Item: Quantitative comparison of snow profiles
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Title: Quantitative comparison of snow profiles
Proceedings: International Snow Science Workshop Proceedings 2018, Innsbruck, Austria
Authors:
- Pascal Hagenmuller [ Météo-France - CNRS, CNRM/CEN, Grenoble, France ]
- Léo Viallon [ Météo-France - CNRS, CNRM/CEN, Grenoble, France ]
- Coline Bouchayer [ Météo-France - CNRS, CNRM/CEN, Grenoble, France ]
- Michaela Teich [ Department of Wildland Resources, Utah State University, Logan Utah, USA ]
- Matthieu Lafaysse [ Météo-France - CNRS, CNRM/CEN, Grenoble, France ]
- Vincent Vionnet [ Centre for Hydrology, University of Saskatchewan, Saskatoon Saskatchewan, Canada ] [ Météo-France - CNRS, CNRM/CEN, Grenoble, France ]
Date: 2018-10-07
Abstract: A snow profile, i.e. the variation of snow physical properties as a function of depth, captures the snow cover stratigraphy which is crucial for many applications such as the assessment of the avalanche danger. With the increasing use of distributed snowpack numerical models or electronic highly-resolved snow penetrometers, which generates a huge amount of data, there is a need for a robust and efficient method to compare and classify snow profiles. It has long been recognized that accounting for shifted layer position, i.e. layers at the same depth are not necessarily at the same position in the stratigraphic sequence, is crucial to obtain a meaningful metric. In this work, we present a new metric between snow profiles based on dynamic time warping that properly accounts for depth shifts in the stratigraphy. The new perspectives opened by this development are illustrated on three instances: the clustering of large spatially-distributed snowpack simulations, the correction of snowpack simulations with observed snow profiles and the quantification of the spatial variability of measured hardness profiles.
Object ID: ISSW2018_O10.5.pdf
Language of Article: English
Presenter(s):
Keywords: snow stratigraphy; sequence alignment; matching; spatial variability
Page Number(s): 876-879
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