Layered Snowpacks Research Articles

Modeling the Observed Microwave Emission from Shallow Multi-Layer Tundra Snow Using DMRT-ML

The observed brightness temperatures (Tb) at 37 GHz from typical moderate density dry snow in mid-latitudes decreases with increasing snow water equivalent (SWE) due to volume scattering of the ground emissions by the overlying snow. At a certain point, however, as SWE increases, the emission from the snowpack offsets the scattering of the sub-nivean emission. In tundra snow, the Tb slope reversal occurs at shallower snow thicknesses. While it has been postulated that the inflection point in the seasonal time series of observed Tb V 37 GHz of tundra snow is controlled by the formation of a thick wind slab layer, the simulation of this effect has yet to be confirmed. Therefore, the Dense Media Radiative Transfer Theory for Multi Layered (DMRT-ML) snowpack is used to predict the passive microwave response from airborne observations over shallow, dense, slab-layered tundra snow. Airborne radiometer observations coordinated with ground-based in situ snow measurements were acquired in the Canadian high Arctic near Eureka, NT, in April 2011. The DMRT-ML was parameterized with the in situ snow measurements using a two-layer snowpack and run in two configurations: a depth hoar and a wind slab dominated pack. With these two configurations, the calibrated DMRT-ML successfully predicted the Tb V 37 GHz response (R correlation of 0.83) when compared with the observed airborne Tb footprints containing snow pits measurements. Using this calibrated model, the DMRT-ML was applied to the whole study region. At the satellite observation scale, observations from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) over the study area reflected seasonal differences between Tb V 37 GHz and Tb V 19 GHz that supports the hypothesis of the development of an early season volume scattering depth hoar layer, followed by the growth of the late season emission-dominated wind slab layer. This research highlights the necessity to consider the two-part emission characteristics of a slab-dominated tundra snowpack at 37 GHz Tb.

Sources, evolution and impacts of EC and OC in snow on sea ice: a measurement study in Barrow, Alaska

Based on the field measurements in Barrow, Alaska within the period of April–May 2015, we investigate the sources and variations of elemental carbon (EC) and organic carbon (OC) in the surface layer of snowpack on sea ice, and estimate their effects on the sea ice albedo. Results show that the snow OC in Barrow are from natural sources (e.g. terrestrial higher plants and micro-organisms) mainly, as well as biomass burning (e.g. forest fires and straw combustion) as an important part. Both EC and OC can accumulate at the snow surface with snow melt. The variations in EC and OC and liquid water content in the snow layer are well consistent during the snow-melting period. A higher rate of snow melt implied a more efficient enrichment of EC and OC. In the last phase of snow melt, the concentration increased to a maximum of 16.2 ng/g for EC and 128 ng/g for OC, which is ∼10 times larger than those before snow melt onset. Except for the dominant influence of melt amplification mechanism, the variation in concentrations of EC and OC could be disturbed by the air temperature fluctuation and snowfall. Our study indicates that the light-absorbing impurities contributed 1.6%–5.1% to the reduction in sea ice albedo with melt during the measurement period. The significant period of light-absorbing impurities influencing on sea ice albedo begins with the rapid melting of overlying snow and ends before the melt ponds formed widely, which lasted for about 10 days in Barrow, 2015.

Assessing snow instability in skier-triggered snow slab avalanches by combining failure initiation and crack propagation

Dry-snow slab avalanches start with a local failure in a weak snowpack layer buried below cohesive snow slab layers. If the size of the failed zone exceeds a critical size, rapid crack propagation occurs possibly followed by slab release if the slope is steep enough. The probability of skier-triggering a slab avalanche is generally characterized by classical stability indices that do not account for crack propagation. In this study, we propose a new model to evaluate the conditions for the onset of crack propagation in skier-triggered slab avalanches. For a given weak layer, the critical crack length characterizing crack propagation propensity was compared to the size of the area where the skier-induced stress exceeds the shear strength of the weak layer. The ratio between both length scales yields a stability criterion combining the processes of failure initiation and crack propagation. The critical crack length was calculated from a recently developed model based on numerical simulations. The skier-induced stress was computed from analytical solutions and finite element simulations to account for slab layering. A detailed sensitivity analysis was performed for simplified snow profiles to characterize the influence of snowpack properties and slab layering on crack propagation propensity. Finally, we applied our approach to manually observed snow profiles and compared our new criterion to Rutschblock scores.

