Stochastic modeling of blowing snow: Analyzing risk and deposition time dynamics

Abstract

Blowing snow poses significant safety risks on roadways due to reduced visibility and dangerous pavement conditions, leading to an increased likelihood of vehicular incidents. This study aims to estimate the probability of blowing snow accurately and reliably by accounting for the random effects of cohesion and sintering on snow particle interactions. The Monte Carlo simulation was performed using the critical wind speed formula by He and Ohara (2017) for snow incipient motion to stochastically predict the probability of blowing snow events. The temporal variability of wind speed was characterized using maximum likelihood estimation (MLE) statistics based on the high-frequency wind data collected near Interstate 80 (I-80). The combined random variables of wind and critical wind speed (blowing snow index) can determine the probability of blowing snow occurrences over specific periods of time. The developed Stochastic Blowing Snow (SBS) model was calibrated and validated using the continuous snow flux measurements at seven ISAW monitoring sites in the Alps. The model showed promising results, effectively distinguishing between periods of high and low blowing snow risk in open terrain of Wyoming, USA, as well as in the Alps.