Layered Snowpacks Research Articles

Exploring the potential of forest snow modeling at the tree and snowpack layer scale

Abstract. Boreal and sub-alpine forests host seasonal snow for multiple months per year; however, snow regimes in these environments are rapidly changing due to rising temperatures and forest disturbances. Accurate prediction of forest snow dynamics, relevant for ecohydrology, biogeochemistry, cryosphere, and climate sciences, requires process-based models. While snow schemes that track the microstructure of individual snow layers have been proposed for avalanche research, so far, tree-scale processes resolving canopy representations only exist in a few snow-hydrological models. A framework that enables layer- and microstructure-resolving forest snow simulations at the meter scale is lacking to date. To fill this research gap, this study introduces the forest snow modeling framework FSMCRO, which combines two detailed, state-of-the art model components: the canopy representation from the Flexible Snow Model (FSM2) and the snowpack representation of the Crocus ensemble model system (ESCROC). We apply FSMCRO to discontinuous forests at boreal and sub-alpine sites to showcase how tree-scale forest snow processes affect layer-scale snowpack properties. Simulations at contrasting locations reveal marked differences in stratigraphy throughout the winter. These arise due to different prevailing processes at under-canopy versus gap locations and due to variability in snow metamorphism dictated by a spatially variable snowpack energy balance. Ensemble simulations allow us to assess the robustness and uncertainties of simulated stratigraphy. Spatially explicit simulations unravel the dependencies of snowpack properties on canopy structure at a previously unfeasible level of detail. Our findings thus demonstrate how hyper-resolution forest snow simulations can complement observational approaches to improve our understanding of forest snow dynamics, highlighting the potential of such models as research tools in interdisciplinary studies.

Numerical investigation of crack propagation regimes in snow fracture experiments

A snow slab avalanche releases after failure initiation and crack propagation in a highly porous weak snow layer buried below a cohesive slab. While our knowledge of crack propagation during avalanche formation has greatly improved over the last decades, it still remains unclear how snow mechanical properties affect the dynamics of crack propagation. This is partly due to a lack of non-invasive measurement methods to investigate the micro-mechanical aspects of the process. Using a DEM model, we therefore analyzed the influence of snow cover properties on the dynamics of crack propagation in weak snowpack layers. By focusing on the steady-state crack speed, our results showed two distinct fracture process regimes that depend on slope angle, leading to very different crack propagation speeds. For long experiments on level terrain, weak layer fracture is mainly driven by compressive stresses. Steady-state crack speed mainly depends on slab and weak layer elastic moduli as well as weak layer strength. We suggest a semi-empirical model to predict crack speed, which can be up to 0.6 times the slab shear wave speed. For long experiments on steep slopes, a supershear regime appeared, where the crack propagation speed reached approximately 1.6 times the slab shear wave speed. A detailed micro-mechanical analysis of stresses revealed a fracture principally driven by shear. Overall, our findings provide new insight into the micro-mechanics of dynamic crack propagation in snow, and how these are linked to snow cover properties.Graphical

Snow water equivalent retrieved from X- and dual Ku-band scatterometer measurements at Sodankylä using the Markov Chain Monte Carlo method

Abstract. Radar at high frequency is a promising technique for fine-resolution snow water equivalent (SWE) mapping. In this paper, we extend the Bayesian-based Algorithm for SWE Estimation (BASE) from passive to active microwave (AM) application and test it using ground-based backscattering measurements at three frequencies (X and dual Ku bands; 10.2, 13.3, and 16.7 GHz), with VV polarization obtained at a 50° incidence angle from the Nordic Snow Radar Experiment (NoSREx) in Sodankylä, Finland. We assumed only an uninformative prior for snow microstructure, in contrast with an accurate prior required in previous studies. Starting from a biased monthly SWE prior from land surface model simulation, two-layer snow state variables and single-layer soil variables were iterated until their posterior distribution could stably reproduce the observed microwave signals. The observation model is the Microwave Emission Model of Layered Snowpacks 3 and Active (MEMLS3&a) based on the improved Born approximation. Results show that BASE-AM achieved an RMSE of ∼ 10 cm for snow depth and less than 30 mm for SWE, compared with the RMSE of ∼ 20 cm snow depth and ∼ 50 mm SWE from priors. Retrieval errors are significantly larger when BASE-AM is run using a single snow layer. The results support the potential of X- and Ku-band radar for SWE retrieval and show that the role of a precise snow microstructure prior in SWE retrieval may be substituted by an SWE prior from exterior sources.

