Snow profile alignment and similarity assessment for aggregating, clustering, and evaluating snowpack model output for avalanche forecasting

Abstract

Abstract. Snowpack models simulate the evolution of the snow stratigraphy based on meteorological inputs and have the potential to support avalanche risk management operations with complementary information relevant for their avalanche hazard assessment, especially in data-sparse regions or at times of unfavorable weather and hazard conditions. However, the adoption of snowpack models in operational avalanche forecasting has been limited, predominantly due to missing data processing algorithms and uncertainty around model validity. Thus, to enhance the usefulness of snowpack models for the avalanche industry, numerical methods are required that evaluate and summarize snowpack model output in accessible and relevant ways. We present algorithms that compare and assess generic snowpack data from both human observations and models, which consist of multidimensional sequences describing the snow characteristics of grain type, hardness, and age. Our approach exploits Dynamic Time Warping, a well-established method in the data sciences, to match layers between snow profiles and thereby align them. The similarity of the aligned profiles is then evaluated by our independent similarity measure based on characteristics relevant for avalanche hazard assessment. Since our methods provide the necessary quantitative link to data clustering and aggregating methods, we demonstrate how snowpack model output can be grouped and summarized according to similar hazard conditions. By emulating aspects of the human avalanche hazard assessment process, our methods aim to promote the operational application of snowpack models so that avalanche forecasters can begin to build an understanding of how to interpret and trust operational snowpack simulations.