Item: Numerical investigation of factors causing near-surface metamorphism
Title: Numerical investigation of factors causing near-surface metamorphism
Proceedings: International Snow Science Workshop, Davos 2009, Proceedings
Authors: Andrew E. Slaughter and Edward E. Adams, Department of Civil Engineering, Montana State University, Bozeman, MT
Abstract: Buried layers of surface hoar or near-surface facets within the snowpack are well known to be the culprit in a majority of avalanches. Near-surface metamorphism has been the topic of a multitude of field, laboratory, and analytical investigations. Analytically, a variety of models are capable of reasonably modeling temperature in the snowpack. Using a computationally efficient 1-D thermal model, the SOBOL method of sensitivity analysis was implemented to exploit modern computational resources. To the authors’ knowledge, this has yet to be done for specific metamorphic processes. The resulting sensitivity indices quantify the relative importance of each input as well as the importance of the interaction between inputs. The sensitivity results for radiation-recrystallization confirm the conceptual understanding of the process indicating that thermal conductivity, albedo, and long- and short-wave radiation are the most influential, with the thermal conductivity being highly interactive. Surface hoar was heavily dependent on long-wave radiation and to some extent wind speed, air temperature, and humidity, but revealed little interaction between these terms. Finally, the optimum environmental and snow conditions conducive to radiation-recrystallization and surface hoar are quantified statistically. These numerically determined conditions compared well with recorded field data of radiation recrystallization and to a lesser extent with recorded surface hoar events. The research presented here is intended to be a tool among many for assessing near-surface metamorphism as well as designing additional experimentation.
Keywords: facets, surface hoar, radiation-recrystallization, numerical analysis, monte carlo, sensitivity
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