Experimental measurement of acoustic properties in snow

This paper presents the results of field experiments done for the measurement of attenuation constant and speed of sound in the snow medium. The field experiments were conducted at two locations corresponding to relatively dry and wet snow. The main objective of our method is to overcome the potential limitations of the other methods. There are two major concerns: one is sound signal leakage and the other is the measurement need to be done within a same layer of snowpack. The ref lections from the boundaries will affect the measurements. In our experiments the transducers are kept far from the snowpack boundaries, so that there will be no likelihood of strong ref lected signals from the boundaries. These issues have not been addressed by the other researchers. This work adds to the measurement results of the attenuation constant and speed of sound in snow that are available in the research literature. It is found that sound signal attenuation greatly depends on the frequency of sound signal and wetness of snow.

Application of a Markov Chain Monte Carlo algorithm for snow water equivalent retrieval from passive microwave measurements

Recent applications of passive microwave remote sensing techniques to estimate snow water equivalent (SWE) increasingly rely on the comprehension of microwave emission theories, instead of traditional empirical fitting approaches. In this study, an advanced SWE retrieval algorithm based on the Markov Chain Monte Carlo method was developed. This method samples the posterior multiple-layer snow properties according to the likelihood of the brightness temperature (TB) simulation with the actual TB observations. The Microwave Emission Model of Layered Snowpacks with improved Born approximation (MEMLS-IBA) was used as the observation model. Using a globally applicable method to produce prior estimates of snow properties, the retrieval approach is called the Bayesian Algorithm for SWE Estimation with Passive Microwave measurements (BASE-PM), and was applied on 48 snowpits at Sodankylä, Finland; Churchill, Canada and Colorado, US. The result shows that the root mean squared (RMS) error of the retrieved SWE is 42.7mm excluding two outliers, and is 30.8mm if the outliers as well as six deep snowpits from Colorado are excluded. This accuracy approximately meets the 30-mm requirement of Integrated Global Observing Strategy for shallow snow. The poor performance for the outlier and deep snowpits is explained. Additional experiments using more accurate priors show that SWE retrieval accuracy can be improved with local snowcover knowledge, e.g. if historical snowpit measurements or snow process model simulations are available.

Improving the Radiance Assimilation Performance in Estimating Snow Water Storage across Snow and Land-Cover Types in North America

Abstract Continental-scale snow radiance assimilation (RA) experiments are conducted in order to improve snow estimates across snow and land-cover types in North America. In the experiments, the ensemble adjustment Kalman filter is applied and the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) brightness temperature TB observations are assimilated into an RA system composed of the Community Land Model, version 4 (CLM4); radiative transfer models (RTMs); and the Data Assimilation Research Testbed (DART). The performance of two snowpack RTMs, the Dense Media Radiative Transfer–Multi-Layers model (DMRT-ML), and the Microwave Emission Model of Layered Snowpacks (MEMLS) in improving snow depth estimates through RA is compared. Continental-scale snow estimates are enhanced through RA by using AMSR-E TB at the 18.7- and 23.8-GHz channels [3% (DMRT-ML) and 2% (MEMLS) improvements compared to the cases using the 18.7- and 36.5-GHz channels] and by considering the vegetation single-scattering albedo ω [2.5% (DMRT-ML) and 4.8% (MEMLS) improvements compared to the cases neglecting ω]. The contribution of TB of the vegetation canopy to TB at the top of the atmosphere is better represented by considering ω in the RA system, and improvements in the resulting snow depth are evident for the forest land-cover type (about 5%–11%) and the taiga and alpine snow classes (about 5%–11% and 4%–8%, respectively), especially in the MEMLS case. Compared to the open-loop run (0.171-m snow depth RMSE), about 7% (DMRT-ML) and 10% (MEMLS) overall improvements of the RA performance are achieved.

