The current model captures data from three separate inputs: The NAM weather forecast model--primarily wind, air temperature and precipitation values SNOTEL data--on the ground snow values include snowpack depth and SWE

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.