Comparison of varied complexity parameterizations in estimating blowing snow occurrences

Abstract

Blowing snow has a major impact on the spatial–temporal evolution of snow cover. An accurate prediction of the initiation of snow particle movement is one of the prerequisites for blowing snow modeling. Previous studies have proposed varied complexity parameterizations to estimate the occurrence of blowing snow. However, a quantitative evaluation of the performance of different schemes in blowing snow identification remains lacking, particularly outside the regions where empirical schemes were proposed. This study details a comparative study of the performance of five distinct parameterizations with varying degrees of complexity in estimating the occurrence of blowing snow over the central French Alps. Our results show that less than half of blowing snow events can be accurately detected at most stations by traditional schemes, for example, the constant and temperature-based empirical threshold wind speed schemes, probability estimation, and physically based methods. In addition, the spatial variability of wind speed can result in considerable spatial variability in the occurrence of blowing snow, which cannot be captured by traditional methods. Decision tree-based models can detect blowing snow occurrences with higher accuracy, and the spatial variation in snow transport can be captured by including information on blowing snow occurrences at low wind speed stations. However, regardless of the training strategies employed, the decision tree-based model employed here estimated far too many false alarms. This study contributes to blowing snow modeling by presenting the current state-of-the-art and limitations of various schemes for detecting blowing snow occurrences, and highlights the importance of parameterization modification, key parameter calibration, and optimization.