Global warming response of snowpack at mountain range in northern Japan estimated using multiple dynamically downscaled data

We estimate the response of snowpack to global warming along with the uncertainty of the snowpack change by using a combination of multiple general-circulation models (GCMs), a single regional atmospheric model, and a one-dimensional multi-layered snowpack model. The target site is Mt. Annupuri in Kutchan, Hokkaido, Japan. The forcing of the snowpack model is taken from dynamically downscaled data from GCMs for the present climate and GCMs in a decade when the global-mean temperature has increased by 2K from present conditions. The results show that global warming would decrease the monthly-mean snow depth throughout the winter season. Other salient features are the decrease of snow depth by 60cm with maximum uncertainty of 20cm at the beginning of the snow ablation period, the occurrence of the snow-depth peak a month earlier, and the dominance of melt forms in an earlier season. The ratio of melt forms for all snowpack layers increases with little uncertainty before the snow ablation period. The ratio of hoar does not change much, even though the air temperature increases. The uncertainty in snowpack evaluation is also discussed.

Comparison of commonly-used microwave radiative transfer models for snow remote sensing

This paper reviews four commonly-used microwave radiative transfer models that take different electromagnetic approaches to simulate snow brightness temperature (TB): the Dense Media Radiative Transfer - Multi-Layer model (DMRT-ML), the Dense Media Radiative Transfer - Quasi-Crystalline Approximation Mie scattering of Sticky spheres (DMRT-QMS), the Helsinki University of Technology n-Layers model (HUT-nlayers) and the Microwave Emission Model of Layered Snowpacks (MEMLS). Using the same extensively measured physical snowpack properties, we compared the simulated TB at 11, 19 and 37GHz from these four models. The analysis focuses on the impact of using different types of measured snow microstructure metrics in the simulations. In addition to density, snow microstructure is defined for each snow layer by grain optical diameter (Do) and stickiness for DMRT-ML and DMRT-QMS, mean grain geometrical maximum extent (Dmax) for HUT n-layers and the exponential correlation length for MEMLS. These metrics were derived from either in-situ measurements of snow specific surface area (SSA) or macrophotos of grain sizes (Dmax), assuming non-sticky spheres for the DMRT models. Simulated TB sensitivity analysis using the same inputs shows relatively consistent TB behavior as a function of Do and density variations for the vertical polarization (maximum deviation of 18K and 27K, respectively), while some divergences appear in simulated variations for the polarization ratio (PR). Comparisons with ground-based radiometric measurements show that the simulations based on snow SSA measurements have to be scaled with a model-specific factor of Do in order to minimize the root mean square error (RMSE) between measured and simulated TB. Results using in-situ grain size measurements (SSA or Dmax, depending on the model) give a mean TB RMSE (19 and 37GHz) of the order of 16–26K, which is similar for all models when the snow microstructure metrics are scaled. However, the MEMLS model converges to better results when driven by the correlation length estimated from in-situ SSA measurements rather than Dmax measurements. On a practical level, this paper shows that the SSA parameter, a snow property that is easy to retrieve in-situ, appears to be the most relevant parameter for characterizing snow microstructure, despite the need for a scaling factor.

Simulating ice layer formation under the presence of preferential flow in layered snowpacks

Abstract. For physics-based snow cover models, simulating the formation of dense ice layers inside the snowpack has been a long-time challenge. Their formation is considered to be tightly coupled to the presence of preferential flow, which is assumed to happen through flow fingering. Recent laboratory experiments and modelling techniques of liquid water flow in snow have advanced the understanding of conditions under which preferential flow paths or flow fingers form. We propose a modelling approach in the one-dimensional, multilayer snow cover model SNOWPACK for preferential flow that is based on a dual domain approach. The pore space is divided into a part that represents matrix flow and a part that represents preferential flow. Richards' equation is then solved for both domains and only water in matrix flow is subjected to phase changes. We found that preferential flow paths arriving at a layer transition in the snowpack may lead to ponding conditions, which we used to trigger a water flow from the preferential flow domain to the matrix domain. Subsequent refreezing then can form dense layers in the snowpack that regularly exceed 700 kg m−3. A comparison of simulated density profiles with biweekly snow profiles made at the Weissfluhjoch measurement site at 2536 m altitude in the Eastern Swiss Alps for 16 snow seasons showed that several ice layers that were observed in the field could be reproduced. However, many profiles remain challenging to simulate. The prediction of the early snowpack runoff also improved under the consideration of preferential flow. Our study suggests that a dual domain approach is able to describe the net effect of preferential flow on ice layer formation and liquid water flow in snow in one-dimensional, detailed, physics-based snowpack models, without the need for a full multidimensional model.

