I originally came up with this idea when I was working on another project to help the Ski Guides of Hokkaido Japan share snow and avalanche information as there is no avalanche forecasts there.
In the US (as opposed to Canada and Europe) our avalanche forecasting is generally only lightly funded. I wanted to find a way to increase the capability of our forecasting while also building a platform which allowed us to improve the forecast products themselves. The Open Avalanche Project is meant to both be a data and experimentation platform for this. Operationalizing the data and using ML to predict the aspects of an avalanche forecast seemed like a good way to begin to move towards that goal.
This goes beyond what existing avalanche forecasts do in that it has a gridded resolution as opposed to a regional estimate. It also has been built to operate at scale as I would like to cover every avalanche hazard area in the world with a forecast.
I'd love to answer any questions folks have about the approach taken or the goals of the project.
What are the weather inputs into the models and do you think that will capture enough? Wind transportation seems easy enough to think about but stuff like hoar frost seems harder to predict using available weather data, but maybe that's just clear cold days?
I do wonder as well whether the models should actually be regionally specific. Sure avy patterns are different but that likely falls out if you are basing it all on weather. IE, WA can act like CO when we have many weeks in a row of cold and vice versa during warm spells there.
Anyways, will be keeping an eye on this. Enabling places with no current forecast to have something at all is a noble goal, though I am skeptical that the recent WA deaths would have been prevented, that's just education and group dynamics sadly.
SNODAS-which has things like snowpack temperature and sublimation rates
The input combines these and has a lookback of ~21 days to allow for things like buried hoar frost. I do think there is work to be done to validate that buried weak layers are well represented in the model but currently the model is a pretty good representation of the human forecasts which do account for those.
I do agree that regional variation is a factor. One example is that I feature engineered a Long Term Cold feature which we know is a primary cause of snowpack faceting leading to buried deep layers. This is more of a continental feature of avalanches as opposed to a coastal. My model doesn't consider this feature important. I currently attribute this to being only trained on costal forecasts. I have reached out to both to the Colorado Avalanche and Utah avalanche centers to work on how to best incorporate their historical forecast data in to the modeling to assess this further.
I would potentially be interested in contributing to this. One of the approaches I have thought would be beneficial is to generate probabilities of a slide occuring on a given slope using snow profiles as features in the model. Here we have the Utah Avalanche Center who go visit slides and generate snowpack reports like this: https://utahavalanchecenter.org/avalanches/37960
There are also a bunch of other snow pits dug with reports on slopes where a slide didn't occur. I would be neat to be able to plug in a slope (aspect, pitch, elevation, wind loading, etc) and have a red/yellow/green generated. The problem I ran into was obtaining the data - it all looks to be stored in random places.
Seems like 3 could be a whole system of grading or simply 3- or 3+. I know that there is huge risk with adding that indicator as people then take it for 'gospel' but informed is better than ignorance.