Journal cover Journal topic
Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

IF value: 5.240
IF5.240
IF 5-year value: 5.768
IF 5-year
5.768
CiteScore value: 8.9
CiteScore
8.9
SNIP value: 1.713
SNIP1.713
IPP value: 5.53
IPP5.53
SJR value: 3.18
SJR3.18
Scimago H <br class='widget-line-break'>index value: 71
Scimago H
index
71
h5-index value: 51
h5-index51
Preprints
https://doi.org/10.5194/gmd-2020-171
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-2020-171
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: methods for assessment of models 10 Aug 2020

Submitted as: methods for assessment of models | 10 Aug 2020

Review status
A revised version of this preprint was accepted for the journal GMD and is expected to appear here in due course.

Snow profile alignment and similarity assessment for aggregating, clustering, and evaluating of snowpack model output for avalanche forecasting

Florian Herla1, Simon Horton1,2, Patrick Mair3, and Pascal Haegeli1 Florian Herla et al.
  • 1Simon Fraser University, Burnaby, BC, Canada
  • 2Avalanche Canada, Revelstoke, BC, Canada
  • 3Harvard University, Cambridge, MA, USA

Abstract. Snowpack models simulate the evolution of the snow stratigraphy based on meteorological inputs and have the potential to support avalanche risk management operations with complementary information relevant to their avalanche hazard assessment, especially in data-sparse regions or at times of unfavorable weather and hazard conditions. However, the adoption of snowpack models in operational avalanche forecasting has been limited, predominantly due to missing data processing algorithms and uncertainty around model validity. Thus, to enhance the usefulness of snowpack models for the avalanche industry, numerical methods are required that evaluate and summarize snowpack model output in accessible and relevant ways. We present algorithms that compare and assess generic snowpack data from both human observations and models. Our approach exploits Dynamic Time Warping, a well-established method in the data sciences, to match layers between snow profiles and thereby align them. The similarity of the aligned profiles is then evaluated by our independent similarity measure based on characteristics relevant for avalanche hazard assessment. Since our methods provide the necessary quantitative link to data clustering and aggregating methods, we demonstrate how snowpack model output can be grouped and summarized according to similar hazard conditions. Through emulating a human avalanche hazard assessment approach, our methods aim to promote the operational application of snowpack models so that avalanche forecasters can begin to build understanding in how to interpret and when to trust operational snowpack simulations.

Florian Herla et al.

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Printer-friendly Version - Printer-friendly version Supplement - Supplement

Florian Herla et al.

Model code and software

Snow profile alignment and similarity assessment---Data and Code Florian Herla, Simon Horton, Patrick Mair, and Pascal Haegeli https://doi.org/10.17605/OSF.IO/9V8AD

Florian Herla et al.

Viewed

Total article views: 267 (including HTML, PDF, and XML)
HTML PDF XML Total Supplement BibTeX EndNote
191 73 3 267 25 8 8
  • HTML: 191
  • PDF: 73
  • XML: 3
  • Total: 267
  • Supplement: 25
  • BibTeX: 8
  • EndNote: 8
Views and downloads (calculated since 10 Aug 2020)
Cumulative views and downloads (calculated since 10 Aug 2020)

Viewed (geographical distribution)

Total article views: 242 (including HTML, PDF, and XML) Thereof 239 with geography defined and 3 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 

Cited

Saved

No saved metrics found.

Discussed

No discussed metrics found.
Latest update: 01 Dec 2020
Publications Copernicus
Download
Short summary
The adoption of snowpack models in support of avalanche forecasting has been limited. To promote their operational application, we present a numerical method for processing and summarizing snow stratigraphy profiles. By emulating the data assimilation process of avalanche forecasters, our algorithm contributes to analysis and validation tools that will allow forecasters to familiarly interact with the simulations and develop an in-depth understanding of how to interpret and when to trust them.
The adoption of snowpack models in support of avalanche forecasting has been limited. To promote...
Citation