Impact of spatial averaging on radar reflectivity at internal snowpack layer boundaries

ABSTRACTMicrowave radar amplitude within a snowpack can be strongly influenced by spatial variability of internal layer boundaries. We quantify the impact of spatial averaging of snow stratigraphy and physical snowpack properties on surface scattering from near-nadir frequency-modulated continuous-wave radar at 12–18 GHz. Relative permittivity, density, grain size and stratigraphic boundaries were measured in-situ at high resolution along the length of a 9 m snow trench. An optimal range of horizontal averaging (4–6 m) was identified to attribute variations in surface scattering at layer boundaries to dielectric contrasts estimated from centimetre-scale measurements of snowpack stratigraphy and bulk layer properties. Single vertical profiles of snowpack properties seldom captured the complex local variability influencing near-nadir radar surface scattering. We discuss implications of scaling in-situ measurements for snow radiative transfer modelling and evaluation of airborne microwave remote sensing of snow.

Bromide and chloride distribution across the snow‐sea ice‐ocean interface: A comparative study between an Arctic coastal marine site and an experimental sea ice mesocosm

AbstractDuring springtime in the Arctic, bromine explosion events occur when high concentrations of reactive bromine species are observed in the boundary layer with the concurrence of ozone depletion events and mercury depletion events. While a variety of substrates including snow, sea ice, frost flowers, and aerosols have been proposed to be the substrate and/or source of bromine activation in the Arctic, recent studies have highlighted the role of snow. Here we report concentration profiles of halides (Br− and Cl−), Na+, and mercury across the snow‐sea ice‐seawater interface at a coastal marine site in the Canadian Arctic Archipelago in March and June 2014, as well as in an experimental sea ice mesocosm in Winnipeg in January and February 2014. The occurrence of bromine activation at the Arctic site in March was indicated by the high mercury concentrations in snowpack. At both the Arctic and mesocosm sites, the molar ratios of Br−/Na+ were nearly constant throughout the sea ice depth, but highly variable in the upper layer of the overlying snowpack, revealing that bromine activation takes place in the sunlit snow instead of sea ice. This is supported by calculations showing that the loss of Br– from the upper layer of the snowpack is large enough to produce the observed concentrations of reactive bromine in the atmospheric boundary layer. However, the upper layer of the Arctic snowpack tends to be generally enriched in Br– due to the net addition of Br–‐containing gases and nonsea‐salt aerosols.

Estimating the effective elastic modulus and specific fracture energy of snowpack layers from field experiments

ABSTRACTMeasurements of the mechanical properties of snow are essential for improving our understanding and the prediction of snow failure and hence avalanche release. We performed fracture mechanical experiments in which a crack was initiated by a saw in a weak snow layer underlying cohesive snow slab layers. Using particle tracking velocimetry (PTV), the displacement field of the slab was determined and used to derive the mechanical energy of the system as a function of crack length. By fitting the estimates of mechanical energy to an analytical expression, we determined the slab effective elastic modulus and weak layer specific fracture energy for 80 different snowpack combinations, including persistent and nonpersistent weak snow layers. The effective elastic modulus of the slab ranged from 0.08 to 34 MPa and increased with mean slab density following a power-law relationship. The weak layer specific fracture energy ranged from 0.08 to 2.7 J m−2 and increased with overburden. While the values obtained for the effective elastic modulus of the slab agree with previously published low-frequency laboratory measurements over the entire density range, the values of the weak layer specific fracture energy are in some cases unrealistically high as they exceeded those of ice. We attribute this discrepancy to the fact that our linear elastic approach does not account for energy dissipation due to non-linear parts of the deformation in the slab and/or weak layer, which would undoubtedly decrease the amount of strain energy available for crack propagation.

