Decennial infrasonic array analysis of snow-avalanche activity and its weather forcing in Pennine Alps: implications for forecasting

Snow avalanches rank among the deadliest natural hazards in mountain environments worldwide. To date, forecasting is mostly based on measuring meteorological forcing, aiming at assessing the probability of event triggering in a certain area. To validate forecast models, information on avalanche occurrence is critical. However, real-time avalanche detection is still challenging and generally limited to radar or visual surveillance of one or a few known channels; here the need for novel monitoring solutions. In the last decades, infrasound has proven to be one of the most promising tools for real-time detection of avalanches. Indeed, snow avalanches, moving downhill, generate acoustic pressure waves in the air, which can be recorded with an array of infrasonic sensors that allows to detect and characterize the source. However, many difficulties still exist, mostly connected to the discrimination of the avalanche infrasound among the signals radiated by other natural or anthropic infrasonic sources active at the Earth's surface or in the atmosphere.

Here we present an analysis of >10 years of data recorded by a small-aperture infrasonic array deployed at an altitude of ~2000 m in Valle d'Aosta (Itay). To detect snow-avalanche events, we develop an algorithm aimed at identifying avalanche signals in the recorded infrasound dataset and calibrated on two avalanche crises occurred in the site. The identified avalanche-type infrasonic signals are then compared to local meteorological data and avalanche bulletins, to test the accuracy of our algorithm. Several clusters of avalanche-type infrasonic signals are identified on days with favourable weather conditions for the triggering of snow avalanches. Our study also allows us to investigate the meteorological forcing of snow avalanches in the Pennine Alps, showing that avalanche storms are induced preferentially as a result of the destabilisation of thick snow accumulations, but also highlighting the importance of weather patterns at seasonal scale.

This study was financially supported by the National Recovery and Resilience Plan, Mission 4 Component 2 - Investment 1.4 - NATIONAL CENTER FOR HPC, BIG DATA AND QUANTUM COMPUTING - funded by the European Union - NextGenerationEU - CUPB83C22002830001.