Articles | Volume 16, issue 11
The Cryosphere, 16, 4593–4615, 2022
https://doi.org/10.5194/tc-16-4593-2022
The Cryosphere, 16, 4593–4615, 2022
https://doi.org/10.5194/tc-16-4593-2022
Research article
03 Nov 2022
Research article | 03 Nov 2022

A random forest model to assess snow instability from simulated snow stratigraphy

Stephanie Mayer et al.

Download

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on tc-2022-34', Pascal Hagenmuller, 07 Apr 2022
    • AC1: 'Reply on RC1', Stephanie Mayer, 06 Jul 2022
  • RC2: 'Comment on tc-2022-34', Edward Bair, 10 Jun 2022
    • AC2: 'Reply on RC2', Stephanie Mayer, 06 Jul 2022
  • RC3: 'Comment on tc-2022-34', Anonymous Referee #3, 12 Jun 2022
    • AC3: 'Reply on RC3', Stephanie Mayer, 06 Jul 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Publish subject to minor revisions (review by editor) (28 Jul 2022) by Guillaume Chambon
AR by Stephanie Mayer on behalf of the Authors (25 Aug 2022)  Author's response    Author's tracked changes    Manuscript
ED: Publish as is (01 Sep 2022) by Guillaume Chambon
Download
Short summary
Information on snow instability is crucial for avalanche forecasting. We introduce a novel machine-learning-based method to assess snow instability from snow stratigraphy simulated with the snow cover model SNOWPACK. To develop the model, we compared observed and simulated snow profiles. Our model provides a probability of instability for every layer of a simulated snow profile, which allows detection of the weakest layer and assessment of its degree of instability with one single index.