Toward a real validation of passive microwave snowmelt detection algorithms over the Antarctic Ice sheet

Antarctic Ice Sheet (AIS) surface snowmelt can profoundly impact climatological and hydrological processes, as well as energy and mass balances. Passive microwave remote sensing has proven highly effective in detecting snowmelt, and the accuracy of the algorithms is typically assessed using air temperature (Tair). Nonetheless, the occurrence of snowmelt cannot be solely determined by Tair being above the melting point, it can only provide evaluation of the algorithms rather than a real validation. This study is the first to conduct a reliable validation of six commonly used algorithms by using the melt rate data from a surface energy balance model, including the cross-polarized gradient ratio (XPGR) algorithm, the diurnal amplitude variation (DAV) algorithm, the Microwave Emission Model of Layered Snowpacks based (MEMLS-based) algorithm, the constant threshold algorithm (CTA), the Ashcraft and Long Algorithm (ALA), and the M + 30 K (M30 hereafter) algorithm. Results suggest that the M30 algorithm and MEMLS-based algorithm have overall better performances, with high overall accuracies (with a value of 95.81 % and 95.56 %) and high kappa coefficient (with a value of 0.69 and 0.68). Though Tair is not reliable for evaluating the algorithms, a threshold of Tair = − 0.8 ℃ can serve as an alternative criterion in the absence of melt rate data. Additionally, two algorithms with the highest accuracy were selected to investigate interannual snowmelt trends in the Antarctic and the sub-regions. The results show a significant increase in melt extent and density in the Wilkes and Adelie sub-region during the period 1979–2020.

A novel global snow depth retrieval method considering snow metamorphism and forest influence

Accurate snow depth (SD) information at global scales is highly important for validation and calibration of hydrological model, snowmelt runoff prediction, and global water supplies estimation. Passive microwave remote sensing has been proven to be an efficient method to detect SD at regional or global scales. However, numerous heterogeneous snow property parameters and forest parameters affect the microwave radiation characteristics, which is a great challenge for SD retrieval. In this study, a novel SD retrieval algorithm that comprehensively considered the multiple sensitive factors was developed at global scales by coupling the Microwave Emission Model of Layered Snowpacks (MEMLS) model and machine learning (ML) technique. The exponential correlation length (Pec), snow density (ρ), and forest cover fraction (fforest) are three sensitive parameters for retrieving SD from passive microwave observations based on the extended Fourier amplitude sensitivity test (EFAST) algorithm. The key parameter Pec, which characterizes the snow microstructure information inside snowpacks, was optimized by minimizing the brightness temperature between satellite observations and microwave radiative transfer model simulations by integrating the MEMLS, forest impact, and atmospheric radiation attenuation. The random forest (RF) ML technique was used, and the optimal combination of independent variables was selected by correlation heatmap and importance ranking of variables. Six selected essential parameters, including Pec, vertical polarized brightness temperature differences between 18.7 and 36.5 GHz, ρ from ERA5, fforest, elevation, and longitude together with target SD output were used to train the RF model. Seventy percent of the entire data at meteorological stations were randomly chosen to train the RF model, and the remaining 30% were adopted as the validation data from 2013 to 2020 to ensure the mutual independence of training data and validation data. Compared with independent in situ measurements, the overall RMSE of SD is 13.6 cm, which was noticeably lower than those of the international SD remote sensing products and reanalysis datasets, including AMSR2 (36.2 cm), GlobSnow 3.0 (25.3 cm), ERA5 (38.2 cm), and ERA5-land (29.9 cm). The results demonstrated that the new proposed algorithm, by coupling the MEMLS model with the ML technique, could significantly enhance the overall performance in SD retrieval at global scales.

