Preprints
https://doi.org/10.5194/tc-2022-29
https://doi.org/10.5194/tc-2022-29
 
09 Feb 2022
09 Feb 2022
Status: a revised version of this preprint is currently under review for the journal TC.

Brief communication: A numerical tool for averaging large data sets of snow stratigraphy profiles useful for avalanche forecasting

Florian Herla1, Pascal Haegeli1, and Patrick Mair2 Florian Herla et al.
  • 1Simon Fraser University, Burnaby, BC, Canada
  • 2Harvard University, Cambridge, MA, USA

Abstract. Snowpack models can provide detailed insight about the evolution of the snow stratigraphy in ways 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. To address this challenge, we present an averaging algorithm for snow profiles that can effectively synthesize 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.

Florian Herla et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-29', Frank Techel, 18 Feb 2022
  • RC2: 'Comment on tc-2022-29', Christoph Mitterer, 13 Apr 2022
    • AC1: 'Author Response to RC1 and RC2', Florian Herla, 11 May 2022

Florian Herla et al.

Data sets

An averaging algorithm for snow profiles—Data and Code Herla, F., Mair, P., and Haegeli, P. https://doi.org/10.17605/OSF.IO/7MA6G

Florian Herla et al.

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Short summary
We present an averaging algorithm for multidimensional snow stratigraphy profiles that elicits the predominant snow layering among large numbers of profiles and allows for compiling of informative summary statistics and distributions of snowpack layer properties. This creates new opportunities for presenting and analyzing operational snowpack simulations in support of avalanche forecasting.