From vol 123, Spring 2020
By Simon Horton

HOW TECHNOLOGY TRANSFORMED WEATHER FORECASTING, AND WHAT IT COULD DO TO AVALANCHE FORECASTING.

AVALANCHE CANADA FORECASTER GRANT HELGESON TESTS SOME PROTOTYPE FORECASTING TOOLS. // ALEX COOPER

WEATHER FORECASTING HAS RADICALLY transformed over the past century. In the 1920s, field observers reported current weather conditions back to meteorologists, who would draw up weather maps and extrapolate the weather for the next few days. Fast forward 100 years and precise forecasts are possible in mountainous terrain thanks to a global network of earth observations, advances in scientific understanding, and some of the most powerful supercomputers in the world. 

This raises the question, will avalanche forecasting head down a similar transition from field-based to computer-based work?

Certainly not anytime soon, but some form of technological progression is inevitable. The transition for weather forecasting took decades, starting around the time of the Second World War. Growth in aviation enhanced our interest in the atmosphere, that in turn changed our perspective of the weather. In the decades that followed, space exploration led to weather satellites that gave us an even broader view of the atmosphere. Then, early computers began crunching numbers to help with these extrapolations.

Despite these technologies, weather forecasting remained deeply rooted in experience-based pattern recognition for several more decades. By the 1970s, computer models began giving reasonable upper air forecasts (such as jet stream forecasts), but the Second World War-era style of human-centric forecasting still prevailed. the tides turned in the 1980s and 1990s when computer forecasts became more accurate and forecasters eventually learned when they
could and could not trust them. Trust in the ability of computer models to accurately predict weather continues to grow.

Avalanche forecasting currently relies on manual field observations and experience-based pattern recognition comparable to methods used by Second World War-era weather forecasters. However, over the past decades, the avalanche research community has developed remote sensing and computer modelling methods that have similar potential to observe, understand, and predict avalanche conditions. These technologies are at a similar status to weather forecasting technologies from the 1960s or 1970s. New methods are becoming available, but we don’t really know how to implement them into daily work.

As we saw in weather forecasting, these barriers were eventually overcome as the technology improved. We can speculate what a future avalanche forecasting system might look like by looking at how similar technology transformed weather prediction.

OBSERVATIONS AND PREDICTIONS
Modern weather forecasters use interactive dashboards that combine field observations, satellite imagery, and computer predictions to form a comprehensive picture of current and future weather conditions. Could this be what our workspaces look like in the future? Maybe instead of simply reviewing field observations from your own and neighbouring operations, a future avalanche forecasting tool will combine these observations with automated maps of avalanche activity, virtual snow profiles, and computer predictions of future conditions. to help form a more comprehensive picture of avalanche hazard. Even if it’s speculation, it’s important to understand the potential for technology to change avalanche work so we can shape it in a way that we all benefit.

The technologies impacting weather and avalanche forecasting broadly fit into two categories: ones that observe the conditions and ones that predict the conditions. In terms of observing conditions, weather satellites were the game changer that allowed us to expand from point observations to continuous spatial coverage. The same thing could happen with avalanche and snowpack observations. Networks of detectors that sense vibrations from avalanches are already installed, and recent research out of Europe has shown impressive accuracy in detecting avalanche debris in near real-time using radar-based satellites. Testing in Norway has found these satellites, which can even see through clouds, can provide updated daily maps of avalanche debris with complete spatial coverage across mountainous terrain. Imagine being able to see map of every avalanche that ran in Canada over the past 24 hours!

Remote sensors—from either satellites, aircrafts, drones, or on the ground—can also tell us about the snowpack. Deriving maps of snow-covered areas is already straightforward, but there is an increasing ability to sense snowpack layering remotely too. There are still many hurdles to getting actual x-ray vision into the snowpack—detecting thin weak layers is difficult, wet snow and crusts create a lot of errors, and all remote sensing technologies struggle in steep complex terrain. Advances in remote sensing are happening quickly, but most likely will need to be supplemented with other information to give us the best possible picture of avalanche conditions.

This is where prediction comes in. Our understanding of weather evolved in the 1920s with improved theories about the 3D structure of frontal systems. This didn’t have much impact on forecasting until several decades later when these theories were applied in computer models. This has become the backbone of modern weather forecasting.

