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Refining the processing of paired time series data to improve velocity estimation in snow flows

journal contribution
posted on 08.08.2018, 11:11 authored by H.K. Truong, Chris KeylockChris Keylock, N. Eckert, H. Bellot, M. Naaim
For effective avalanche risk mitigation, numerical models with a correct description of snow rheology are needed. Conventionally, velocity in snow flow experiments is inferred by cross-correlating the voltage signals of paired sensors. The intention of this paper is to reconsider this problem to enhance processing of these data, leading to more effective estimates of fluctuating velocity quantities. The algorithm consists of a wavelet decomposition, a denoising step and a weighting method for the reconstituted signal. The resulting velocity time series are both consistent and informative, providing confidence that one can analyse not only the mean velocity profiles, but also the velocity distribution. Our approach is illustrated using a typical chute experiment undertaken at Col du Lac Blanc in the French Alps. Not only has the mean velocity profile a more complex shape than the bilinear one postulated from the results of the standard cross-correlation processing, but the probability distribution functions of the velocity at different heights is much more continuous and dispersed, revealing interesting new patterns of greater dynamical relevance.

Funding

This study was funded by the European Regional Development Fund through the French-Italian project called Monitoring for the Avalanche Provision, Prediction and Protection (MAP3). CJK was supported by Royal Academy of Engineering/Leverhulme Trust Senior Research Fellowship, LTSRF1516-12-89.

History

School

  • Architecture, Building and Civil Engineering

Published in

Cold Regions Science and Technology

Volume

151

Pages

75 - 88

Citation

TRUONG, H.K. ... et al, 2018. Refining the processing of paired time series data to improve velocity estimation in snow flows. Cold Regions Science and Technology, 151, pp.75-88.

Publisher

© Elsevier

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Acceptance date

05/03/2018

Publication date

2018

Notes

This paper is closed access.

ISSN

0165-232X

Language

en