Snow Specific Surface Area Research Articles

First Retrievals of Surface and Atmospheric Properties Using EnMAP Measurements over Antarctica

The paper presents the first retrievals of clean snow properties using spaceborne hyperspectral observations via the Environmental Mapping and Analysis Program (EnMAP). The location close to the Concordia station at the Dome C Plateau (Antarctica) was selected. At this location, the atmospheric effects (except molecular light scattering and absorption) are weak, and the simplified atmospheric correction scheme could be applied. The ice grain size, snow specific surface area, and snow spectral and broadband albedos were retrieved using single-view EnMAP measurements. In addition, we propose a technique to retrieve trace gas concentrations (e.g., water vapor and ozone) from EnMAP observations over the snow surfaces. A close correspondence of satellite and ground-measured parameters was found.

Snow and meteorological conditions at Villum Research Station, Northeast Greenland: on the adequacy of using atmospheric reanalysis for detailed snow simulations

Reliable and detailed measurements of atmospheric and snow conditions in the Arctic are limited. While modern atmospheric reanalyses could potentially replace the former, the latter can be principally simulated by dedicated snow modelling. However, because the uncertainties of reanalyses and modelling are still exceptionally large at high latitudes, a thorough analysis of the performance of atmospheric reanalyses and the snow model simulations are required. Specifically, we aim to answer the following questions for Villum Research Station (VRS), northeast Greenland: (1) What are the predominant snow and meteorological conditions? (2) What are systematic differences between the modern atmospheric reanalysis ERA5 and in situ measurements? (3) Can the snow model Crocus simulate reliably snow depth and stratigraphy? We systematically compare atmospheric in situ measurements and ERA5 reanalysis (November 2015–August 2018) and evaluate simulated and measured snow depth (October 2014–September 2018). Moreover, modelled and measured vertical profiles of snow density and snow specific surface area (SSA) are analysed for two days where a survey had taken place. We found good agreement between in situ and ERA5 atmospheric variables with correlation coefficients >0.84 except for precipitation, wind speed, and wind direction. ERA5’s resolution is too coarse to resolve the topography in the study area adequately, leading presumably to the detected biases. Crocus can simulate satisfactorily the evolution of snow depth, but simulations of SSA and density profiles, whether driven by ERA5 or in situ measurements are biased compared to measurements. Unexpectedly, measured snow depth agrees better with ERA5 driven simulation than with simulation forced with in situ measurements (explained variance: 0.73 versus 0.23). This is due to differences in snowfall, humidity and air temperature between both forcing datasets. In conclusion, ERA5 has great potential to force snow models but the use of Crocus in the Arctic is affected by limitations such as inappropriate parametrisations for Arctic snowpack evolution, but also by lack of process formulations such as vertical water vapour transport. These limitations strongly affect the accuracy of the vertical profiles of physical snow properties.

Impact of the sampling procedure on the specific surface area of snow measurements with the IceCube

Abstract. The specific surface area (SSA) of snow can be directly measured by X-ray computed tomography or indirectly measured using the reflectance of near-infrared light. The IceCube (IC) is a well-established spectroscopic instrument that uses a near-infrared wavelength of 1310 nm. We compared the SSA of six snow types measured with both instruments. We measured significantly higher values with the IC, with a relative percentage difference of between 20 % and 52 % for snow types with an SSA between 5 and 25 m2 kg−1. We found no significant difference for snow with an SSA between 30 and 80 m2 kg−1. The difference is statistically significant between snow types but not uniquely related to the SSA. We suspected that particles artificially created during the sample preparation were the source of the difference. We sampled, measured and counted these particles to conduct numerical simulations with the TwostreAm Radiative TransfEr in Snow (TARTES) radiation transfer solver. The results support the hypothesis that these small, artificial particles can significantly increase the reflectivity at 1310 nm and, therefore, lead to an overestimation of the SSA.

