Numerical simulation of drifting snow: erosion and deposition models

Naaïm, Mohamed; Naaim-Bouvet, Florence; Martínez, Hugo

doi:10.1017/s0260305500014798

Cited by 61 publications

(54 citation statements)

References 7 publications

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“…The amount of snow eroded during a drift event depends on the cohesion of surface snow and the intensity of wind. For all patches, snow layers are removed from top downwards, until a nondriftable layer or the maximum amount of erosion is reached.The driftability index, introduced by Guyomarc'h and Mérindol [] and extended to a wider density range by Vionnet et al [], is computed for each layer at the end of a drift event using the maximum wind speed during the event.The maximum quantity of snow F that can be eroded from a patch during a time step Δ t is calculated after Naaim et al [, equation (14)]:

F = normalΔt ρ_{a} A ((), u_{*}^{2} - u_{* t}^{2}),

where ρ a is air density, A a coefficient depending on intergranular bonding of the snow, u * t is the threshold friction velocity, and u * is the friction velocity. According to Naaim et al [], we chose ρ a A =7 × 10 −4 kg m −4 s. u * t and u * are calculated after the parameterization of Louis []:

u_{*}^{2} = a^{2} u_{10}^{2} F_{m} ((), \frac{10}{z_{0}}, R_{i}),

where u 10 is ERA‐Interim wind speed at 10 m, a is the drag coefficient in neutral conditions, and R i the Richardson number of the boundary layer.…”

Section: Crocus Simulationsmentioning

confidence: 99%

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Modeling the impact of snow drift on the decameter-scale variability of snow properties on the Antarctic Plateau

Libois

Picard

Arnaud

et al. 2014

J. Geophys. Res. Atmos.

133

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The annual accumulation and the physical properties of snow close to the surface on the Antarctic Plateau are characterized by a large decameter‐scale variability resulting from snow drift that is not simulated by one‐dimensional snow evolution models. Here, the detailed snowpack model Crocus was adapted to Antarctic conditions and then modified to account for this drift‐induced variability using a stochastic snow redistribution scheme. For this, 50 simulations were run in parallel and were allowed to exchange snow mass according to rules driven by wind speed. These simple rules were developed and calibrated based on in situ pictures of the snow surface recorded for two years. The simulation performed with this new model shows three substantial improvements with respect to standard Crocus simulations. First, significant and rapid variations of snow height observed in hourly measurements are well reproduced, highlighting the crucial role of snow drift in snow accumulation. Second, the statistics of annual accumulation is also simulated successfully, including the years with net ablation which are as frequent as 15% in the observations and 11% in the simulation. Last, the simulated vertical profiles of snow density and specific surface area down to 50 cm depth were compared to 98 profiles measured at Dome C during the summer 2012–2013. The observed spatial variability is partly reproduced by the new model, especially close to the surface. The erosion/deposition processes explain why layers with density lower than 250 kg m−3 or specific surface area larger than 30 m2 kg−1 can be found deeper than 10 cm.

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F = normalΔt ρ_{a} A ((), u_{*}^{2} - u_{* t}^{2}),

u_{*}^{2} = a^{2} u_{10}^{2} F_{m} ((), \frac{10}{z_{0}}, R_{i}),

where u 10 is ERA‐Interim wind speed at 10 m, a is the drag coefficient in neutral conditions, and R i the Richardson number of the boundary layer.…”

Section: Crocus Simulationsmentioning

confidence: 99%

“…where a is air density, A a coefficient depending on intergranular bonding of the snow, u * t is the threshold friction velocity, and u * is the friction velocity. According to Naaim et al [1998], we chose a A=7 × 10 −4 kg m −4 s. u * t and u * are calculated after the parameterization of Louis [1979]:…”

Section: Multipatch Simulations (Mp Mp' and Mp")mentioning

confidence: 99%

Modeling the impact of snow drift on the decameter-scale variability of snow properties on the Antarctic Plateau

Libois

Picard

Arnaud

et al. 2014

J. Geophys. Res. Atmos.

133

View full text Add to dashboard Cite

show abstract

“…This kind of model was first developed by Uematsu et al [1991] and many researchers have since modified and improved the model. Naaim et al [1998] and Gauer [1998Gauer [ , 2001 divided the transport process into saltation and suspension, and applied a continuum theory to each layer separately. Fukushima et al [1999Fukushima et al [ , 2001 solved a set of conservation equations without splitting the transport mode.…”

Section: Introductionmentioning

confidence: 99%

Numerical simulation of snow saltation and suspension in a turbulent boundary layer

2004

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[1] A new numerical model was developed to describe the development of drifting snow on a flat surface. The model uses Lagrangian stochastic theory to account for turbulence effects on the suspension of snow grains, and also includes aerodynamic entrainment, the grain-bed collision process, wind modification by the grains, and a distribution of grain sizes. The calculated wind profile, shear stress, and mass flux near the surface agreed quantitatively with previous wind tunnel experiments. Because of turbulence, snow grains can reach 10 m high, a results that agrees with recent measurements but had not previously been simulated using saltation models. We also found that the steady state fluid shear stress exceeded the threshold stress, meaning that the grains were continually entrained by the fluid. A distinct change in the mass flux profile occurred at 0.1 m high for the following reason. Below 0.1 m, the particle inertia dominated the grain motion and turbulence had only a small effect on the motion; in contrast, above 0.1 m, most particles were less than 100 mm in diameter and their motion was mainly affected by the turbulence and not inertia. That is, the particles above 0.1 m were in suspension mode.

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“…Many studies focus on numerical simulations of the physical processes (Anderson and Haff, 1991;McEwan and Willets, 1991;Naaim et al, 1998;Shao and Li, 1999;Doorschot and Lehning, 2002). Measurements of sand and snow drift have recently mainly been carried out in wind tunnels (Nalpanis et al, 1993;Nishimura et al, 1998;Nishimura and Hunt, 2000).…”

Section: Introductionmentioning

confidence: 99%

Field measurements of snow-drift threshold and mass fluxes, and related model simulations

Jdoorschot

Lehning

Vrouwe

2004

Boundary-Layer Meteorol

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Field measurements were carried out to calculate the threshold friction velocity for snow saltation, and mass fluxes during snow drift. The wind was measured in three components by an ultrasonic anemometer, and the mass fluxes were determined using an optical sensor ('snow particle counter'), acoustic sensors ('Flowcapt') and mechanical traps. The threshold friction velocity was found to be correlated to the grain size (R 2 ¼ 0:75). The mass flux measurements were compared with numerical simulations of snow drift, and it was demonstrated that the maximum snow transport takes place at shear stress values of roughly two times the average shear stress over 20 min. By implementing a probability distribution for the shear stress the mass flux was simulated with only the mean measured value of the shear stress as input. This procedure enables the future use of the numerical model for operational applications.

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Numerical simulation of drifting snow: erosion and deposition models

Abstract: E Di ssipati on of the tu rbul ent kin etic energy (m 2 s :1)

Cited by 61 publications

References 7 publications

Modeling the impact of snow drift on the decameter-scale variability of snow properties on the Antarctic Plateau

Modeling the impact of snow drift on the decameter-scale variability of snow properties on the Antarctic Plateau

Numerical simulation of snow saltation and suspension in a turbulent boundary layer

Field measurements of snow-drift threshold and mass fluxes, and related model simulations

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