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The Cryosphere An interactive open-access journal of the European Geosciences Union
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Short summary
Snow avalanches threaten people and infrastructure snow-covered mountain regions. To mitigate the effects of avalanches warnings are issued by public forecasting services. Presently, the five danger levels are described in qualitative terms. We aim to characterize the avalanche danger levels based on expert field observations of snow instability. Our findings contribute to an evidence-based description of the danger levels and eventually improve consistency and accuracy of avalanche forecasts.
Preprints
https://doi.org/10.5194/tc-2020-350
https://doi.org/10.5194/tc-2020-350

  21 Dec 2020

21 Dec 2020

Review status: this preprint is currently under review for the journal TC.

Avalanche danger level characteristics from field observations of snow instability

Jürg Schweizer1, Christoph Mitterer2, Benjamin Reuter3, and Frank Techel1 Jürg Schweizer et al.
  • 1WSL Institute for Snow and Avalanche Research SLF, Davos, Switzerland
  • 2Avalanche Forecasting Service Tyrol, Innsbruck, Austria
  • 3Centre d’Etude de la Neige, CEN, CNRM, MétéoFrance, Grenoble, France

Abstract. Avalanche danger levels are described in qualitative terms that mostly are not amenable to measurements or observations. However, estimating and improving forecast consistency and accuracy requires descriptors that can be observed or measured. Therefore, we aim to characterize the avalanche danger levels based on expert field observations of snow instability. We analyzed 589 field observations by experienced researchers and forecasters recorded mostly in the region of Davos (Switzerland) during 18 winter seasons (2001–2002 to 2018–2019). The data include a snow profile with a stability test (rutschblock, RB) and observations on snow surface quality, drifting snow, signs of instability and avalanche activity. In addition, observers provided their estimate of the local avalanche danger level. A snow stability class (very poor, poor, fair, good, very good) was assigned to each profile based on RB score, RB release type and snowpack characteristics. First, we describe some of the key snowpack characteristics of the data set. In most cases, the failure layer included persistent grain types, even after a recent snowfall. We then related snow instability data to the local avalanche danger level. For the danger levels 1–Low to 4–High, we derived typical stability distributions. The proportions of profiles rated poor and very poor clearly increased with increasing danger level. For our data set, the proportions were 5 %, 13 %, 49 % and 63 % for the danger levels 1–Low to 4–High, respectively. Furthermore, we related the local avalanche danger level to the occurrence of signs of instability such as whumpfs, shooting cracks and recent avalanches. The absence of signs of instability was most closely related to 1–Low, the presence to 3–Considerable. Adding the snow stability class and the 3-day sum of new snow depth improved the discrimination between the lower three danger levels. Still, 2–Moderate was not well described. Nevertheless, we propose some typical situations that approximately characterize each of the danger levels. Obviously, there is no single easily observable set of parameters that would allow fully characterizing the avalanche danger levels. One reason for this shortcoming is the fact that the snow instability data we analyzed usually lack information on spatial frequency, which is needed to reliably assess the danger level.

Jürg Schweizer et al.

 
Status: open (until 15 Feb 2021)
Status: open (until 15 Feb 2021)
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Jürg Schweizer et al.

Jürg Schweizer et al.

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Short summary
Snow avalanches threaten people and infrastructure snow-covered mountain regions. To mitigate the effects of avalanches warnings are issued by public forecasting services. Presently, the five danger levels are described in qualitative terms. We aim to characterize the avalanche danger levels based on expert field observations of snow instability. Our findings contribute to an evidence-based description of the danger levels and eventually improve consistency and accuracy of avalanche forecasts.
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