Articles | Volume 20, issue 11
https://doi.org/10.5194/nhess-20-2873-2020
https://doi.org/10.5194/nhess-20-2873-2020
Research article
 | 
02 Nov 2020
Research article |  | 02 Nov 2020

Sensitivity of modeled snow stability data to meteorological input uncertainty

Bettina Richter, Alec van Herwijnen, Mathias W. Rotach, and Jürg Schweizer

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Cited articles

Bartelt, P. and Lehning, M.: A physical SNOWPACK model for the Swiss avalanche warning Part I: Numerical model, Cold Reg. Sci. Technol., 35, 123–145, https://doi.org/10.1016/S0165-232X(02)00074-5, 2002. a
Bellaire, S. and Jamieson, B.: Nowcast with a forecast–snow cover simulations on slopes, in: Proceedings of International Snow Science Workshop, Anchorage, USA, 172–178, 2012. a
Bellaire, S., Jamieson, B., and Fierz, C.: Forcing the snow-cover model SNOWPACK with forecasted weather data, The Cryosphere, 5, 1115–1125, https://doi.org/10.5194/tc-5-1115-2011, 2011. a, b, c, d
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, b
Birkeland, K. W.: Terminology and Predominant Processes Associated with the Formation of Weak Layers of Near-Surface Faceted Crystals in the Mountain Snowpack, Arct. Alp. Res., 30, 193–199, https://doi.org/10.2307/1552134, 1998. a
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We investigated the sensitivity of modeled snow instability to uncertainties in meteorological input, typically found in complex terrain. The formation of the weak layer was very robust due to the long dry period, indicated by a widespread avalanche problem. Once a weak layer has formed, precipitation mostly determined slab and weak layer properties and hence snow instability. When spatially assessing snow instability for avalanche forecasting, accurate precipitation patterns have to be known.
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