16 Jun 2022
16 Jun 2022
Status: this preprint is currently under review for the journal TC.

Stochastic analysis of cone penetration tests in snow

Pyei Phyo Lin1, Isabel Peinke2, Pascal Hagenmuller2, Matthias Wächter1, M. Reza Rahimi Tabar1,3, and Joachim Peinke1 Pyei Phyo Lin et al.
  • 1ForWind, Institute of Physics, University of Oldenburg, Oldenburg, Germany
  • 2Univ. Grenoble Alpes, Université de Toulouse, Météo-France, CNRS, CNRM, Centre d’Études de la Neige, Grenoble, France
  • 3Department of Physics, Sharif University of Technology, Tehran 11155-9161, Iran

Abstract. Cone penetration tests have long been used to characterize the snowpack stratigraphy. With the development of sophisticated digital penetrometers such as the Snow MicroPenetrometer, vertical profiles of snow hardness can now be measured at a spatial resolution of a few microns. At this high vertical resolution and by using small penetrometer tips, more and more details of the penetration process get resolved, leading to much more stochastic signals. An accurate interpretation of these signals regarding snow characteristics requires employing advanced data analysis. Here, the failure of ice connections and the pushing aside of separated snow grains during cone penetration lead to a combination of a) diffusive noise, as in Brownian motion, and b) jumpy noise, as proposed by previous dedicated inversion methods. The determination of the Kramers-Moyal coefficients allows differentiating between diffusive and jumpy behaviors and determining the functional resistance dependencies of these stochastic contributions. We show how different snow types can be characterized by this combination of highly-resolved measurements and data analysis methods. In particular, we show that denser snow structures exhibited a more collective diffusive behavior supposedly related to the pushing aside of separated snow grains. On lighter structures with larger pore space, the measured hardness profile appeared to be characterized by stronger jump noise probably related to breaking single cohesive bonds. The proposed methodology provides new insights into the characterization of the snowpack stratigraphy with cone penetration tests.

Pyei Phyo Lin et al.

Status: open (until 11 Aug 2022)

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Pyei Phyo Lin et al.

Pyei Phyo Lin et al.


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Short summary
Characterization of layers of snowpack with highly resolved cone penetration tests leads to detailed fluctuating signals. We used advanced stochastic analysis to differentiate snow types by interpreting the signals as a mixture of continuous and discontinuous random fluctuations. These two types of fluctuations seem to correspond to different mechanisms of drag force generation during the experiments. The proposed methodology thus provides new insights to the characterization of snow layers.