Statistical properties of fresh snow roughness

Manes, Costantino; Guala, Michele; Löwe, Henning; Bartlett, Stuart; Egli, Luca; Lehning, Michael

doi:10.1029/2007wr006689

Cited by 31 publications

(40 citation statements)

References 31 publications

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“…Taking into account the snow-covered topography, we used a surface roughness length of z 0 = 0.005 m, which is in agreement with typical values for snow (Doorschot et al, 2004) and (Manes et al, 2008). Note that roughness lengths for momentum z 0 , heat z H and moisture z Q are approximated to be equal.…”

Section: Numerical Modelsmentioning

confidence: 55%

Micrometeorological processes driving snow ablation in an Alpine catchment

Mott¹,

Egli²,

Grünewald³

et al. 2011

The Cryosphere

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Abstract. Mountain snow covers typically become patchy over the course of a melting season. The snow pattern during melt is mainly governed by the end of winter snow depth distribution and the local energy balance. The objective of this study is to investigate micro-meteorological processes driving snow ablation in an Alpine catchment. For this purpose we combine a meteorological boundary-layer model (Advanced Regional Prediction System) with a fully distributed energy balance model (Alpine3D). Turbulent fluxes above melting snow are further investigated by using data from eddy-correlation systems. We compare modeled snow ablation to measured ablation rates as obtained from a series of Terrestrial Laser Scanning campaigns covering a complete ablation season. The measured ablation rates indicate that the advection of sensible heat causes locally increased ablation rates at the upwind edges of the snow patches. The effect, however, appears to be active over rather short distances of about 4-6 m. Measurements suggest that mean wind velocities of about 5 m s −1 are required for advective heat transport to increase snow ablation over a long fetch distance of about 20 m. Neglecting this effect, the model is able to capture the mean ablation rates for early ablation periods but strongly overestimates snow ablation once the fraction of snow coverage is below a critical value of approximately 0.6. While radiation dominates snow ablation early in the season, the turbulent flux contribution becomes important late in the season. Simulation results indicate that the air temperatures appear to overestimate the local air temperature above snow patches once the snow coverage is low. Measured turbulent fluxes support these findings by suggesting a stable internal boundary layer close to the snow surface causing a strong decrease of the sensible heat flux towards the snow cover.Correspondence to: R. Mott (mott@slf.ch) Thus, the existence of a stable internal boundary layer above a patchy snow cover exerts a dominant control on the timing and magnitude of snow ablation for patchy snow covers.

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Section: Numerical Modelsmentioning

confidence: 55%

Micrometeorological processes driving snow ablation in an Alpine catchment

Mott¹,

Egli²,

Grünewald³

et al. 2011

The Cryosphere

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“…For instance, Manes et al [11] used HHCF as a measure of fresh snow roughness and obtained χ 1 between 0.58 and 0.62 in a set of five experiments. These values are consistent with our model with very high semi-ellipsoidal grains (h = 3l) or with conic or pyramidal grains with h = 2l or less.…”

Section: Comparison With Experimental Resultsmentioning

confidence: 99%

Roughness exponents and grain shapes

Reis

2011

Phys. Rev. E

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In surfaces with grainy features, the local roughness w shows a crossover at a characteristic length rc, with roughness exponent changing from α1 ≈ 1 to a smaller α2.. The grain shape, the choice of w or height-height correlation function (HHCF) C, and the procedure to calculate root mean-square averages are shown to have remarkable effects on α1. With grains of pyramidal shape, α1 can be as low as 0.71, which is much lower than the previous prediction 0.85 for rounded grains. The same crossover is observed in the HHCF, but with initial exponent χ1 ≈ 0.5 for flat grains, while for some conical grains it may increase to χ1 ≈ 0.7. The universality class of the growth process determines the exponents α2 = χ2 after the crossover, but has no effect on the initial exponents α1 and χ1, supporting the geometric interpretation of their values. For all grain shapes and different definitions of surface roughness or HHCF, we still observe that the crossover length rc is an accurate estimate of the grain size. The exponents obtained in several recent experimental works on different materials are explained by those models, with some surface images qualitatively similar to our model films.

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“…Our research will complete our picture of the Earth's surface roughness without snow [e.g., Mark and Aronson, 1984;Perron et al, 2008;Abedini and Shaghaghian, 2009] and with snow. In particular, we want to characterize snow surface roughness from the scale of the individual snow grain [Manes et al, 2008] to the scale of snow drifts investigated here.…”

Section: Resultsmentioning

confidence: 99%

Persistence in intra‐annual snow depth distribution: 2. Fractal analysis of snow depth development

Schirmer¹,

Lehning²

2011

Water Resources Research

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[1] We present an analysis of high resolution laser scanning data of snow depths of three different slopes in the Wannengrat catchment (introduced in part 1) using omnidirectional and directional variograms for three specific terrain features; cross-loaded slopes, lee slopes, and windward slopes. A break in scaling behavior was observed in all subareas, which can be seen as the roughness scale of bare earth terrain which is modified by the snow cover. In the wind-protected lee slope a different scaling behavior was observed, compared to the two wind-exposed areas. The wind-exposed areas have a smaller ordinal intercept , a smaller short range fractal dimension D, and a larger scale break distance L than the wind-protected lee slope. Snow depth structure inherits characteristics of dominant NW storms, which results, e.g., in a trend toward larger break distances in the course of the accumulation season. This can be interpreted as a result of surface smoothing at increasing scales. Similar scaling characteristics were obtained for two different years at the end of the accumulation season. Since snow depth structure is altered strongly by NW storms, this inter-annual consistency may strongly depend on their frequency in an accumulation period. With the analysis of directional variograms anisotropies of fractal parameters were detected, which were related to dominant wind directions.Citation: Schirmer, M., and M. Lehning (2011), Persistence in intra-annual snow depth distribution: 2. Fractal analysis of snow depth development, Water Resour. Res., 47, W09517,

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Statistical properties of fresh snow roughness

Cited by 31 publications

References 31 publications

Micrometeorological processes driving snow ablation in an Alpine catchment

Micrometeorological processes driving snow ablation in an Alpine catchment

Roughness exponents and grain shapes

Persistence in intra‐annual snow depth distribution: 2. Fractal analysis of snow depth development

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