Linking Snow, Soil, and Stream During Snowmelt and Rain‐On‐Snow Events: Storage and Transport of Ions in an Acid‐Sensitive Alpine Catchment (Tatra Mountains, Poland)

AbstractThe purpose of the study was to examine the storage and transport of ions through snowpack, soils, and stream water in an acid‐sensitive alpine catchment (Tatra Mountains, Poland) during snowmelt and rain‐on‐snow events. Samples of snowpack layers, near‐surface soil horizons, and stream water were collected in the winter and snowmelt seasons of 2019. A laboratory experiment was conducted to determine the effect of temperature on the rate of soil nitrogen mineralization and nitrification. Our study has shown that snowpack is an important source of ions in the catchment. As the snow melts, the release of ions from snowpack occurs. A gradual and slow melting of snow starts even before the first snowmelt‐induced increase in stream discharge. ions eluted from available snowpack are temporarily stored in soil, which is shown by a large increase in the concentration of water‐soluble in the soil at that time. ions are washed out of soils and supplied to streams during the first snowmelt event. This is demonstrated by a large increase in the stream water concentration, termed an “ pulse.” The ion is a key acid anion responsible for the acidification of the studied stream during snowmelt season, as the pulse coincides with a decrease in bicarbonate alkalinity. Our field research and laboratory experiment have shown a minor role of mineralization and nitrification in production in soils in the winter and pulse formation in stream water during the early stages of the snowmelt season.

Temporal evolution of crack propagation characteristics in a weak snowpack layer: conditions of crack arrest and sustained propagation

Abstract. For a slab avalanche to release, we need sustained crack propagation in a weak snow layer beneath a cohesive snow slab – a process we call dynamic crack propagation. Field measurements on crack propagation are very scarce. We therefore performed a series of crack propagation experiments, up to 9 m long, over a period of 10 weeks and analysed these using digital image correlation techniques. We derived the elastic modulus of the slab (0.5 to 50 MPa), the elastic modulus of the weak layer (50 kPa to 1 MPa) and the specific fracture energy of the weak layer (0.1 to 1.5 J m−2) with a homogeneous and a layered-slab model. During crack propagation, we measured crack speed, touchdown distance, and the energy dissipation due to compaction and dynamic fracture (5 mJ m−2 to 0.43 J m−2). Crack speeds were highest for experiments resulting in full propagation, and crack arrest lengths were always shorter than touchdown lengths. Based on these findings, an index for self-sustained crack propagation is proposed. Our data set provides unique insight and valuable data to validate models.

Investigation into percolation and liquid water content in a multi-layered snow model for wet snow instabilities in Glacier National Park, Canada

Water percolation in snow plays a crucial role in the avalanche risk assessment. Liquid water content and wetting front are hard to measure in the field; hence, accurate simulation of the phenomena can be of great help to forecasters. This study was the first to evaluate water percolation simulations with the SNOWPACK model using Richards’ scheme on Mount Fidelity, Glacier National Park, Canada. The study highlights that, at this site, an updated configuration on precipitation phase transition and new snow density can significantly improve simulations of the snow cover, and water percolation in particular, which can be relevant in an era of an increased occurrence of rain-on-snow (ROS) events. More specifically, emphasis was put on the quality of the input data and parameters. The analysis of the precipitation phase temperature threshold showed that a value of 1.4°C was the best suited to track the rain/snow transition on site. A 10-year analysis of 24-h precipitation measured using the rain gauge and 24-h new snow water equivalent showed an excellent correlation. New snow density sub-models were evaluated using the 24-h new snow density values taken by the park technicians. The BELLAIRE model performed best and was used to drive the snow simulations. Two SNOWPACK snow simulations were evaluated using 1) rain gauge precipitation amount (PCPM) and 2) automatic snow height measurement (HS) at the same site. Both runs simulated the main snowpack layers observed during the dry season (i.e., before spring percolation was observed), and both simulated the snow properties with good accuracy. The water equivalent of snow cover, used as a proxy for a first-order characterization of the simulations generated by both simulations, was slightly underestimated compared with four manual measurements taken on-site during the winter. Nevertheless, the comparison of both measured density and modeled bulk density showed great correspondence. The percolation timing and wetting front depth were evaluated using field measurements from field campaigns and continuous observations from on-site instruments. The main percolation events were correctly simulated and were coincident with the observed wet avalanche cycles. The results highlight the need for accurate input data on valid simulation of the wetting front and percolation timing on site. Good percolation information generated using the SNOWPACK model and Richards’ scheme could be used to assess the snowpack stability by forecasters in areas where such data are available.

