new approach.
The analysis clearly showed that the differences between the danger level forecast in the avalanche bulletin and the observations reported from the field are much smaller when the subdivision forms part of the picture as well.
In addition, a clear correlation was shown to exist with objectively measurable factors that play a role in the formation of avalanches.
From one subdivision to the next one above, for example, the frequency of whumpfing sounds and of avalanches triggered by people increases and the snowpack stability test results deteriorate.
Since the experimental series also demonstrated that subdividing the danger levels is dysfunctional in the case of wet snow avalanches, subdivisions are not being reported when the snow is wet.
Where the graphical reduction method is being applied, the subdivisions can be taken into account by entering the diagram on the left or right side of the column representing the danger level and then using the diagonal color code.
What is the target audience for the subdivisions and how can they be useful?
Everyone can benefit from the additional information which is provided.
“Those who wish to avoid the most dangerous situations on a given winter’s day can simply stay away from regions where the danger level is 3+ or higher, for instance, and instead either choose to visit a region where the danger is lower or even make other plans for the day,” explains Kurt Winkler.
The subdivisions can be used quantitatively as well.
The calculation of the avalanche risk on the website skitourenguru.ch, for example, takes them into account.
The subdivisions can also be useful when applying the graphical reduction method.
For an approximate assessment, the danger level can still be used in such cases.
Alternatively, based on the subdivision, the user can enter the diagram on the left or right side of the column representing the danger level and then use the diagonal colour code.
Can the new subdivisions be disregarded?
Although, as with any forecast, incorrect assessments are possible, evaluations show that the subdivisions reflect the danger more accurately as a rule.
Disregarding the subdivision bars access to additional information that would facilitate a better assessment of the risk.
“In any event, it is essential to continue taking the danger level seriously,” concludes Kurt Winkler.
“A 3-minus remains a level 3 and indicates ‘considerable avalanche danger’ – it is not to be taken lightly or treated as though it were a 2.”
Tajikistan Avalanche Early Warning System – A Life-Saving System. Georgia Tech engineering student Mirza Samnani volunteered over 1,000 hours to develop early warning systems for avalanches in Tajikistan.
Samnani hiked over 9,200 feet — three times — to install two weather stations in the village of Manem in Tajikistan. This project has the potential to affect more than 1 million people in hundreds of villages in the high mountainous regions of Central and South Asia including Tajikistan, Afghanistan, and Pakistan that are prone to avalanches every winter. It can also be scaled to other parts of the world where avalanches pose a high risk.
Samnani was contacted by a member of Madad, a nonprofit organization that works to save lives within communities that are vulnerable to climate change. The group came across his previous work building life-saving robots for a hospital in his hometown of Mumbai, India, during the pandemic. Seeing his love for helping others and his engineering skills, they invited Samnani to join Madad.
High mountain regions in Central Asia, Karakoram, Hindu-Kush, and the Himalayas are prone to snow avalanches. Their frequency and intensity are expected to increase due to atmospheric warming. The lack of a village-level avalanche early warning system poses a significant challenge in warning communities for timely action to avert disaster. Currently, local volunteers manage weather monitoring posts, where they manually track temperature, rainfall, and snow depth.
The early warning system Samnani worked on has seven different sensors. They monitor weather and environmental conditions, including wind speed and direction, atmospheric pressure, temperature, humidity, rainfall, snowfall, solar radiation from the sun, solar radiation absorbed from the ground, snow depth, and seismic activity. These conditions help calculate the snowpack parameters and, based on the trigger threshold, the algorithm determines when to disseminate an alert or warning about a Tajikistan Avalanche.
The project faced uncertainty because of the rough terrain as well as strict regulations and policies in the region. Even after extensive testing, communication and power issues occurred, but these problems ended up serving as learning opportunities that ultimately improved the Tajikistan Avalanche system.
“This project presented me with a unique opportunity to utilize my engineering skills to develop a system to predict the risk of an avalanche. In just 10 months our engineering team took this project from concept to installation. I am excited for the data that we will receive from these pilot units in the upcoming months. I plan to continue to work with this project and other important projects that will help improve the quality of life for people living in poor or dangerous conditions,” Samnani said.
Currently, he and the group are collecting and monitoring the data being received from each warning system tower and studying the integrity and performance of the towers. In the next few years, more of these stations will be deployed to cover more than 600 high-risk villages in central and south Asia and eventually will incorporate sirens and text and phone alerts.
New avalanche nonprofit will provide financial aid for Forest Service
NH (Nov. 15, 2022) – Friends of Tuckerman Ravine and White Mountain Avalanche Education Foundation, both longtime nonprofits of Mt. Washington Valley, have merged. The merger will support and promote the Mount Washington Avalanche Center, a Forest Service operation.
Both groups have previously been critical to the success of the Forest Service center, bestowing financial support. This combined entity will enhance the ability to raise money.
“We are very excited about these organizations finally coming together after so many years. Both have the same goals, and now together, we can streamline our efforts to fund the Avalanche Center,” said Jake Risch, president of the new Avalanche Nonprofit – the Mount Washington Avalanche Center Foundation.
This fall, MWAC will host various events in support of the Forest Service :
The 12th annual Eastern Snow and Avalanche Workshop takes place on Nov. 17–18
Winter Backcountry Kickoff Party took place on Nov. 19, in Intervale, NH.
Can we use Artificial Intelligence for Avalanche Forecasting?
Even the best forecasters only reach about 75% accuracy in their avalanche predictions. After all, they are only human. One of the greatest challenges is in the fact that avalanche danger cannot be precisely measured or communicated. It is, therefore, a matter of expert assessment and opinion by humans.
Last season the Swiss Institute for Snow and Avalanche Research successfully tested a first-of-its-kind, artificial intelligence computer program to assist in its avalanche forecasts. The SLF is a world leader in the field of avalanche forecasting and pushes the boundaries of avalanche forecasting methods.
SLF human observers collect measurements from over 120 different remote automatic weather stations located across the Swiss Alps. They then analyze the data, make interpretations, and combine opinions into a single bulletin published at 5 pm each day.
SLF researchers tested the new AI model as a “second opinion” against the human-made forecasts. In many cases the model agreed, but sometimes it did not. The differences are then reviewed.
The new Artificial Intelligence for Avalanche Forecasting model was trained using more than two decades-worth of weather and forecast information. SLF researchers now estimate the program is about 75% accurate in its model predictions. SLF forecaster Frank Techel describes the Artificial Intelligence for Avalanche Forecasting:
“The computer analyses the data in a different way than we do. That’s why it occasionally arrives at a slightly different conclusion. The computer forecast is very useful, especially for drawing exact boundaries between regions in which different danger levels prevail.”
The model, however, is limited to dry-snow avalanches. The SLF is planning on introducing a new Artificial Intelligence for Avalanche Forecasting system capable of modeling wet-snow avalanches as well as snowpack stability next season.