Estimation of snow depth from passive microwave brightness temperature data in forest regions of northeast China

Abstract Snow depth is an important factor in water resources management in Northeast China. Forest covers 40% of Northeast China, and the presence of forests influences the accuracy of snow depth retrievals from passive microwave remote sensing data. An optimal iteration method was used to retrieve the forest transmissivities at 18 and 36 GHz based on the snow and forest microwave radiative transfer models and the snow properties measured in field experiments. The transmissivities at 18 and 36 GHz are 0.895 and 0.656 in the horizontal polarization, and 0.821 and 0.615 in the vertical polarization, respectively. Furthermore, the forest transmissivity and snow properties were input into the Microwave Emission Model of Layered Snowpacks (MEMLS) to establish a dynamic look-up table (LUT). Snow depths were retrieved from satellite passive microwave remote sensing data based on the LUT method, and these retrievals were verified by snow depth observations at 103 meteorological stations. The results showed that the bias between the retrieved and measured snow depths is very small, with root mean square errors (RMSEs) of approximately 6 cm in forest regions and 4 cm in non-forest regions. When compared with the existing snow products, the snow depth retrieved in this work presented the highest level of accuracy. The regional snow depth product in China is superior to the GlobSnow and NASA AMSR-E standard SWE products in non-forest regions, whereas the GlobSnow estimate is superior to the regional snow depth product in China and NASA AMSR-E standard SWE product estimates in forest regions. Therefore, we conclude that 1) the influence of forest on snow depth retrieval is important, and the appropriate forest parameters should be considered in the estimation of snow depth from passive microwave brightness temperature data; and 2) the snow depth retrieval algorithm based on the dynamic LUT method proved to be efficient in Northeast China.

Retrieving soil surface temperature under snowpack using special sensor microwave/imager brightness temperature in forested areas of Heilongjiang, China: an improved method

Soil surface temperature ( T s ) is an important indicator of global temperature change and a key input parameter for retrieving land surface variables using remote sensing techniques. Due to the masking in the thermal infrared band and the scattering in the microwave band of snow, the temperature of soil surfaces covered by snow is difficult to infer from remote sensing data. We attempted to estimate T s under snow cover using brightness temperature data from the special sensor microwave/imager. T s under snow cover was underestimated due to the strong scattering effect of snow on upward soil microwave emissions at 37 GHz. The underestimated portion of T s is related to snow properties, such as depth, grain size, and moisture. Based on the microwave emission model of layered snowpacks, the simulated results revealed a linear relationship between the underestimated T s and the brightness temperature difference (TBD) at 19 and 37 GHz. When TBDs at 19 and 37 GHz were introduced to the T s estimation method, accuracy improved, i.e., the root mean square error and bias of the estimated T s decreased greatly, especially for dry snow. This improvement allows T s estimation of snow-covered surfaces from 37 GHz microwave brightness temperature.

Arctic Snow Microstructure Experiment for the development of snow emission modelling

Abstract. The Arctic Snow Microstructure Experiment (ASMEx) took place in Sodankylä, Finland in the winters of 2013–2014 and 2014–2015. Radiometric, macro-, and microstructure measurements were made under different experimental conditions of homogenous snow slabs, extracted from the natural seasonal taiga snowpack. Traditional and modern measurement techniques were used for snow macro- and microstructure observations. Radiometric measurements of the microwave emission of snow on reflector and absorber bases were made at frequencies 18.7, 21.0, 36.5, 89.0, and 150.0 GHz, for both horizontal and vertical polarizations. Two measurement configurations were used for radiometric measurements: a reflecting surface and an absorbing base beneath the snow slabs. Simulations of brightness temperatures using two microwave emission models, the Helsinki University of Technology (HUT) snow emission model and Microwave Emission Model of Layered Snowpacks (MEMLS), were compared to observed brightness temperatures. RMSE and bias were calculated; with the RMSE and bias values being smallest upon an absorbing base at vertical polarization. Simulations overestimated the brightness temperatures on absorbing base cases at horizontal polarization. With the other experimental conditions, the biases were small, with the exception of the HUT model 36.5 GHz simulation, which produced an underestimation for the reflector base cases. This experiment provides a solid framework for future research on the extinction of microwave radiation inside snow.

