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Physical and simulated optical properties of the top 8 cm of snow measured at the UVD site on February 12 2021. SSA and ρs are computed directly from the µCT sample. Note that the depths correspond to the RTM model depths for the virgin snow calculation.
Source publication
A majority of snow radiative transfer models (RTM) treat snow as a collection of idealized grains rather than a semi-organized ice-air matrix. Here we present a generalized multi-layer photon-tracking RTM that simulates light transmissivity and reflectivity through snow based on x-ray microtomography, treating snow as a coherent structure rather th...
Contexts in source publication
Context 1
... optical properties used in the 1D RTM were determined from four approximately 800 mm 3 µCT samples, with each 340 sample representing a 2 cm thick layer within the top 8 cm of the snowpack. The RTM is then configured with 4 layers according to these optical properties (given in Table 2). The top three layers are each 2 cm thick, and the bottom layer is 28 cm thick, such that the entire snow depth amounted to 34 cm. ...Context 2
... choose this configuration working under the hypothesis that the snow microstructure below 8 cm had little impact on the measured surface spectral albedo. To simulate the panels, the snowpack depth is modified to be 4.75 and 2.5 cm deep with a 100 % absorptive lower boundary while maintaining the 345 layering corresponding to Table 2. ...Similar publications
A majority of snow radiative transfer models (RTMs) treat snow as a collection of idealized grains rather than an organized ice–air matrix. Here we present a generalized multi-layer photon-tracking RTM that simulates light reflectance and transmittance of snow based on X-ray microtomography images, treating snow as a coherent 3D structure rather th...
We develop a new way of retrieving the cloud index from a large variety of satellite instruments sensitive to reflected solar radiation, embedded on geostationary and non-geostationary platforms. The cloud index is a widely used proxy for the effective cloud transmissivity, also called the “clear-sky index”. This study is in the framework of the de...
Citations
... [14][15][16] , and references therein). Quite successful attempts have been made to describe photon transfer through snow as a two-phase ice-air mixture by means of the photontracking Monte-Carlo method using X-ray tomography images of snow [17][18][19][20][21] . ...
Porous materials are considered as a random binary mixture of two homogeneous phases with different refractive indices; the void may be an option for one of the phases. The stereological approach has been used to formulate a system of radiative transfer equations in which the refraction/reflection by the interface between the two phases is the source of scattering. For arbitrary statistics, it is the system of integral equations with the phase chord length distributions as their kernels. For Markov statistics, it becomes the system of standard-like radiative transfer equations. For a sparse mixture, i.e. when the mean chord of one phase is much larger than that of the other, the system reduces to a single equation which is the standard radiative transfer one. Dense packing affects apparent optical properties of the medium by reducing its albedo, increasing the transmittance and shifting a position of the maximum of the light angular distribution. The model has been applied to the optics of snow. The result is a higher accuracy in retrieving the specific surface area from snow reflectance measurements when compared with the conventional method using Mie calculations. The reasons of the albedo reduction with density and of the so far successful use of the Mie solution in the optics of snow are discussed. The theory presented applies to plenty of densely packed granular media: foams, various man-made powders (food, pharmaceutic, or metal), or natural covers of planetary surfaces, such as snow, soil, sand, or regolith.
... Improving this agreement could be done by performing chemical measurements of impurities in each snow layer and by improving our understanding of the relationship between grain shape and the physical variables B and g. This latter suggestion could be realized by either performing field studies similar to Libois et al. (2014) or ray-tracing simulations on microtomography images of real snow samples (Haussener et al., 2012;Letcher et al., 2021). Figure 11 shows that the PAR flux increase at the snow-ice interface around 9 June coincides fairly well with the chlorophyll a increase. ...
The energy budget of Arctic sea ice is strongly affected by the snow cover. Intensive sampling of snow properties was conducted near Qikiqtarjuaq in Baffin Bay on typical landfast sea ice during two melt seasons in 2015 and 2016. The sampling included stratigraphy, vertical profiles of snow specific surface area (SSA), density and irradiance, and spectral albedo (300–1100 nm). Both years featured four main phases: (I) dry snow cover, (II) surface melting, (III) ripe snowpack, and (IV) melt pond formation. Each phase was characterized by distinctive physical and optical properties. A high SSA value of 49.3 m2 kg−1 was measured during phase I on surface wind slabs together with a corresponding broadband albedo (300–3000 nm) of 0.87. Phase II was marked by alternating episodes of surface melting, which dramatically decreased the SSA below 3 m2 kg−1, and episodes of snowfall re-establishing pre-melt conditions. Albedo was highly time-variable, with minimum broadband values around 0.70. In phase III, continued melting led to a fully ripe snowpack composed of clustered rounded grains. Albedo began to decrease in the visible as snow thickness decreased but remained steady at longer wavelengths. Moreover, significant spatial variability appeared for the first time following snow depth heterogeneity. Spectral albedo was simulated by radiative transfer using measured SSA and density vertical profiles and estimated impurity contents based on limited measurements. Simulations were most of the time within 1 % of measurements in the visible and within 2 % in the infrared. Simulations allowed the calculations of albedo and of the spectral flux at the snow–ice interface. These showed that photosynthetically active radiation fluxes at the bottom of the snowpack durably exceeded 5 W m−2
(∼9.2 µmol m−2 s−1) only when the snowpack thickness started to decrease at the end of phase II.
... Improving this agreement could be 585 done by performing chemical measurements of impurities in each snow layer and by improving our understanding of the relationship between grain shape and the physical variables B and g. This latter suggestion could be realized either by performing field studies similar to (Libois et al., 2014) or ray-tracing simulations on microtomography images of real snow samples (Haussener et al., 2012;Letcher et al., 2021). ...
The energy budget of Arctic sea ice is strongly affected by the snow cover. Intensive sampling of snow properties was conducted near Qikiqtarjuak in Baffin Bay on typical landfast sea ice during two melt seasons in 2015 and 2016. The sampling included stratigraphy, vertical profiles of snow specific surface area (SSA), density and irradiance, and spectral albedo (300–1100 nm). Both years featured four main phases: I) dry snow cover, II) surface melting, III) ripe snowpack and IV) melt pond formation. Each phase was characterized by distinctive physical and optical properties. A high SSA value of 49.3 m2 kg-1 was measured during phase I on surface wind slabs together with a corresponding broadband albedo of 0.87. Phase II was marked by alternating episodes of surface melting which dramatically decreased the SSA below 3 m2 kg-1 and episodes of snowfall reestablishing pre-melt conditions. Albedo was highly time-variable with minimum values at 1000 nm around 0.45. In Phase III, continued melting led to a fully ripe snowpack composed of clustered rounded grains. Albedo began to decrease in the visible as snow thickness decreased but remained steady at longer wavelengths. Moreover, significant spatial variability appeared for the first time following snow depth heterogeneity. Spectral albedo was simulated by radiative transfer using measured SSA and density vertical profile, and impurity contents based on measurements. Simulations were most of the time within 1 % of measurements in the visible and within 2 % in the infrared. Simulations allowed the calculation of albedo and of the spectral flux at the top of the sea ice. These showed that photosynthetically active radiation fluxes at the bottom of the snowpack durably exceeded 5 W m-2 (about 9.2 µmol m-2 s-1) only when the snowpack thickness started to decrease at the end of Phase II.