Exploring the role of snow metamorphism on the isotopic composition of the surface snow at EastGRIP

Abstract. Stable water isotopes from polar ice cores are invaluable high-resolution climate proxy records. Recent studies have aimed to improve our understanding of how the climate signal is stored in the stable water isotope record by addressing the influence of post-depositional processes on the isotopic composition of surface snow. In this study, the relationship between surface snow metamorphism and water isotopes during precipitation-free periods is explored using measurements of snow-specific surface area (SSA). Continuous daily SSA measurements from the East Greenland Ice Core Project site (EastGRIP) during the summer seasons of 2017, 2018 and 2019 are used to develop an empirical decay model to describe events of rapid decrease in SSA linked to snow metamorphism. We find that SSA decay during precipitation-free periods at the EastGRIP site is best described by the exponential equation SSA(t)=(SSA0-22)⋅e-αt+22, and has a dependency on wind speed. The relationship between surface snow SSA and snow isotopic composition is primarily explored using empirical orthogonal function analysis. A coherence between SSA and deuterium excess is apparent during 2017 and 2019, suggesting that processes driving change in SSA also influence snow deuterium excess. By contrast, 2018 was characterised by a covariance between SSA and δ18O highlighting the inter-annual variability in surface regimes. Moreover, we observed changes in isotopic composition consistent with fractionation effects associated with sublimation and vapour diffusion during periods of rapid decrease in SSA. Our findings support recent studies which provide evidence of isotopic fractionation during sublimation, and show that snow deuterium excess is modified during snow metamorphism.

A generalized photon-tracking approach to simulate spectral snow albedo and transmittance using X-ray microtomography and geometric optics

Abstract. 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 than a collection of grains. The model uses a blended approach to expand ray-tracing techniques applied to sub-1 cm3 snow samples to snowpacks of arbitrary depths. While this framework has many potential applications, this study's effort is focused on simulating reflectance and transmittance in the visible and near infrared (NIR) through thin snowpacks as this is relevant for surface energy balance and remote sensing applications. We demonstrate that this framework fits well within the context of previous work and capably reproduces many known optical properties of a snow surface, including the dependence of spectral reflectance on the snow specific surface area and incident zenith angle as well as the surface bidirectional reflectance distribution function (BRDF). To evaluate the model, we compare it against reflectance data collected with a spectroradiometer at a field site in east-central Vermont. In this experiment, painted panels were inserted at various depths beneath the snow to emulate thin snow. The model compares remarkably well against the reflectance measured with a spectroradiometer, with an average RMSE of 0.03 in the 400–1600 nm range. Sensitivity simulations using this model indicate that snow transmittance is greatest in the visible wavelengths, limiting light penetration to the top 6 cm of the snowpack for fine-grain snow but increasing to 12 cm for coarse-grain snow. These results suggest that the 5 % transmission depth in snow can vary by over 6 cm according to the snow type.

Snow surface properties derived from PRISMA satellite data over the Nansen Ice Shelf (East Antarctica)

In this paper, we made use of PRISMA imaging spectroscopy data for retrieving surface snow properties in the Nansen Ice Shelf (East Antarctica). PRISMA satellite mission has been launched in 2019 and it features 239 spectral bands covering the 400-2500 nm interval. These data are promising for cryospheric applications, since several snow and ice parameters can be derived from reflectance in the Visible Near InfraRed - Short Wave InfraRed (VNIR-SWIR) wavelength interval. Here we analyze, for the first time, PRISMA data collected in Antarctica. Our scene was acquired on December 2020 over the Nansen Ice Shelf (NIS). Using PRISMA data we estimated various snow parameters (effective grain diameter, snow specific surface area, snow spectral and broadband albedo, bottom of atmosphere snow reflectance, type of impurities in snow and their concentration), and we compared them with data presented in the scientific literature.

Metamorphism of snow on Arctic sea ice during the melt season: impact on spectral albedo and radiative fluxes through snow

Abstract. 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.

