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

7Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.
Get full text

Abstract

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.

Cite

CITATION STYLE

APA

Herla, F., Haegeli, P., & Mair, P. (2022). A data exploration tool for averaging and accessing large data sets of snow stratigraphy profiles useful for avalanche forecasting. Cryosphere, 16(8), 3149–3162. https://doi.org/10.5194/tc-16-3149-2022

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free