A data exploration tool for averaging and accessing large data sets of snow stratigraphy profiles useful for avalanche forecasting

• Published in: The Cryosphere
• Main Authors: Herla, Florian, Haegeli, Pascal, Mair, Patrick
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2022
• Subjects: article; Verlagsveröffentlichung; The Cryosphere
• Online Access: https://doi.org/10.5194/tc-16-3149-2022; https://noa.gwlb.de/receive/cop_mods_00062097; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00061429/tc-16-3149-2022.pdf; https://tc.copernicus.org/articles/16/3149/2022/tc-16-3149-2022.pdf

Snowpack models can provide detailed insight about the evolution of the snow stratigraphy in a way that is not possible with direct observations. However, the lack of suitable data aggregation methods currently prevents the effective use of the available information, which is commonly reduced to bulk properties and summary statistics of the entire snow column or individual grid cells. This is only of limited value for operational avalanche forecasting and has substantially hampered the application of spatially distributed simulations, as well as the development of comprehensive ensemble systems. To address this challenge, we present an averaging algorithm for snow profiles that effectively synthesizes large numbers of snow profiles into a meaningful overall perspective of the existing conditions. Notably, the algorithm enables compiling of informative summary statistics and distributions of snowpack layers, which creates new opportunities for presenting and analyzing distributed and ensemble snowpack simulations.