Comparison of snowpack structure in gaps and under the canopy in a humid boreal forest

AbstractThe boreal forest covers a significant portion of the Northern Hemisphere and is snow‐covered for over half of the year. Understanding the interactions between the forest canopy and snow is essential in hydrological, meteorological, and climate modelling. However, this is challenging because the density of a forest can range from closed canopies to open gaps. In winter 2018–2019, we assessed differences in snowpack microstructure in small forest gaps and under the canopy of a humid boreal site in eastern Canada. Our experimental approach consisted of quasi‐continuous weekly observations of stratigraphy and measurements of density profiles and temperature in a series of snow pits in both environments. High‐resolution specific surface area (SSA) profiles were measured twice, allowing for an estimation of snow permeability and hydraulic conductivity. The shallower snowpack under the canopy displayed a stronger vertical temperature gradient and less compaction than in forest gaps. This resulted in the dominance of faceted snow crystals with a small SSA. In contrast, we observed that small, rounded grains with a larger SSA than that of faceted crystals prevailed in the gaps. Due to denser snow and higher SSA, snow permeability inside gaps was found to be lower than under the canopy. Implicitly, the estimated hydraulic conductivity was also lower in gaps. Following rain‐on‐snow events, snow under the canopy displayed layers of melt‐freeze polycrystals, while in the gaps, well‐defined ice layers were formed. The combination of low snow permeability and ice layers is likely to affect liquid water transport in the gap snowpack as compared to the canopy. Although observed at relatively small scales in our study, if these differences are confirmed at a catchment scale, they are likely to impact the hydrology of forested areas during snowmelt.

Sensitivity of Antarctic surface climate to a new spectral snow albedo and radiative transfer scheme in RACMO2.3p3

Abstract. This study investigates the sensitivity of modeled surface melt and subsurface heating on the Antarctic ice sheet to a new spectral snow albedo and radiative transfer scheme in the Regional Atmospheric Climate Model (RACMO), version 2.3p3 (Rp3). We tune Rp3 to observations by performing several sensitivity experiments and assess the impact on temperature and melt by incrementally changing one parameter at a time. When fully tuned, Rp3 compares well with in situ and remote sensing observations of surface mass and energy balance, melt, near-surface and (sub)surface temperature, albedo and snow grain specific surface area. Near-surface snow temperature is especially sensitive to the prescribed fresh snow specific surface area and fresh dry snow metamorphism. These processes, together with the refreezing water grain size and subsurface heating, are important for melt around the margins of the Antarctic ice sheet. Moreover, small changes in the albedo and the aforementioned processes can lead to an order of magnitude overestimation of melt, locally leading to runoff and a reduced surface mass balance.

Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals

Abstract. Topography and vegetation play a major role in sub-pixel variability of Arctic snowpack properties but are not considered in current passive microwave (PMW) satellite SWE retrievals. Simulation of sub-pixel variability of snow properties is also problematic when downscaling snow and climate models. In this study, we simplified observed variability of snowpack properties (depth, density, microstructure) in a two-layer model with mean values and distributions of two multi-year tundra dataset so they could be incorporated in SWE retrieval schemes. Spatial variation of snow depth was parameterized by a log-normal distribution with mean (μsd) values and coefficients of variation (CVsd). Snow depth variability (CVsd) was found to increase as a function of the area measured by a remotely piloted aircraft system (RPAS). Distributions of snow specific surface area (SSA) and density were found for the wind slab (WS) and depth hoar (DH) layers. The mean depth hoar fraction (DHF) was found to be higher in Trail Valley Creek (TVC) than in Cambridge Bay (CB), where TVC is at a lower latitude with a subarctic shrub tundra compared to CB, which is a graminoid tundra. DHFs were fitted with a Gaussian process and predicted from snow depth. Simulations of brightness temperatures using the Snow Microwave Radiative Transfer (SMRT) model incorporating snow depth and DHF variation were evaluated with measurements from the Special Sensor Microwave/Imager and Sounder (SSMIS) sensor. Variation in snow depth (CVsd) is proposed as an effective parameter to account for sub-pixel variability in PMW emission, improving simulation by 8 K. SMRT simulations using a CVsd of 0.9 best matched CVsd observations from spatial datasets for areas > 3 km2, which is comparable to the 3.125 km pixel size of the Equal-Area Scalable Earth (EASE)-Grid 2.0 enhanced resolution at 37 GHz.