Differences Between the HUT Snow Emission Model and MEMLS and Their Effects on Brightness Temperature Simulation

Microwave emission models are a critical component of snow water equivalent retrieval algorithms applied to passive microwave measurements. Several such emission models exist, but their differences need to be systematically compared. This paper compares the basic theories of two models: the multiple-layer Helsinki University of Technology (HUT) model and the microwave emission model of layered snowpacks (MEMLS). By comparing the mathematical formulation side by side, three major differences were identified: 1) by assuming that the scattered intensity is mostly (96%) in the forward direction, the HUT model simplifies the radiative transfer equation in $4\pi$ space into two one-flux equations, whereas MEMLS uses a two-flux theory; 2) the HUT scattering coefficient is much larger than the one of MEMLS; and 3) MEMLS considers the trapped radiation inside snow due to internal reflection by a six-flux model, which is not included in HUT. Simulation experiments indicate that the large scattering coefficient of the HUT model compensates for its large forward scattering ratio to some extent, but the effects of one-flux simplification and the trapped radiation still result in different $T_{B}$ simulations between the HUT model and MEMLS. The models were compared with observations of natural snow cover at Sodankyla, Finland; Churchill, Canada; and Colorado, USA. No optimization of the snow grain size was performed. It shows that the HUT model tends to underestimate $T_{B}$ for deep snow. MEMLS with the physically based improved Born approximation performed best among the models, with a bias of −1.4 K and a root-mean-square error of 11.0 K.

Microwave scattering coefficient of snow in MEMLS and DMRT-ML revisited: the relevance of sticky hard spheres and tomography-based estimates of stickiness

Abstract. The description of snow microstructure in microwave models is often simplified to facilitate electromagnetic calculations. Within dense media radiative transfer (DMRT), the microstructure is commonly described by sticky hard spheres (SHS). An objective mapping of real snow onto SHS is however missing which prevents measured input parameters from being used for DMRT. In contrast, the microwave emission model of layered snowpacks (MEMLS) employs a conceptually different approach, based on the two-point correlation function which is accessible by tomography. Here we show the equivalence of both electromagnetic approaches by reformulating their microstructural models in a common framework. Using analytical results for the two-point correlation function of hard spheres, we show that the scattering coefficient in both models only differs by a factor which is close to unity, weakly dependent on ice volume fraction and independent of other microstructural details. Additionally, our analysis provides an objective retrieval method for the SHS parameters (diameter and stickiness) from tomography images. For a comprehensive data set we demonstrate the variability of stickiness and compare the SHS diameter to the optical equivalent diameter. Our results confirm the necessity of a large grain-size scaling when relating both diameters in the non-sticky case, as previously suggested by several authors.

Calibrating laser scanner data from snow surfaces: Correction of intensity effects

Terrestrial laser scanning data have become more and more commonly used in cryospheric studies as the commercial instruments are getting cheaper and more user-friendly. We have studied the usability of laser scanning intensity data in remote sensing of snow-covered surfaces by focusing on two topics: the effect of incidence angle on the intensity data and the depth which the backscattered laser beam represents. The measurements were made with a phase-based laser scanner using 650–690nm wavelength. For some of the snow backscatter vs. depth studies measurements were also made with a pulse-based scanner at 905nm. The incidence angle effect was studied by rotating a snow surface sample relative to the scanner and measuring the difference in the intensity values. The experiment was repeated for different snow types. The snow pack layer that the backscattered laser signal represents was studied by inserting black metal plates horizontally into the snow pack and measuring the changes in the intensity values with plates at different depths. The results suggest that the snow type has no effect on the incidence angle effect and that for dry snow the backscattering of the laser beam takes place from the very surface, but for wet snow, the majority of the signal is backscattered from 0.5 to 1cm depth. An empirical correction function for the incidence angle effect is also presented.

