NOTE: This is the second post that will address the general question of Why Is It So Complicated? This time we're going to talk about cold weather, avalanche forecasting, and persistent weak layers. Instead of engaging in endless speculation over the state of the winter snowpack, I'd like to take this opportunity to discuss the basic elements of forecasting, and what you can and cannot accomplish with forecasting.
Seeing The Future
Recently there have been a few online discussions about snow and weather conditions in the Pacific Northwest. With the current cold weather, and a generally colder winter forecast, a lot of people are wondering if persistent weaknesses will plague the snowpack this winter.
The straight answer to the above question is as follows: no one really knows what will happen in the Cascades this winter. What we know right now is that the current cold temperatures are almost certainly producing instability wherever the snowpack is shallow, and there is a high likelihood that the cold temperatures are producing surface instability in areas where the snowpack is relatively deep. It is very likely that faceting is widespread during clear, cold nights when the snow loses heat through long wave radiation loss. On solar aspects at high elevations, you might find radiation recrystallisation.
Armed with the knowledge that instability is developing in the snowpack, we can start to speculate about what might happen next.
In some areas, the instability may persist through the next couple of storm cycles, whereas in other areas, the first big avalanche cycle will clean things up. There's also the spectre of a pineapple express, which would produce widespread instability in many places, but not everywhere, while simultaneously producing a fantastic layer of "glue" to heal surface instabilities. On the other hand, a strong rain event could melt the facets and turn the snowpack into a layer of bulletproof concrete.
Of course, in the event any faceted snow is buried to a depth of about 1 metre, the existence of moderate temperatures would allow rounding to prevail in the snowpack...effectively healing instability in areas with deep snow cover. It could take a week in some areas, a couple of weeks in other areas, and in some areas the problem could indeed persist for the entire winter.
When you get down to it, just about anything could happen at this point.
Why Is It So Complicated?
In simple terms, it's complicated because no one knows the future interactions between terrain, snowpack, and weather. Therefore, no one knows whether or not persistent weaknesses will develop. As I often write on this blog, the chaotic interaction between terrain, snowpack, and weather is responsible for much of the uncertainty in backcountry avalanche forecasting. Rich Marriot writes, how can you forecast avalanches if you can't forecast the weather. The short answer is that you definitely can forecast avalanches, you just can't forecast avalanches very far into the future.
So, if the chaotic interaction between terrain, snowpack, and weather is responsible for a lot of uncertainty, it might be useful to understand why this is the case. To do this, we have to examine the discipline of forecasting. In the context of avalanche forecasting, wanting to know why there is so much uncertainty leads us directly to the following principles:
- Information Types & Relation to Perception
- Scales in Space and Time
Why Forecasting Is Difficult
Forecasting is concerned with producing an accurate picture of future events. For avalanches, we can consider information types such as Class I data, Class II data, and Class III data. We can also consider the relationship between perception and data from a specific class. To define the scope of the problem, we can consider scales of space and time, or in very simple terms, we want to know where ( space ) and when ( time ).
To issue any type of forecast, you start by gathering data, and then you subject this information to some form of analysis. If you happen to be out in the backcountry on a particular afternoon, you have the tremendous luxury of hindsight made available by knowing the previous weather, and by knowing something about the current mechanical structure of the snowpack. You can also make very specific observations of the environment, including observations of the terrain and current snow deposition patterns.
On our theoretical afternoon, you also know the current weather. So while it is generally more difficult to issue a precise forecast, you also just happen to have access to an incredible amount of information on which to base any such forecast. To make things even easier, you're only concerned with issuing a forecast for a very short time, and for a very limited number of places. On the other hand, long-range forecasts are based on theoretical weather data ( Class III ), and there is always high uncertainty associated with such data. High uncertainty means that you're much more likely to make errors and blow the forecast as result.
For this reason, forecasts for the immediate time frame and for a small geographic area, often called nowcasts, are the most accurate. A forecast for a few days ahead is less accurate, and a long-range forecast might not be accurate in any sense. Nowcasts are more accurate because uncertainty is lower when we actually know something about the variables affecting the current situation. Unfortunately, we usually can't know the variables that will create or affect situations in the future.
This means that for any date far enough in the future, for a large area, uncertainty is essentially unlimited. If you want to see how this works, issue a forecast for your life over the next minute. What about a forecast for the next hour. What about the next 24 hours. What about next week? What about next month? Three months from now? Three years from now?
Bayesian Logic
The dynamic, integative process humans that use to conduct avalanche forecasting can be referred to as a Bayesian activity. We say that this process is dynamic, because it is active, and because it changes as information is collected. The process itself is integrative because you must consider the evidence as a whole, rather than as separate pieces. This is reflected by the following formula:
- Prior × Likelihood = Posterior ( or, what follows )
- The combination of Past Conditions and Current Conditions = Forecast
The most important characteristics of Bayesian revision is the ability for a single piece of data to change the forecast. That means, you throw out the old forecast as you acquire additional data. If you observe unstable snow, your forecast must change.
Try It Out
Here are some exercises:
- Forecast snowpack instability for the Cascade Mountains during winter 2010-2011. ( You can also choose your home mountain range if you don't live in the Cascades. )
- Forecast snowpack instability for Phantom Trees backcountry ski run on November 30th, 2010 at 2:30pm. ( You can also choose your own favourite backcountry ski run. )
- When you're finished with your forecasts, write a short snippet about which forecast is more precise and why it is more precise.
- Remember, it's easy to confuse accuracy with precision, but they are not the same thing at all.
With respect to the Cascades, it seems pretty likely that another big dump will produce significant instability. But such general forecasts are easy to issue because it's well known that large dumps of snow produce significant snowpack instability. When we consider the uncertainty of long range forecasting, it's very easy to see why it's all but impossible to say anything about an entire winter.
As usual, we'll just have to wait and see.
But remember, theoretical avalanches aren't dangerous. It's the real avalanches that you have to watch out for, and real avalanches always happen at a very specific place and time.
Addendum
Regardless of the specific place and time in which you find yourself, Happy Holidays to all my readers.
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