The Broadband Albedo of Snow

The asymptotic radiative transfer theory is used to derive the analytical approximation for the broadband albedo of pure and polluted snow surfaces. The technique for the determination of the effective snow grain size and also the snow specific surface area from the shortwave broadband albedo measurements is proposed.

Evaluation of stereology for snow microstructure measurement and microwave emission modeling: a case study

ABSTRACT Reliable microstructure measurement of snow is a requirement for microwave radiative transfer model validation. Snow specific surface area (SSA) can be measured using stereological methods, in which snow samples are cast in the field and photographed in the laboratory. Processing stereology photographs manually by counting intersections of test cycloids with air–ice boundaries reduces the problems in binary segmentation. This paper is a case study to evaluate the repeatability of the manually stereology interpretation by two independent research groups. We further assessed how uncertainty in snow SSA influences simulated brightness temperature (TB) driven by the Microwave Emission Model of Layered Snowpacks (MEMLS), and how stereology compares to Near Infrared (NIR) camera and hand lens. Data was obtained from two alpine snow profiles from Steamboat Springs, Colorado. Results showed that stereological SSA values measured by two groups are highly consistent, and the ground radiometer measured TB at 19 and 37 GHz was successfully predicted (RMSE<3.8 K); simulations using NIR SSA and hand-lens geometric grain size (Dg ) measurements have larger errors. This conclusion was not sensitive to uncertainty in the free parameters of TB modeling.

Quantification of the radiative impact of light-absorbing particles during two contrasted snow seasons at Col du Lautaret (2058 m a.s.l., French Alps)

Abstract. The presence of light-absorbing particles (LAPs) in snow leads to a decrease in short-wave albedo affecting the surface energy budget. However, the understanding of the impacts of LAPs is hampered by the lack of dedicated datasets, as well as the scarcity of models able to represent the interactions between LAPs and snow metamorphism. The present study aims to address both these limitations by introducing a survey of LAP concentrations over two snow seasons in the French Alps and an estimation of their impacts based on the Crocus snowpack model that represents the complex interplays between LAP dynamics and snow metamorphism. First, a unique dataset collected at Col du Lautaret (2058 m a.s.l., above sea level, French Alps) for the two snow seasons 2016–2017 and 2017–2018 is presented. This dataset consists of spectral albedo measurements, vertical profiles of snow specific surface area (SSA), density and concentrations of different LAP species. Spectral albedos are processed to estimate SSA and LAP absorption-equivalent concentrations near the surface of the snowpack. These estimates are then compared to chemical measurements of LAP concentrations and SSA measurements. Our dataset highlights, among others, large discrepancies between two measurement techniques of black carbon (BC) concentrations in snow (namely thermal-optical and laser-induced incandescence). Second, we present ensemble snowpack simulations of the multi-physics version of the detailed snowpack model Crocus, forced with in situ meteorological data, as well as dust and BC deposition fluxes from an atmospheric model. The temporal variations of near-surface LAP concentrations and SSA are most of the time correctly simulated. The simulated seasonal radiative forcing of LAPs is 33 % higher for the 2017–2018 snow season than for the 2016–2017 one, highlighting a strong variability between these two seasons. However, the shortening of the snow season caused by LAPs is similar with 10 ± 5 and 11 ± 1 d for the first and the second snow seasons, respectively. This counter-intuitive result is attributed to two small snowfalls at the end of the first season and highlights the importance in accounting for meteorological conditions to correctly predict the impact of LAPs. The strong variability of season shortening caused by LAPs in the multi-physics ensemble for the first season (10 ± 5 d) also points out the sensitivity of model-based estimations of LAP impact on modelling uncertainties of other processes. Finally, the indirect impact of LAPs (i.e. the enhancement of energy absorption due to the acceleration of the metamorphism by LAPs) is negligible for the 2 years considered here, which is contrary to what was found in previous studies for other sites.

