Glide-snow avalanches pose a major challenge for operational forecasting and local avalanche authorities. Although their key prerequisite, a moist interface between the snowpack and smooth ground, is well known, predicting the timing of glide-snow avalanches remains difficult. We analyzed five seasons of avalanche monitoring data in the Planneralm area of Styria, Austria. Glide-snow avalanche activity in the study area follows typical temporal patterns, with the highest release probability in the early afternoon and peak activity from mid-March to mid-April. Using meteorological data and avalanche observations as input, we trained machine-learning models to predict hours with glide-snow avalanche release. The most significant predictors were the mean air temperature of the preceding 48h, the day of the winter season, the hour of the day, and the decrease in snow height. The combination of those variables suggests a longer-term predisposition toward glide-snow avalanche release, as well as short-term driving factors. Our decision tree model correctly identified the vast majority of avalanche hours (recall 90%) at the cost of a moderate false alarm rate (15%). Our model could support operational glide-snow avalanche forecasting by identifying hours with elevated glide-snow potential that warrant increased attention and may require warnings or temporary closures by local authorities.
Reiweger et al. (Fri,) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: