Snow Grain Research Articles

Effects of Snow Grain Shape and Mixing State of Snow Impurity on Retrieval of Snow Physical Parameters From Ground‐Based Optical Instrument

AbstractWe proposed new snow grain model and snow impurity mixture models for the purpose of accurate retrievals of snow grain size and concentration of light‐absorbing particles (LAP) in snow from the optical remote sensing data. Two kinds of ice crystal models, irregularly shaped Voronoi columns, and Voronoi aggregates were employed. LAP can be captured by the snow through two processes: dry and wet deposition. Two different snow impurity mixture models were proposed. One is an external mixture model. We employed a coated sphere model in which soot were hydrophilic particles to represent a hygroscopic property of aerosol and assumed the hydrophilic soot particles to be externally mixed with snow particles. The other is an internal mixture model. We employed a dynamic effective medium approximation method in which soot particles were randomly located within snow particle with any size distribution and number concentration. Validation of these models is conducted using a ground‐based spectral radiometer system with in situ measurement data. For snow grain size retrievals, a Voronoi mixture model seamlessly representing a geometrical shape and an optical properties of various snow types can provide accurate retrievals. For the retrieval of LAP concentration in snow, employing different mixing state models depending on season and measurement site gives accurate results. We also discussed the uncertainty of retrieved snow parameters on the surface slope involved in the retrieval accuracy. These models are expected to be useful for advanced airborne/satellite remote sensing and climate studies via radiative transfer modeling in the atmosphere‐snow system.

Wind tunnel test of wind-induced snowdrift on stepped flat roofs during snowfall

Snow distribution on a building surface is uneven due to the complicated drift movements of snow grains affected by the wind. Therefore, an accurate estimation of the snow distribution on a building’s roof is essential in protecting its structure. The existing wind tunnel tests of snow distribution on roofs do not consider the influences of falling snow, resulting in deviations between the test results and the actual snow distribution. In this study, a wind tunnel test method considering the snow falling process was proposed to simulate the irregular snow distribution on stepped flat roofs during snowfall. Based on the test data, the variation rule of snow distribution on the stepped flat roof with different test methods and conditions is summarized. (1) The results of the conventional snow erosion test, which neglected the influences of falling snow, deviated from the existing measurement results. (2) The test results of the proposed method with varying wind speeds and grain sizes met the double-linear or triple-linear rule, which conformed to the measurement results. (3) The initial snow significantly influenced the snow distribution on the front of the lower roof on windward side but only slightly affected the snow distribution on the other locations. The research conclusions could provide significant contributions to the actual engineering design.

The Use of a Monte Carlo Markov Chain Method for Snow-Depth Retrievals: A Case Study Based on Airborne Microwave Observations and Emission Modeling Experiments of Tundra Snow

Snow-depth retrieval from passive microwave observations without a priori information is a highly undetermined problem. Achieving accurate snow-depth retrievals requires a priori information on the snowpack properties, such as grain size, density, physical temperature, and stratigraphy. On a practical level, however, retrieval algorithms must consider prior information, while minimizing the dependence on it, as accurate ancillary data are not globally available. In this study, we build on the previously published Bayesian Algorithm for Snow Water Equivalent Estimation (BASE) to retrieve snow depth using an airborne passive microwave data set over the tundra snow in the Eureka region. The method computes the optimal estimates of snow depth, density, grain size, and other variables, given the brightness temperature observations and prior information, using Markov chain Monte Carlo (MCMC). The airborne data set includes passive microwave brightness temperature (T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> ) at 18.7 and 36.5 GHz. The in situ measurements of the snow depth provide validation data for 464 sensor footprints. The microwave radiative transfer (RT) model used is the Dense Media RT-Multilayered (DMRT-ML) model. We use a two-layer wind slab and depth hoar assumption based on the local snow cover knowledge from the previous research on the study area. To improve our understanding of the results using the airborne T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> s, the inversion was also applied using the synthetic observations, where T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> s were generated from the RT model. For the case with synthetic observations, the snow-depth RMSE was 0.07 cm. When the airborne T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> s are used, the snow-depth RMSE was 21.8 cm. This discrepancy is due to the large spatial variability in the MagnaProbe snow-depth measurements and the fact that not all physical processes affecting the airborne T <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> s are represented in the RT model. Our work verifies the feasibility and applicability of the proposed methodology regionally for the airborne retrievals and reinforces the tractable applicability of a physics-based RT model in the SWE retrievals.

Physically based model of the contribution of red snow algal cells to temporal changes in albedo in northwest Greenland

Abstract. Surface albedo of snow and ice is substantially reduced by inorganic impurities, such as aeolian mineral dust (MD) and black carbon (BC), and also by organic impurities, such as microbes that live in the snow. In this paper, we present the temporal changes of surface albedo, snow grain size, MD, BC and snow algal cell concentration observed on a snowpack in northwest Greenland during the ablation season of 2014 and our attempt to reproduce the changes in albedo with a physically based snow albedo model. We also attempt to reproduce the effects of inorganic impurities and the red snow algae (Sanguina nivaloides) on albedo. Concentrations of MD and red snow algae in the surface snow were found to increase in early August, while snow grain size and BC were found to not significantly change throughout the ablation season. Surface albedo was found to have decreased by 0.08 from late July to early August. The albedo simulated by the model agreed with the albedo observed during the study period. However, red snow algae exerted little effect on surface albedo in early August. This is probably owing to the abundance of smaller cells (4.9×104 cells L−1) when compared with the cell abundance of red snow reported by previous studies in the Arctic region (∼108 cells L−1). The simulation of snow albedo until the end of the melting season, with a snow algae model, revealed that the reduction in albedo attributed to red snow algae could equal 0.004, out of a total reduction of 0.102 arising from the three impurities on a snowpack in northwest Greenland. Finally, we conducted scenario simulations using the snow albedo model, coupled with the snow algae model, in order to simulate the possible effects of red snow blooming on snow albedo under warm conditions in northwest Greenland. The result suggests that albedo reduction by red snow algal growth under warm conditions (surface snow temperature of +1.5 ∘C) reached 0.04, equivalent to a radiative forcing of 7.5 W m−2 during the ablation season of 2014. This coupled albedo model has the potential to dynamically simulate snow albedo, including the effect of organic and inorganic impurities, leading to proper estimates of the surface albedo of snow cover in Greenland.

Evaluating the Preconditions of Two Remote Sensing SWE Retrieval Algorithms over the US

A large amount of fresh water resources are stored in the snowpack, which is the primary source of water for streamflow in many places at middle-to-high latitude areas. Therefore, snow water equivalent (SWE) is a key parameter in the water cycle. Active and passive microwave remote sensing methods have been used to retrieve SWE due to relatively poor resolution of current in situ interpolated maps with good accuracy. However, estimation of SWE has proved challenging, despite several decades of efforts to develop retrieval approaches. Active sensors provide higher-resolution observations. Two recent promising retrieval algorithms using active data are dual frequency dual polarization backscattered power and differential interferometry. These retrieval algorithms have some restrictions on snow characteristics, the environment, and instrument properties. The restrictions limit the snow that is suitable for the specific retrieval algorithm. In order to better understand how much of the snowpack satisfies the precondition of these retrieval approaches, we use a 4 km gridded snowpack product over the contiguous US for years 1997 and 2015. We use a simple scattering model to simulate the scattering characteristics of snow. The snow property maps, simulated scattering characteristics of snow, and environmental conditions are used to filter the suitable snow for each retrieval algorithm. We show that snow wetness and vegetation coverage are the two main limiting conditions for these retrieval algorithms. We show that 39% and 44% of the grid-points with snow satisfy the preconditions of dual polarization dual frequency retrieval algorithms at 13.5 GHz (one of the recommended frequencies for this algorithm in the literature) in 1997 and 2015, respectively. The most important limiting factors for dual polarization dual frequency retrieval method are dryness of snow, penetration depth, and vegetation-free constraints. The backscattered power in dual polarization dual frequency method is more sensitive to snow density and grain radius rather than to snow depth. We also show that 55% and 53% of the grid-points with snow satisfy the precondition of differential interferometry retrieval algorithms at 1 GHz (one of the recommended frequencies for this algorithm in the literature) in 1997 and 2015, respectively. The most important precondition-limiting factors for differential interferometry are dryness of snow and vegetation-free constraints. The differential interferometry phase retrieval algorithm is equally sensitive to snow height and snow density variations and is independent of snow grain radius.

Albedo of Black Carbon‐Contaminated Snow Across Northwestern China and the Validation With Model Simulation

AbstractLight‐absorbing particles in snow can significantly reduce the snow albedo. Quantification of the influence of black carbon (BC), one of the most important light‐absorbing particles, on snow albedo is essential for understanding the budgets of solar radiation on snow‐covered areas. We measured BC concentration in snow at 28 sites and snow albedo at 18 sites in a vast region across northwestern China in January 2018. The BC concentration was in a wide range of 40–1,850 ng g−1. The presence of the BC reduced the snow albedo by 0.01–0.20 at the visible wavelength band (400–750 nm). The reduction differed from sites to sites with large values close to industrial areas that are characterized by high pollutants emission. Albedos simulated with a Snow, Ice, and Aerosol Radiation model based on the measured BC agreed well with the measured albedos, with the deviation within ±0.03 and the average underestimation of &lt;0.002. It worth noting that the retrieved optical effective snow grain radius was the key factor in snow albedo calculation. The BC‐induced radiative forcing was estimated to be 0.2–6.9 W m−2, indicating strong acceleration of snowmelt due to BC in northwestern China. All obtained data are extremely valuable for climate model validation associated with the albedo of BC‐contaminated snow cover.

Microorganisms Associated With Dust on Alpine Snow

We investigated snow microstructure and microbial composition from snow samples collected from western Colorado, a region that experiences frequent dust deposition events. We developed a methodology to quantify the amount, size and location of dust particles within the snow matrix through analysis of X-ray micro-computed tomography data. Concurrently, we determined the microbial composition in sampled dust layers through DNA sequencing. We found that dust particles were generally embedded in the snow grains, with a small fraction of the dust particles’ surface area exposed to air. Microbial community composition varied more by alpine site rather than residence time of the dust within the snowpack, with the recently deposited dust layer harboring both the highest diversity and highest concentration of dust particles. The presence of microbiota in the dust-impacted snowpack has important implications for snowpack stability, melting and biogeochemistry.

Experimental Study of Cone Penetration in Snow Using X-Ray Tomography

The cone penetration test is widely used to determine the mechanical properties of snow and to delineate snow stratigraphy. Precise knowledge of the snow stratigraphy is essential for many applications such as avalanche forecasting or estimating the snowpack energy budget. With the development of sophisticated, high-resolution digital penetrometers such as the SnowMicroPenetrometer, the cone penetration test remains one of the only objective methods to measure snow stratigraphy. An accurate interpretation of the measured hardness profiles requires to understand the interaction between the cone tip and the snow material. In this study, we measured the displacement induced by the penetration of a conic tip with a radius of 2.5~mm in eight different snow samples using X-ray tomography. The experiments were conducted at a temperature of -10°C. To recover the full three-dimensional displacements between the tomographic images measured before and after the test, we specifically designed a tracking algorithm which exploits the unique shape of each snow grain. The tracking algorithm enables to recover most of the granular displacements and accurately captures the volumetric strain directly derived from density changes. The measured displacements are shown to be oriented downwards below the tip apex, upwards close to the snow surface, and nearly only radially in between. We observed and quantified the development of a compaction around and below the tip. Surprisingly, we also observed dilation of the snow material close to the snow and tip surfaces in very high-density samples. The radial extent of the compaction zone ranged between 1.6 and 2.3 times the tip radius. These results were compared to existing interpretative models. Although limited to relatively small samples and short penetration depths, these results provide new insights on snow deformation during a cone penetration test, and the validity of these models.

The contribution of surfaces to the Raman spectrum of snow

We investigated if the Raman spectrum of snow is affected by the highly differentiated snow morphology. The latter is subjected to continuous changes driven by the variation of ambient parameters, mainly temperature and humidity as snow progresses from freshly deposited, to compact, to icy. We measured the Raman spectrum of snow grains of different size and morphology on the temperature interval between −5.5 °C and −0.5 °C approaching the melting point. We found systematic differences with respect to bulk ice Ih in the OH-stretching band of snows with different histories. We attribute the observed spectral differences to the change of the molecular arrangement of water molecules between bulk and surface sites. We investigate this characteristic via molecular dynamics DFT simulations of bulk ice and of three thin slabs cut along specific surfaces. We analysed the simulated Raman spectra and compared their distinguishing features to those of experimental spectra of snow. The Raman features of the OH-stretching band allow to distinguish among layers inside a snowpack, each layer being characterized by a different specific surface area. This is relevant to estimate the mechanical stability of the snowpack itself. The influence of UV-A irradiation on snow is discussed.

Validation of the SNTHERM Model Applied for Snow Depth, Grain Size, and Brightness Temperature Simulation at Meteorological Stations in China

Validation of the snow process model is an important preliminary work for the snow parameter estimation. The snow grain growth is a continuous and accumulative process, which cannot be evaluated without comparing with the observations in snow season scale. In order to understand the snow properties in the Asian Water Tower region (including Xinjiang province and the Tibetan Plateau) and enhance the use of modeling tools, an extended snow experiment at the foot of the Altay Mountain was designed to validate and improve the coupled physical Snow Thermal Model (SNTHERM) and the Microwave Emission Model of Layered Snowpacks (MEMLS). By matching simultaneously the observed snow depth, geometric grain size, and observed brightness temperature (TB), with an RMSE of 1.91 cm, 0.47 mm, and 4.43 K (at 36.5 GHz, vertical polarization), respectively, we finalized the important model coefficients, which are the grain growth coefficient and the grain size to exponential correlation length conversion coefficients. When extended to 102 meteorological stations in the 2008–2009 winter, the SNTHERM predicted the daily snow depth with an accuracy of 2–4 cm RMSE, and the coupled SNTHERM-MEMLS model predicted the satellite-observed TB with an accuracy of 13.34 K RMSE at 36.5 GHz, vertical polarization, with the fractional snow cover considered.

Presenting Snow Grain Size and Shape Distributions in Northern Canada Using a New Photographic Device Allowing 2D and 3D Representation of Snow Grains

Geophysical properties of snow are known to be sensitive to climate variability and are of primary importance for hydrological and climatological process simulations. Numerous studies using passive microwaves have attempted to quantify snow from space, but the methods suffer from poor spatial resolution retrievals, combined with a great sensitivity to snow grain morphology. Those issues motivated work using active microwaves that are now core to space mission concept proposals currently under development. However, a clear limitation remains with regards to snow microstructure contribution to backscattering, especially in large depth hoar layers typical of polar snowpacks. This leads to difficulties retrieving Snow Water Equivalent (SWE) from space or developing radiative transfer models owing to a lack of field observations of snow microstructure. This paper presents an innovative technique to measure various snow grain metrics in the field where micro-photographs of snow grains are taken under angular directional LED lighting. The projected shadows are digitized so that a 3D reconstruction of the snow grains is possible and distribution functions can be proposed for various snow grain metrics and grain types. This device, dLED, has been used in several field campaigns and a very large dataset was collected and is presented in this paper. Distribution histograms from >160,000 digitized grains were produced for each metric for all grains considered as a whole dataset (unclassified), and also for each grain type: 1) Defragmented/broken (DF), 2) Depth Hoar (DH), 3) Facets (F), 4) Rounds (R) and 5) Precipitation Particles (PP). We selected distribution functions for each metric per grain time by analysing L-moment diagrams that summarize the shape of a probability distribution. Our results show that the logarithmic Kappa (LKAP) distribution is well suited to explain the snow grain metric distribution for each grain type. Location, scale and shape parameter values for each distribution are presented and a comparison with values derived from our shortwave infrared laser device, the InfraRed Integrating Sphere (IRIS), is provided. A discussion is presented on the pros and cons of the dLED and the use of the distributions presented in this paper for microwave radiative transfer modeling work.

Version 1 of a sea ice module for the physics-based, detailed, multi-layer SNOWPACK model

Abstract. Sea ice is an important component of the global climate system. The presence of a snowpack covering sea ice can strongly modify the thermodynamic behavior of the sea ice, due to the low thermal conductivity and high albedo of snow. The snowpack can be stratified and change properties (density, water content, grain size and shape) throughout the seasons. Melting snow provides freshwater which can form melt ponds or cause flushing of salt out of the underlying sea ice, while flooding of the snow layer by saline ocean water can strongly impact both the ice mass balance and the freezing point of the snow. To capture the complex dynamics from the snowpack, we introduce modifications to the physics-based, multi-layer SNOWPACK model to simulate the snow–sea-ice system. Adaptations to the model thermodynamics and a description of water and salt transport through the snow–sea-ice system by coupling the transport equation to the Richards equation were added. These modifications allow the snow microstructure descriptions developed in the SNOWPACK model to be applied to sea ice conditions as well. Here, we drive the model with data from snow and ice mass-balance buoys installed in the Weddell Sea in Antarctica. The model is able to simulate the temporal evolution of snow density, grain size and shape, and snow wetness. The model simulations show abundant depth hoar layers and melt layers, as well as superimposed ice formation due to flooding and percolation. Gravity drainage of dense brine is underestimated as convective processes are so far neglected. Furthermore, with increasing model complexity, detailed forcing data for the simulations are required, which are difficult to acquire due to limited observations in polar regions.

基于两种辐射传输模型的雪粒径与反照率反演

基于两种辐射传输模型的雪粒径与反照率反演

Aerosols in the E3SM Version 1: New Developments and Their Impacts on Radiative Forcing

AbstractThe new Energy Exascale Earth System Model Version 1 (E3SMv1) developed for the U.S. Department of Energy has significant new treatments of aerosols and light‐absorbing snow impurities as well as their interactions with clouds and radiation. This study describes seven sets of new aerosol‐related treatments (involving emissions, new particle formation, aerosol transport, wet scavenging and resuspension, and snow radiative transfer) and examines how they affect global aerosols and radiative forcing in E3SMv1. Altogether, they give a reduced total aerosol radiative forcing (−1.6 W/m2) and sensitivity in cloud liquid water to aerosols, but an increased sensitivity in cloud droplet size to aerosols. A new approach for H2SO4 production and loss largely reduces a low bias in small particles concentrations and leads to substantial increases in cloud condensation nuclei concentrations and cloud radiative cooling. Emitting secondary organic aerosol precursor gases from elevated sources increases the column burden of secondary organic aerosol, contributing substantially to global clear‐sky aerosol radiative cooling (−0.15 out of −0.5 W/m2). A new treatment of aerosol resuspension from evaporating precipitation, developed to remedy two shortcomings of the original treatment, produces a modest reduction in aerosols and cloud droplets; its impact depends strongly on the model physics and is much stronger in E3SM Version 0. New treatments of the mixing state and optical properties of snow impurities and snow grains introduce a positive present‐day shortwave radiative forcing (0.26 W/m2), but changes in aerosol transport and wet removal processes also affect the concentration and radiative forcing of light‐absorbing impurities in snow/ice.

Wet‐Snow Metamorphism Drives the Transition From Preferential to Matrix Flow in Snow

AbstractIn order to explain the poorly understood transition between preferential and matrix flow in snow, we compared observations from a cold‐laboratory experiment with predictions from a multidimensional water transport snow model. We found a good agreement between the modeled and observed evolution of grain size distributions if two or three dimensions are considered by the model, which validates existing theories of snow grain growth. Furthermore, the model reproduced the spatial migration of preferential flow paths with time and a progressive homogenization of snow wetness and structure only if grain growth was simulated. Spatially varying grain growth thus drives the transition from a preferential flow to a matrix flow regime. This transition is faster when grain size and density are lower, or infiltration rates are higher. This explains why preferential flow is more persistent in firn than in snow.