Modeling of crack propagation in weak snowpack layers using the discrete element method

Abstract. Dry-snow slab avalanches are generally caused by a sequence of fracture processes including (1) failure initiation in a weak snow layer underlying a cohesive slab, (2) crack propagation within the weak layer and (3) tensile fracture through the slab which leads to its detachment. During the past decades, theoretical and experimental work has gradually led to a better understanding of the fracture process in snow involving the collapse of the structure in the weak layer during fracture. This now allows us to better model failure initiation and the onset of crack propagation, i.e., to estimate the critical length required for crack propagation. On the other hand, our understanding of dynamic crack propagation and fracture arrest propensity is still very limited. To shed more light on this issue, we performed numerical propagation saw test (PST) experiments applying the discrete element (DE) method and compared the numerical results with field measurements based on particle tracking. The goal is to investigate the influence of weak layer failure and the mechanical properties of the slab on crack propagation and fracture arrest propensity. Crack propagation speeds and distances before fracture arrest were derived from the DE simulations for different snowpack configurations and mechanical properties. Then, in order to compare the numerical and experimental results, the slab mechanical properties (Young's modulus and strength) which are not measured in the field were derived from density. The simulations nicely reproduced the process of crack propagation observed in field PSTs. Finally, the mechanical processes at play were analyzed in depth which led to suggestions for minimum column length in field PSTs.

Error Characterization of Coupled Land Surface-Radiative Transfer Models for Snow Microwave Radiance Assimilation

Snow microwave radiance assimilation (RA) or brightness temperature data assimilation (DA) has shown promise for improving snow water equivalent (SWE) estimation. A successful RA study requires, however, an analysis of the error characteristics of coupled land surface-radiative transfer models (LSM/RTMs). This paper focuses on the Community Land Model version 4 (CLM4) as the land-surface model and on the microwave emission model for layered snowpacks (MEMLS) and the dense media radiative transfer multilayer (DMRT-ML) model as RTMs. Using the National Aeronautics and Space Administration Cold Land Processes Field Experiment (CLPX) data sets and through synthetic experiments, the errors of the coupled CLM4/DMRT-ML and CLM4/MEMLS are characterized by: 1) evaluating the CLM4 snowpack state simulations; 2) assessing the performance of RTMs in simulating the brightness temperature $(T_{B} )$ ; and 3) analyzing the correlations between the SWE error $(\varepsilon\_\text{SWE})$ and the $T_{B}$ error $(\varepsilon\_T_{B} ) $ from the RA perspective. The results using the CLPX data sets show that, given a large error of the snow grain radius $(\varepsilon\_r_{e} )$ under dry snowpack conditions (along with a small error of the snow temperature $(\varepsilon\_T_\mathrm{snow})$ ), the correlations between $\varepsilon\_\text{SWE}$ and $\varepsilon\_T_{B} $ are mainly determined by the relationship between $\varepsilon\_r_{e}$ and the snow depth error $(\varepsilon\_d_{\mathrm{snow}})$ or the snow density error $(\varepsilon\_\rho_{\mathrm{snow}} ) $ . The synthetic experiments were carried out for the CLPX region (shallow snowpack conditions and the Rocky Mountains (deep snowpack conditions using the atmospheric ensemble reanalysis produced by the coupled DA Research Testbed/Community Atmospheric Model (CAM4. The synthetic experiments support the results from the CLPX data sets and show that the errors of soil (the water content and the temperature, snow wetness, and snow temperature mostly result in positive correlations between $\varepsilon\_\text{SWE}$ and $\varepsilon\_T_{B} $ . CLM4/DMRT-ML and CLM4/MEMLS tend to produce varying RA performance, with more positive and negative correlations between $\varepsilon\_\text{SWE}$ and $\varepsilon\_T_{B} $ , respectively. These results suggest the necessity of using multiple snowpack RTMs in RA to improve the SWE estimation at the continental scale. The results in this paper also show that the magnitude of $\varepsilon\_r_{e}$ and its relationship to $\varepsilon\_\text{SWE} $ are important for the RA performance. Most of the SWE estimations in RA are improved when $\varepsilon\_\text{SWE}$ and $\varepsilon\_r_{e} $ show a high positive correlation (greater than 0.5).