A data exploration tool for averaging and accessing large data sets of snow stratigraphy profiles useful for avalanche forecasting

Abstract. Snowpack models can provide detailed insight about the evolution of the snow stratigraphy in a way that is not possible with direct observations. However, the lack of suitable data aggregation methods currently prevents the effective use of the available information, which is commonly reduced to bulk properties and summary statistics of the entire snow column or individual grid cells. This is only of limited value for operational avalanche forecasting and has substantially hampered the application of spatially distributed simulations, as well as the development of comprehensive ensemble systems. To address this challenge, we present an averaging algorithm for snow profiles that effectively synthesizes large numbers of snow profiles into a meaningful overall perspective of the existing conditions. Notably, the algorithm enables compiling of informative summary statistics and distributions of snowpack layers, which creates new opportunities for presenting and analyzing distributed and ensemble snowpack simulations.

Impacts of ecological succession and climate warming on permafrost aggradation in drained lake basins of the Tuktoyaktuk Coastlands, Northwest Territories, Canada

AbstractRapidly increasing air temperatures will alter permafrost conditions across the Arctic, but variation in soils, vegetation, snow conditions, and their effects on ground thermal regime complicate prediction across spatial and temporal scales. Processes that result in the emergence of new surfaces (lake drainage, channel migration, isostatic uplift, etc.) provide an opportunity to assess the factors influencing permafrost aggradation and terrain evolution under a warming climate. In this study we describe ground temperatures, vegetation, and snow and soil conditions at six drained lake basins (DLBs) that have exposed new terrain in the Tuktoyaktuk Coastlands in the last 20–100 years. We also use one‐dimensional thermal modeling to assess the impact of ecological succession and future climate scenarios on permafrost conditions in historical and future DLBs. Our field observations show that deep snow pack and shallow organic layers at shrub‐dominated DLBs promote increased thaw depth and ground temperatures compared to a sedge‐dominated DLB and two ancient DLB reference sites. Modeling of past and future drainages shows that climate warming projected under RCP 8.5 will reduce rates of permafrost aggradation and thickness, and drive top‐down thaw that could degrade permafrost in shrub‐dominated DLBs by the end of the century. Permafrost at sedge‐dominated sites was more resilient to warming under RCP 8.5, with the onset of top‐down thaw delayed until about 2080. Together, this indicates that the effects of ecological succession on organic soil development and snow drifting will strongly influence the aggradation and resilience of permafrost in DLBs. Our analysis suggests that DLBs and other emergent landscapes will be the first permafrost‐free environments to develop under a warming climate in the continuous permafrost zone.

Global Sensitivity Analysis of the MEMLS Model for Retrieving Snow Water Equivalent

Sensitivity analysis (SA) of model parameters is of great importance for understanding, development, and application of models. However, the influence of snow microstructure variability on snow water equivalent retrieval from passive microwave measurements is still unclear. This article explores the parameter sensitivity of the microwave emission model of layered snowpacks (MEMLS) with improved born approximation (IBA) by using a quantitative global SA method, the extended Fourier amplitude sensitivity test (EFAST) algorithm. A deep analysis is conducted, including the sensitivity of passive microwave emission to snow parameters, the sensitivity variation analysis for different snow conditions, and the temporal properties of the parameter sensitivity. The results show the exponential correlation length, snow depth, and snow density are the three most sensitive parameters for snow without salt in the MEMLS model for the brightness temperature gradient at 18.7 and 36.5 GHz. For snow with a small salt content, the exponential correlation length, snow depth, snow temperature, and snow density are the four most sensitive parameters. Second, snow parameter variability highly affects the microwave radiation. The sensitivity values of microwave brightness temperature to snow depth gradually increase when the exponential correlation length is less than 0.25 mm and then slightly decreases with the increase of exponential correlation length and decreases along with the increase of snow density. Finally, our analysis highlights the importance to include the snow density, especially for deep snow depth, in the combination of sensitive factors in future multiparameter retrievals.