Snow Depth Retrieval in Farmland Based on a Statistical Lookup Table from Passive Microwave Data in Northeast China

At present, passive microwave remote sensing is the most efficient method to estimate snow depth (SD) at global and regional scales. Farmland covers 46% of Northeast China and accurate SD retrieval throughout the whole snow season has great significance for the agriculture management field. Based on the results of the statistical analysis of snow properties in Northeast China from December 2017 to January 2018, conducted by the China snow investigation project, snow characteristics such as snow grain size (SGS), snow density, snow thickness, and temperature of the layered snowpack were measured and analyzed in detail. These characteristics were input to the microwave emission model of layered snowpacks (MEMLS) to simulate the brightness temperature (TB) time series of snow-covered farmland in the periods of snow accumulation, stabilization, and ablation. Considering the larger SGS of the thick depth hoar layer that resulted in a rapid decrease of simulated TBs, effective SGS was proposed to minimize the simulation errors and ensure that the MEMLS can be correctly applied to satellite data simulation. Statistical lookup tables (LUTs) for MWRI and AMSR2 data were generated to represent the relationship between SD and the brightness temperature difference (TBD) at 18 and 36 GHz. The SD retrieval results based on the LUT were compared with the actual SD and the SD retrieved by Chang’s algorithm, Foster’s algorithm, the standard MWRI algorithm, and the standard AMSR2 algorithm. The results demonstrated that the proposed algorithm based on the statistical LUT achieved better accuracy than the other algorithms due to its incorporation of the variation in snow characteristics with the age of snow cover. The average root mean squared error of the SD for the whole snow season was approximately 3.97 and 4.22 cm for MWRI and AMSR2, respectively. The research results are beneficial for monitoring SD in the farmland of Northeast China.

Evaluation of Brightness Temperature Sensitivity to Snowpack Physical Properties Using Coupled Snow Physics and Microwave Radiative Transfer Models

There are multiple existing microwave radiative transfer models (RTMs) to simulate the brightness temperature (Tb) of snowpacks. It is still challenging to have consistent Tb responses from RTMs due to individual physical formulations of the snowpack scattering process. This article examines three of the widely-used multi-layer RTMs: 1) the microwave emission model of layered snowpacks (MEMLS); 2) the dense media radiative transfer based on the quasi-crystalline approximation (QCA) of Mie scattering of densely packed sticky spheres (DMRT-QMS); and 3) the Helsinki University of Technology (HUT) model. Interestingly, these models yield slightly different Tb responses when driven by the same physical snowpack properties. Tb variations, dependent on the choice of RTMs, are then evaluated to improve the understanding of model differences in microwave emission from a snowpack. We first perform a sensitivity study of the Tb predictions from the three RTMs as a function of snow grain sizes, densities, and depths. While Tb from all three RTMs decreases with increasing snow grain sizes, it is found that a scaling factor is required to have the same amount of Tb attenuation for small grain sizes within the Rayleigh scattering regime. For larger grain sizes, however, a scaling coefficient is not enough to match the model outputs due to the different scattering assumptions of the RTMs. For a single snow layer with increasing snow depths and densities, all three RTMs exhibit Tb attenuations arising from the increase in path lengths and optical depths. Further evaluations are conducted by feeding the three RTMs with the output of a snow physics model driven by in situ weather forcing in a coupled simulation. Outputs of this coupled model include snowpack physical properties and Tbs. By using snow stratigraphy observations, another set of Tb simulation is also conducted with RTMs driven by in situ snowpit observations. The snow physics outputs from the coupled case are compared against in situ snow stratigraphy observations. And both Tb simulations are compared against ground-based microwave observations from the European Space Agency (ESA) Nordic Snow Radar Experiment (NoSREx) 2009–2012. For three consecutive years, the in situ driven Tbs have 21.0-K root-mean-squared error (RMSE) while the coupled simulations have 24.7-K RMSE. However, after isolating the dry snow period and excluding diurnal melting snow conditions, 12.2- and 6.3-K RMSEs are achieved from in situ and coupled cases, respectively, in the 2011 water year.