Similar advances in snow science over the past decades have enhanced our understanding of how weak layers form, how avalanches release, and how they move downslope. The latest theories about snow microstructure and fracture mechanics are being implemented into computer models that simulate both the evolution of the snowpack structure as well as the movement of avalanches once they release. Could these models eventually have as much predictive power as weather models?

VIRTUAL SNOW PROFILES

A VIRTUAL SNOW PROFILE CONTRASTED WITH A HAND DRAWN ONE // SIMON HORTON

 One promising forecasting application is computer models that simulate snow profiles with weather and terrain data. Some European countries started testing virtual snow profiles in the 1990s and Avalanche Canada started testing similar products 10 years ago with a focus on remote, data-sparse areas where the models could be driven with weather forecasts. (You may have seen these virtual profiles on ARFI.) Since their inception, the consistent question is, "How accurate are they?" 

After 10 years of developing and testing these models for public forecasting in Canada, our impression is they provide a reasonable picture of general snowpack patterns, at least at some of the locations some of the time. Similar to how early weather models provided semi-realistic jet stream forecasts, the latest snowpack models can provide some big picture context to what is happening in the snowpack.

Weather models started being useful for big scale processes and we can expect snowpack models to follow suit. Rather than treating virtual profiles as slope-specific information, we can use snowpack models to look at regional differences in snowpack structure. For example, a surface hoar layer may form in the northern Monashees but not the southern Monashees because a storm tracking along the U.S. border causes too much cloud cover for surface hoar to form. Current applications of virtual snow profiles can already resolve some of these big-scale patterns. A focus of the avalanche research group at Simon Fraser University is developing ways practitioners can visualize and understand these patterns. 

Weather models eventually grew from basic advisory tools to predictive powerhouses. A big reason they became more accurate in the 1980s and 1990s is they were fed with more observations of current atmospheric conditions. Uncertainties about the current weather (such as the initial conditions) is one of the main sources of errors in weather models. Over the past few decades, the amount of observations that could be collected by satellites, radars, and airborne sensors has exploded. Once the initial weather conditions are processed, computer models simply apply the laws of physics to figure out what will most likely happen next.

Similarly, the biggest source of uncertainty in snowpack models is figuring out when, where, and how much it snows. The most realistic virtual snow profiles are the ones at locations with good snowfall measurements or forecasts. Once snow is on the ground, the laws of physics do a pretty good job of figuring out how it evolves, but measuring and predicting snowfall is still hard, and even the best measurements and models currently fall short. We can expect to continue getting better at knowing how much it snows with better weather forecasts and observation networks. From there, we can get better at predicting the snowpack structure.

MERGING OLD AND NEW

LEFT: METEROLOGY SERVICES OF CANADA RADAR IMAGE OF WESTERN CANADA // MSC

RIGHT: WEATHER MAP FROM SCANDINAVIA FROM 1874 // WIKIPEDIA CREATIVE COMMONS

 Early weather models were considered advisory tools. Meteorologists would check their own jet stream forecasts against the computer forecasts to increase confidence in their predictions. Computer-based systems were brought into the loop to augment human capabilities rather than replace them. They were a thinking tool. What really changed the day-to-day operations of meteorologists was the development of tools that merged old methods with the new technology.

In the late 1990s, interactive computer dashboards were developed that allowed forecasters to visualize field observations, satellite imagery, and computer model forecasts all in one place. Bringing different sources of information together allowed forecasters to get the most complete picture, filter through all the information, and weight each type of data according to its strengths and weaknesses.

The InfoEx is a current example of a forecasting tool that helps us build an understanding of avalanche conditions based on field observations and analysis by fellow professionals. Perhaps a future version of the InfoEx could combine the information we currently use with satellite maps of avalanche debris and some fusion of a remotely-sensed and computer-simulated snowpack visualizations to give us a more complete picture of avalanche conditions. If designed effectively, such a tool should help us assess hazards faster, more efficiently, and more accurately.

