Articles | Volume 16, issue 11
https://doi.org/10.5194/tc-16-4593-2022
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, Alec van Herwijnen, Frank Techel, and Jürg Schweizer

Data sets

Observed and simulated snow profile data Stephanie Mayer, Alec van Herwijnen, Frank Techel, and Jürg Schweizer https://doi.org/10.16904/envidat.351

Model code and software

sarp.snowprofile.pyface F. Herla https://bitbucket.org/sfu-arp/sarp.snowprofile.pyface/src/master/

Random forest model for the assessment of snow instability S. Mayer https://gitlabext.wsl.ch/mayers/random_forest_snow_instability_model

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.