Articles | Volume 16, issue 8
https://doi.org/10.5194/tc-16-3149-2022
https://doi.org/10.5194/tc-16-3149-2022
Research article
 | 
03 Aug 2022
Research article |  | 03 Aug 2022

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

Florian Herla, Pascal Haegeli, and Patrick Mair

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Cited articles

Bellaire, S., van Herwijnen, A., Mitterer, C., and Schweizer, J.: On forecasting wet-snow avalanche activity using simulated snow cover data, Cold Reg. Sci. Technol., 144, 28–38, https://doi.org/10.1016/j.coldregions.2017.09.013, 2017. a
Giorgino, T.: Computing and Visualizing Dynamic Time Warping Alignments in R: The dtw Package, J. Stat. Softw., 31, 1–24, https://doi.org/10.18637/jss.v031.i07, 2009. a
Hagenmuller, P. and Pilloix, T.: A New Method for Comparing and Matching Snow Profiles, Application for Profiles Measured by Penetrometers, Front. Earth Sci., 4, https://doi.org/10.3389/feart.2016.00052, 2016. a
Herla, F., Horton, S., Mair, P., and Haegeli, P.: Snow profile alignment and similarity assessment for aggregating, clustering, and evaluating snowpack model output for avalanche forecasting, Geosci. Model Dev., 14, 239–258, https://doi.org/10.5194/gmd-14-239-2021, 2021. a, b, c, d, e, f, g, h, i, j, k, l, m
Herla, F., Horton, S., and Haegeli, P.: An informative large-scale validation of snowpack simulations in support of avalanche forecasting in Canada, in preparation, 2022a. a
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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 and may inspire new ways of processing profiles and time series in other geophysical contexts.
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