Crack propagation speeds in weak snowpack layers

AbstractFor the release of a slab avalanche, crack propagation within a weak snowpack layer below a cohesive snow slab is required. As crack speed measurements can give insight into underlying processes, we analysed three crack propagation events that occurred in similar snowpacks and covered all scales relevant for avalanche release. For the largest scale, up to 400 m, we estimated crack speed from an avalanche movie; for scales between 5 and 25 m, we used accelerometers placed on the snow surface and for scales below 5 m, we performed a propagation saw test. The mean crack speeds ranged from 36 ± 6 to 49 ± 5 m s−1, and did not exhibit scale dependence. Using the discrete element method and the material point method, we reproduced the measured crack speeds reasonably well, in particular the terminal crack speed observed at smaller scales. Finally, we used a finite element model to assess the speed of different elastic waves in a layered snowpack. Results suggest that the observed cracks propagated as mixed mode closing cracks and that the flexural wave of the slab is responsible for the energy transfer to the crack tip.

Dynamic crack propagation in weak snowpack layers: insights from high-resolution, high-speed photography

Abstract. Dynamic crack propagation in snow is of key importance for avalanche release. Nevertheless, it has received very little experimental attention. With the introduction of the propagation saw test (PST) in the mid-2000s, a number of studies have used particle tracking analysis of high-speed video recordings of PST experiments to study crack propagation processes in snow. However, due to methodological limitations, these studies have provided limited insight into dynamical processes such as the evolution of crack speed within a PST or the touchdown distance, i.e. the length from the crack tip to the trailing point where the slab comes to rest on the crushed weak layer. To study such dynamical effects, we recorded PST experiments using a portable high-speed camera with a horizontal resolution of 1280 pixels at rates of up to 20 000 frames s−1. We then used digital image correlation (DIC) to derive high-resolution displacement and strain fields in the slab, weak layer and substrate. The high frame rates enabled us to calculate time derivatives to obtain velocity and acceleration fields. We demonstrate the versatility and accuracy of the DIC method by showing measurements from three PST experiments, resulting in slab fracture, crack arrest and full propagation. We also present a methodology to determine relevant characteristics of crack propagation, namely the crack speed (20–30 m s−1), its temporal evolution along the column and touchdown distance (2.7 m) within a PST, and the specific fracture energy of the weak layer (0.3–1.7 J m−2). To estimate the effective elastic modulus of the slab and weak layer as well as the weak layer specific fracture energy, we used a recently proposed mechanical model. A comparison to already-established methods showed good agreement. Furthermore, our methodology provides insight into the three different propagation results found with the PST and reveals intricate dynamics that are otherwise not accessible.

Mercury in precipitated and surface snow at Dome C and a first estimate of mercury depositional fluxes during the Austral summer on the high Antarctic plateau

The role of deposition fluxes on the mercury cycle at Concordia station, on the high Antarctic plateau have been investigated over the Austral summer between December 2017 to January 2018. Wet/frozen deposition was collected daily from specially sited tables, simultaneously with the collection of surface (0–3 cm) and subsurface (3–6 cm) snow and the analysis of Hg0 in the ambient air. Over the course of the experiment the atmospheric Hg0 concentrations ranged from 0.58 ± 0.19 to 1.00 ± 0.33 ng m−3, surface snow Hg concentrations varied between (0–3 cm) 0.006 ± 0.003 to 0.001 ± 0.001 ng cm−3 and subsurface snow (3–6 cm) concentrations varied between 0.001 ± 0.001 to 0.003 ± 0.002 ng cm−3. The maximum daily wet deposition flux was found to be 23 ng m−2 d−1. Despite the low temporal resolution of our measurements combined with their potential errors, the linear regression of the Hg deposition fluxes against the snow accumulation rates allowed us to estimate the mean dry deposition rate from the intercept of the graph as −0.005 +- 0.008 ng m−2 d−1. From this analysis, we conclude that wet deposition accounts for the vast majority of the Hg deposition fluxes at Concordia Station. The number of snow events, together with the continuous GEM measurements have allowed us to make a first estimation of the mean snow scavenging factor at Dome C. Using the slope of the regression of mercury flux on snow accumulation we obtained a snow scavenging factor that ranges from 0.21 to 0.22 ± 0.02 (ngHg/g snow)/(ngHg/m3air). Our data indicate that the boundary layer height and local meteorological effects influence Hg0 reemission from the top of (0–3 cm) the snowpack into the atmosphere and into the deeper snowpack layer (3–6 cm). These data will help constrain numerical models on the behaviour of mercury in Antarctica.