Enhanced Snow Absorption and Albedo Reduction by Dust‐Snow Internal Mixing: Modeling and Parameterization

AbstractWe extend a stochastic aerosol‐snow albedo model to explicitly simulate dust internally/externally mixed with snow grains of different shapes and for the first time quantify the combined effects of dust‐snow internal mixing and snow nonsphericity on snow optical properties and albedo. Dust‐snow internal/external mixing significantly enhances snow single‐scattering coalbedo and absorption at wavelengths of &lt;1.0 μm, with stronger enhancements for internal mixing (relative to external mixing) and higher dust concentrations but very weak dependence on snow size and shape variabilities. Compared with pure snow, dust‐snow internal mixing reduces snow albedo substantially at wavelengths of &lt;1.0 μm, with stronger reductions for higher dust concentrations, larger snow sizes, and spherical (relative to nonspherical) snow shapes. Compared to internal mixing, dust‐snow external mixing generally shows similar spectral patterns of albedo reductions and effects of snow size and shape. However, relative to external mixing, dust‐snow internal mixing enhances the magnitude of albedo reductions by 10%–30% (10%–230%) at the visible (near‐infrared) band. This relative enhancement is stronger as snow grains become larger or nonspherical, with comparable influences from snow size and shape. Moreover, for dust‐snow external and internal mixing, nonspherical snow grains have up to ~45% weaker albedo reductions than spherical grains, depending on snow size, dust concentration, and wavelength. The interactive effect of dust‐snow mixing state and snow shape highlights the importance of accounting for these two factors concurrently in snow modeling. For application to land/climate models, we develop parameterizations for dust effects on snow optical properties and albedo with high accuracy.

Impact of the near-field effects on radiative transfer simulations of the bidirectional reflectance factor and albedo of a densely packed snow layer

Phenomenological radiative transfer models commonly assume that particles in media reside in each other’s far zone and, thus, are defined by their single-scattering properties. This so called far-field assumption becomes questionable for dense particulate snow layers since the location of the snow grains inside is close and in each other’s near zone. Electromagnetic interactions among the particles can occur, which potentially change their single-scattering properties. In this paper, the near-field effects in radiative transfer simulations of the snow surface bidirectional reflectance factor (BRF) and the albedo of a snow layer in the wavelength range of 300 - 2500 nm are investigated using the phenomenological radiative transfer model SCIATRAN. The snow layer is composed of densely packed snow grains of droxtal shape with a maximum dimension of 60 µm. The snow grain shape assumption is justified by the good agreement of the simulated top of atmosphere BRF with satellite measurements over a pure snow surface in Greenland. To evaluate the error of the far-field assumption, the single-scattering properties of the dense particulate snow layer has been modified according to the dense-medium light-scattering theory. By applying the far-field approximation, the BRF is overestimated by less than 0.039 % at forward-scattering angles and underestimated by less than 0.006 % at backscattering angles, respectively. The albedo of the dense particulate snow layer is overestimated by less than 0.012 %. In the considered case, the near-field effects on the snow surface BRF and the albedo of radiative transfer simulations appear negligibly small. Consequently, the simulation of snow surface BRF and albedo in the wavelength range of 300 - 2500 nm by phenomenological radiative transfer models under the far-field assumption is well justified.

Retrieval of Snow Properties from the Sentinel-3 Ocean and Land Colour Instrument

The Sentinel Application Platform (SNAP) architecture facilitates Earth Observation data processing. In this work, we present results from a new Snow Processor for SNAP. We also describe physical principles behind the developed snow property retrieval technique based on the analysis of Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3A/B measurements over clean and polluted snow fields. Using OLCI spectral reflectance measurements in the range 400–1020 nm, we derived important snow properties such as spectral and broadband albedo, snow specific surface area, snow extent and grain size on a spatial grid of 300 m. The algorithm also incorporated cloud screening and atmospheric correction procedures over snow surfaces. We present validation results using ground measurements from Antarctica, the Greenland ice sheet and the French Alps. We find the spectral albedo retrieved with accuracy of better than 3% on average, making our retrievals sufficient for a variety of applications. Broadband albedo is retrieved with the average accuracy of about 5% over snow. Therefore, the uncertainties of satellite retrievals are close to experimental errors of ground measurements. The retrieved surface grain size shows good agreement with ground observations. Snow specific surface area observations are also consistent with our OLCI retrievals. We present snow albedo and grain size mapping over the inland ice sheet of Greenland for areas including dry snow, melted/melting snow and impurity rich bare ice. The algorithm can be applied to OLCI Sentinel-3 measurements providing an opportunity for creation of long-term snow property records essential for climate monitoring and data assimilation studies—especially in the Arctic region, where we face rapid environmental changes including reduction of snow/ice extent and, therefore, planetary albedo.