Changes in the surface roughness of snow from millimetre to metre scales

Fassnacht, S. R.; Williams, Mark W.; Corrao, Mark V.

doi:10.1016/j.ecocom.2009.05.003

Cited by 41 publications

(63 citation statements)

References 15 publications

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“…Roughness increased during snowmelt as compared to immediately after snowfall, as previously observed (Anttila et al, 2014;Fassnacht et al, 2009). The cavities developed during the melting trap a fraction of the reflected light into their walls, particularly at the shortest wavelengths due to multiple reflections between the walls.…”

Section: Model Discrepanciessupporting

confidence: 63%

Measurements and modelling of snow particle size and shortwave infrared albedo over a melting Antarctic ice sheet

et al. 2015

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Abstract. The albedo of a snowpack depends on the singlescattering properties of individual snow crystals, which have a variety of shapes and sizes, and are often bounded in clusters. From the point of view of optical modelling, it is essential to identify the geometric dimensions of the population of snow particles that synthesize the scattering properties of the snowpack surface. This involves challenges related to the complexity of modelling the radiative transfer in such an irregular medium, and to the difficulty of measuring microphysical snow properties. In this paper, we illustrate a method to measure the size distribution of a snow particle parameter, which roughly corresponds to the smallest snow particle dimension, from two-dimensional macro photos of snow particles taken in Antarctica at the surface layer of a melting ice sheet. We demonstrate that this snow particle metric corresponds well to the optically equivalent effective radius utilized in radiative transfer modelling, in particular when snow particles are modelled with the droxtal shape. The surface albedo modelled on the basis of the measured snow particle metric showed an excellent match with the observed albedo when there was fresh or drifted snow at the surface. In the other cases, a good match was present only for wavelengths longer than 1.4 µm. For shorter wavelengths, our modelled albedo generally overestimated the observations, in particular when surface hoar and faceted polycrystals were present at the surface and surface roughness was increased by millimetre-scale cavities generated during melting. Our results indicate that more than just one particle metric distribution is needed to characterize the snow scattering properties at all optical wavelengths, and suggest an impact of millimetre-scale surface roughness on the shortwave infrared albedo.

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Section: Model Discrepanciessupporting

confidence: 63%

Measurements and modelling of snow particle size and shortwave infrared albedo over a melting Antarctic ice sheet

et al. 2015

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“…A well-known example, where perception and fractal dimension are rather inconsistent, is mentioned by Burrough [1981]: a smooth airport runway has a relatively high D, since variations of long distances are low in amplitude. Similarly and more specific to snow, Fassnacht et al [2009] obtained a large D value for a snow surface previously characterized as ''smooth.'' The corresponding roughness was objectively low in magnitude.…”

Section: Estimating Fractal Parametersmentioning

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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“…Terrain smoothing influences albedo (e.g. Manninen et al, 2012), surface heat transfer and energy balance (e.g Fassnacht et al, 2009) and/or snow depth distribution . Whereas albedo is mainly affected by millimetre to centimetre changes of the winter terrain surface, snow distribution processes are modified by a changing winter topography at scales up to several tens of metres.…”

Section: Introductionmentioning

confidence: 99%

Influence of snow depth distribution on surface roughness in alpine terrain: a multi-scale approach

Veitinger¹,

Sovilla²,

Purves

2014

The Cryosphere

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Abstract. In alpine terrain, the snow-covered winter surface deviates from its underlying summer terrain due to the progressive smoothing caused by snow accumulation. Terrain smoothing is believed to be an important factor in avalanche formation and avalanche dynamics, and it affects surface heat transfer, energy balance as well as snow depth distribution. To assess the effect of snow on terrain, we use an adequate roughness definition. We developed a method to quantify terrain smoothing by combining roughness calculations of snow surfaces and their corresponding underlying terrain with snow depth measurements. To this end, elevation models of winter and summer terrain in three selected alpine basins in the Swiss Alps characterized by low, medium and high terrain roughness were derived from highresolution measurements performed by airborne and terrestrial lidar. The preliminary results in the selected basins reveal that, at basin scale, terrain smoothing depends not only on mean snow depth in the basin but also on its variability. The multi-temporal analysis over three winter seasons in one basin suggests that terrain smoothing can be modelled as a function of mean snow depth and its standard deviation using a power law. However, a relationship between terrain smoothing and snow depth was not found at pixel scale. Further, we show that snow surface roughness is to some extent persistent, even in-between winter seasons. Those persistent patterns might be very useful to improve the representation of a winter terrain without modelling of the snow cover distribution. This can for example improve avalanche release area definition and, in the long term, natural hazard management strategies.

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Changes in the surface roughness of snow from millimetre to metre scales

Cited by 41 publications

References 15 publications

Measurements and modelling of snow particle size and shortwave infrared albedo over a melting Antarctic ice sheet

Measurements and modelling of snow particle size and shortwave infrared albedo over a melting Antarctic ice sheet

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

Influence of snow depth distribution on surface roughness in alpine terrain: a multi-scale approach

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