There’s lots of talk about machine learning and losing work to automation. This is scary for a community that looks to the mountains for an escape from the chaos of the digitized world. These emerging technologies could change aspects of avalanche work, but they won’t automate it. The transformation of weather forecasting took decades, billions of dollars, and major international collaborations. Even with that, weather is (probably) still easier to predict than the delicate slope-scale processes that cause avalanches. Plus, meteorologists still have jobs—they just spend less time analyzing data and more time communicating and making decisions.

The hope is that integrating new technologies will improve our hazard assessments so we can focus our time and energy on risk mitigation. Computers are great at pattern recognition—better than humans in many fields—but thankfully avalanche forecasting is more than that. Computer predictions will need to be verified, interrogated, and interpreted by humans. Ultimately, the complex mitigation decisions are up to us. Field work may become more targeted, where the computer system identifies the greatest uncertainties that need to be resolved by sending field teams to answer specific questions.

There will certainly be a learning curve, perhaps a slow one, where we gradually learn what technology can offer. We should all be engaged to learn about how these technologies work and, more importantly, have our say on how they should be implemented so we can make informed decisions about the future of avalanche forecasting.

REFERENCES
Benjamin, S. G., Brown, J. M., Brunet, G., Lynch, P., Saito, K., and Schlatter, T. W., 2019. 100 years of progress in forecasting and NWP applications, Meteorological Monographs, 59, 13.11-13.67.

Eckerstorfer, M., Bühler, Y., Frauenfelder, R., and Malnes, E., 2016. Remote sensing of snow avalanches: Recent advances, potential, and limitations, Cold Regions Science and Technology, 121, 126-140.

Horton, S., Nowak, S., and Haegeli, P., in review. Enhancing the operational value of snowpack models with visualization design principles, Natural Hazards and Earth System Sciences.

Morin, S., Horton, S., Techel, F., Bavay, M., Coléou, C., Fierz, C., Gobiet, A., Hagenmuller, P., Lafaysse, M., and Ližar, M., 2019. Application of physical snowpack models in support of operational avalanche hazard forecasting: A status report on current implementations and prospects for the future, Cold Regions Science and Technology, 102910.

From volume 111, winter 2015-16

By Bree Stefanson

AVALANCHE CONTROL ON EAST STROHN PEAK. PHOTO: MOTI

I MOVED TO STEWART SEVERAL YEARS AGO to work in the BC Ministry of Transportation and Infrastructure Bear Pass Avalanche Program. The afternoon before my first day of work was my first time driving Highway 37A. My jaw dropped and my neck strained as my eyes tried to take in the complexity of the avalanche paths surrounding my truck. Within the first few hours on the job, I was in a helicopter finding out where my targets were and when to deploy the charges. Each of the sixty 25kg bags that went out of the door created avalanches, including numerous showy size 3s and a handful of movie-quality size 4s. It was like nothing I had ever seen. The large paths have a vertical fall of over 2,000m and avalanches can travel upwards of four kilometres before the mass crosses the highway. The mid-sized paths were a couple hundred metres higher than the vertical fall of Castle Mountain Resort, where I had worked as a ski patroller. The smaller paths can bury a vehicle or push one into a lake.

The pass taught me a lot my first season. For example, the center of an approaching low-pressure system can slip a little south and surprise you by sneaking in the backdoor with outflow winds. Amazingly, the large avalanche paths can retain what seems to be an infinite amount of load and are capable of producing size 5 avalanches. An impressive display of nature, the large avalanches command respect for the potential damage they can produce. It was hard not to get caught up in focusing on the large paths, but the “small” paths can still put a size 3 on the road. Also, the importance of clear, timely communication to the public became paramount when living in a community that becomes isolated once the road closes.

Coming into my second season I had a better idea of what to look for and what to expect. I also knew that I had just experienced an “average” season and hadn’t seen anything “above average.” I had come to appreciate the forecasting process which was well-established within the program. This process assesses the overall avalanche hazard for an unmodified snowpack and then applies that assessment to each individual path throughout the forecast area, taking significant occurrences into account. The paths are then individually ranked on the Ministry’s five level hazard scale to identify the paths of concern and dictate specific operational procedures that the maintenance contractor is required to follow while working within the avalanche area.