Combining atmospheric and snow radiative transfer models to assess the solar radiative effects of black carbon in the Arctic

Abstract. The magnitude of solar radiative effects (cooling or warming) of black carbon (BC) particles embedded in the Arctic atmosphere and surface snow layer was explored on the basis of case studies. For this purpose, combined atmospheric and snow radiative transfer simulations were performed for cloudless and cloudy conditions on the basis of BC mass concentrations measured in pristine early summer and more polluted early spring conditions. The area of interest is the remote sea-ice-covered Arctic Ocean in the vicinity of Spitsbergen, northern Greenland, and northern Alaska typically not affected by local pollution. To account for the radiative interactions between the black-carbon-containing snow surface layer and the atmosphere, an atmospheric and snow radiative transfer model were coupled iteratively. For pristine summer conditions (no atmospheric BC, minimum solar zenith angles of 55∘) and a representative BC particle mass concentration of 5 ng g−1 in the surface snow layer, a positive daily mean solar radiative forcing of +0.2 W m−2 was calculated for the surface radiative budget. A higher load of atmospheric BC representing early springtime conditions results in a slightly negative mean radiative forcing at the surface of about −0.05 W m−2, even when the low BC mass concentration measured in the pristine early summer conditions was embedded in the surface snow layer. The total net surface radiative forcing combining the effects of BC embedded in the atmosphere and in the snow layer strongly depends on the snow optical properties (snow specific surface area and snow density). For the conditions over the Arctic Ocean analyzed in the simulations, it was found that the atmospheric heating rate by water vapor or clouds is 1 to 2 orders of magnitude larger than that by atmospheric BC. Similarly, the daily mean total heating rate (6 K d−1) within a snowpack due to absorption by the ice was more than 1 order of magnitude larger than that of atmospheric BC (0.2 K d−1). Also, it was shown that the cooling by atmospheric BC of the near-surface air and the warming effect by BC embedded in snow are reduced in the presence of clouds.

Impact of Shrubs on Winter Surface Albedo and Snow Specific Surface Area at a Low Arctic Site: In Situ Measurements and Simulations

AbstractErect shrubs in the Arctic reduce surface albedo when branches protrude above the snow and modify snow properties, in particular specific surface area (SSA). Important consequences are changes in the land surface–atmosphere energy exchange and the increase of snow melting in autumn, possibly inducing reduced soil thermal insulation and in turn permafrost cooling. Near Umiujaq (56.5°N, 76.5°W) in the Canadian low Arctic where dwarf birches (Betula glandulosa) are expanding, spectral albedo (400–1080 nm) under diffuse light and vertical profiles of SSA were measured in November and December 2015 at four sites: three with protruding branches and one with only snow. At the beginning of the snow season (8 November), shrub-induced albedo reductions were found to be wavelength dependent and as high as 55% at 500 nm and 18% at 1000 nm, which, integrated over the measurement range (400–1080 nm), corresponds to 70 W m−2 of additional absorbed energy. The impact of shrubs is not just snow darkening. They also affect snow SSA in multiple ways, by accumulating snow with high SSA during cold windy precipitation and favoring SSA decrease by inducing melting during warm spells. However, the impact on the radiation budget of direct darkening from shrubs likely dominates over the indirect change in SSA. Spectral albedo was simulated with a linear mixing equation (LME), which fitted well with observed spectra. The average root-mean-square error was 0.009. We conclude that LMEs are a suitable tool to parameterize mixed surface albedo in snow and climate models.

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.