FRACTURE PROFILE ON ORE MOUNTAIN. PHOTO: MOTI

The 2014-15 season started warm and wet with average amounts of precipitation, but freezing levels were often above 1,000m. In the alpine, a significant instability was buried in the fall, and by Christmas a hard slab had developed over top. When we issued a Future Planned Event notifying our stakeholders that the highway would be closed for avalanche control, you can only imagine the feedback we got from surprised locals, as there wasn’t a flake to be found in town. The control mission was successful, with avalanches to size 4 crawling over nearly bare ground and terminating within 200m of the highway. This mission greatly reduced mass from our alpine start zones, and even though we had large deposits visible from the road, no one in town was buying my story.

The first time I heard the term “atmospheric river,” a significant storm that was forecast to track well to the south had shifted its course and was headed towards Stewart. The millimetres were stacking up on the XTs and we were all trying to forecast the effects of 100mm in a 30-hour period on our snowpack. We compared the various forecast models, attempting to pinpoint the peak of the storm. We applied the forecast to our current snowpack and attempted to hypothesize the timing and character of the expected avalanche cycles. Our theory was that there would be too much rapid loading for the paths to retain significant mass, and the paths would shed during peak loading. We anticipated the freezing levels to rise and induce a secondary wet cycle as the snowpack became saturated. With saturated runout zones, deposits from large avalanches initiating later in the storm would slow down, ideally stopping above the road. We planned a control mission for the peak of the storm, targeting rain-saturated paths below treeline. This closure would also empty the road of travelling public, allowing us to get a handle on the avalanche character and where the deposits were actually stopping without added pressure.

The storm was intense, starting with 3-5mm H2OE per hour, steady for hours. Twelve hours into the storm, snow levels were above 1,100m and precipitation rates had reached up to 7.4mm an hour. I was relieved once the road was closed for control, as the large paths were retaining their mass.

AN AVALANCHE DUSTS BEAR PASS. PHOTO: MOTI

Fortunately, the ceiling was high enough to access the below treeline start zones and the snowpack was saturated enough to release wet loose and slab avalanches. After a three hour mission, our BTL concerns were mitigated and we had plenty of daylight to fly through the pass to observe the natural occurrences. Sure enough, as we flew by, we saw every large alpine and mid-elevation path had healthy deposits below them, with all deposits stopping above the highway. We ran through our path hazard avalanche risk table and all of our paths of concern had released, with any residual hazard still falling well within our operational risk band. We made the call to open the road and continued to monitor avalanche activity.

The storm ended as fast as it came. When it was all said and done, Stewart had received 146mm over a couple of days with 110mm falling within a 24-hour period. This exceeded by twice as much the previously recorded maximum precipitation amount for a 24-hour period in Stewart in March.

At first light I drove through the pass with the clear morning sky showing crowns throughout the pass. By 10:00 a.m., the wind increased, grabbing all the new snow available for transport and quickly erasing the crowns.

Fortunately, the weather continued to improve and conditions were favourable for control the following day. We spent most of our mission above the treeline, producing numerous size 3 to 3.5 avalanches and a few size 4s. The deposits of these reloaded paths easily traveled over the debris piles produced during the storm, with some just stopping shy of the road. The avalanches were stunning. They were dry and moving fast until they hit the saturated snow, where they’d push a slow moving finger of wet mass through the run-out zone. It was impressive to see the power of the air blasts from the two plunging avalanches that dusted the road.

A PANORAMA OF THE FRACTURE LINE ON ORE MOUNTAIN. PHOTO: MOTI

Following the mission, I drove through the pass to capture the toe distance mass of the deposits, and I reflected on the storm, the natural cycles and the control missions. I thought about the various avalanche path characteristics over the elevation bands and the many avalanche problems I had just seen. In one storm there was storm slab, persistent slab on surface hoar, large plunging, loose dry, loose wet, and wet slab, as well as the potential for large avalanches to detach huge fins of glacial ice amplifying the deposit size. I was glad that we had eliminated the deep slab problem formed earlier in the season as it had become active in slopes adjacent to the forecast area, and this would have increased the magnitude of the impacts to the highway during the storm.

The Bear Pass is a wild place to work during a significant storm event, and the area provides a fabulous opportunity to learn a lot about avalanches. I am grateful to have seen an event like this and to have had such a solid team to work with through the season. I’m now in my third season in the pass and from the deposits I’ve seen in the archived photos, all I really know is that I have a whole lot more to learn.