Evaluation of the SNOWPACK model’s metamorphism and microstructure in Canada: a case study

ABSTRACTThe snow thermodynamic multi-layer model SNOWPACK was developed to address the risk of avalanches by simulating the vertical properties of snow. Risk and stability assessments are based on the simulation of the vertical variability of snow microstructure, as well as on snow cohesion parameters. Previous research has shown systematic error in grain size simulations (equivalent optical grain size) over several areas in northern Canada. To quantify the simulated errors in snow grain size and uncertainties in stability, the snow specific surface area (SSA) was measured with a laser-based instrument. Optical grain size was retrieved to validate the optical equivalent grain radius from SNOWPACK. The two study plots are located in Glacier National Park, BC, and Jasper National Park, AB, Canada. Profiles for density and stratigraphic analysis were obtained as well as grain size profiles, combined with snow micropenetrometer (SMP) measurements. Density analysis showed good agreement with the simulated values (R2 = 0.76). Optical grain size analysis showed systematic overestimation of the modeled values, in agreement with the current literature. The error in SSA evolution for a rounding environment was mostly constant, whereas error for conditions driven by a temperature gradient was linked to the size of the facetted grains.

Can We Monitor Snow Properties on Sea Ice to Investigate Its Role in Tropospheric Ozone Depletion?

In the lower troposphere over the Arctic Ocean, ozone is often destroyed in spring by chemical chain reactions involving the reactive bromine species Br and BrO. The role of surface snow in generating reactive bromine has been suspected, but many details of the processes not understood. Using unique data such as BrO concentrations from instruments on buoys, Burd et al. (2017, https://doi.org/10.1002/2017JD026906) observed that the snowmelt onset date often coincides with the end of the reactive bromine season. They proposed that the decrease in snow-specific surface area and/or the occurrence of liquid water in snow induced by melting dramatically slows the rate of surface reactions generating bromine, indicating that the physical state of the snow is critical for bromine generation. Their suggestion is discussed, and a method to test it using novel instrumentation recently available is proposed.

Snow specific surface area remote sensing retrieval using a microstructure based reflectance model

The snow specific area (SSA) is a quantity to describe the size of snow grains, which controls the albedo of snow surface. The remote sensing retrieval of snow SSA is important for large scale energy and snow physical properties studies. The previous SSA retrieval algorithms are based on snow reflectance forward models with shape assumptions of snow grains, which may cause bias in the quantitative retrieval of SSA. In this paper, a new SSA retrieval algorithm based on a recently developed snow reflectance model is presented. In the reflectance model, the microstructure of snow medium is simulated by computer and the light scattering process is modeled by Monte Carlo ray tracing and radiative transfer theory. Validation shows the model can accurately simulate snow surface reflectance even at large incident or observation zenith angles. Three datasets were used to validate the SSA retrieval algorithm. The validation results show that the SSA can be accurately estimated using MODIS data. The difference of SSA by using reflectance of different wavelengths are studied by MODIS band 5 and band 6 data. The SSA retrieval algorithm is then applied to seasonal snow cover to study the temporal and spatial pattern of snow surface optical grain size.

A simple model of snow albedo decay using observations from the Community Collaborative Rain, Hail, and Snow-Albedo (CoCoRaHS-Albedo) Network

ABSTRACTThe albedo of seasonal snow cover plays an important role in the global climate system due to its influence on Earth's radiation budget and energy balance. Volunteer Community Collaborative Rain, Hail, and Snow-Albedo (CoCoRaHS-Albedo) observers collected 3249 individual daily albedo, snow depth and density measurements using standardized techniques at dozens of sites across New Hampshire, USA over four winter seasons. The data show that albedo increases rapidly with snow depth up to ~0.14 m. Multiple linear regression models using snowpack age, snow depth or density, and air temperature provide reasonable approximations of surface snow albedo during times of albedo decay. However, the linear models also reveal systematic biases that highlight an important non-linearity in snow albedo decay. Modeled albedo values are reasonably accurate within the range of 0.6–0.9, but exhibit a tendency to overestimate lower albedo values and underestimate higher albedo values. We hypothesize that rapid reduction in high albedo fresh snow results from a decrease in snow specific surface area, while during melt-events the presence of liquid water in the snowpack accelerates metamorphism and grain growth. We conclude that the CoCoRaHS-Albedo volunteer observer network provides useful snow albedo, depth and density measurements and serves as an effective model for future measurement campaigns.