Surface melting over the Greenland ice sheet derived from enhanced resolution passive microwave brightness temperatures (1979–2019)

Abstract. Surface melting is a major component of the Greenland ice sheet surface mass balance, and it affects sea level rise through direct runoff and the modulation of ice dynamics and hydrological processes, supraglacially, englacially and subglacially. Passive microwave (PMW) brightness temperature observations are of paramount importance in studying the spatial and temporal evolution of surface melting due to their long temporal coverage (1979–present) and high temporal resolution (daily). However, a major limitation of PMW datasets has been the relatively coarse spatial resolution, which has historically been of the order of tens of kilometers. Here, we use a newly released PMW dataset (37 GHz, horizontal polarization) made available through a NASA “Making Earth System Data Records for Use in Research Environments” (MeASUREs) program to study the spatiotemporal evolution of surface melting over the Greenland ice sheet at an enhanced spatial resolution of 3.125 km. We assess the outputs of different detection algorithms using data collected by automatic weather stations (AWSs) and the outputs of the Modèle Atmosphérique Régional (MAR) regional climate model. We found that sporadic melting is well captured using a dynamic algorithm based on the outputs of the Microwave Emission Model of Layered Snowpack (MEMLS), whereas a fixed threshold of 245 K is capable of detecting persistent melt. Our results indicate that, during the reference period from 1979 to 2019 (from 1988 to 2019), surface melting over the ice sheet increased in terms of both duration, up to 4.5 (2.9) d per decade, and extension, up to 6.9 % (3.6 %) of the entire ice sheet surface extent per decade, according to the MEMLS algorithm. Furthermore, the melting season started up to 4.0 (2.5) d earlier and ended 7.0 (3.9) d later per decade. We also explored the information content of the enhanced-resolution dataset with respect to the one at 25 km and MAR outputs using a semi-variogram approach. We found that the enhanced product is more sensitive to local-scale processes, thereby confirming the potential of this new enhanced product for monitoring surface melting over Greenland at a higher spatial resolution than the historical products and for monitoring its impact on sea level rise. This offers the opportunity to improve our understanding of the processes driving melting, to validate modeled melt extent at high resolution and, potentially, to assimilate these data in climate models.

Performance of MAR (v3.11) in simulating the drifting-snow climate and surface mass balance of Adélie Land, East Antarctica

Abstract. Drifting snow, or the wind-driven transport of snow particles originating from clouds and the surface below and above 2 m above ground and their concurrent sublimation, is a poorly documented process on the Antarctic ice sheet, which is inherently lacking in most climate models. Since drifting snow mostly results from erosion of surface particles, a comprehensive evaluation of this process in climate models requires a concurrent assessment of simulated drifting-snow transport and the surface mass balance (SMB). In this paper a new version of the drifting-snow scheme currently embedded in the regional climate model MAR (v3.11) is extensively described. Several important modifications relative to previous version have been implemented and include notably a parameterization for drifting-snow compaction of the uppermost snowpack layer, differentiated snow density at deposition between precipitation and drifting snow, and a rewrite of the threshold friction velocity above which snow erosion initiates. Model results at high resolution (10 km) over Adélie Land, East Antarctica, for the period 2004–2018 are presented and evaluated against available near-surface meteorological observations at half-hourly resolution and annual SMB estimates. The evaluation demonstrates that MAR resolves the local drifting-snow frequency and transport up to the scale of the drifting-snow event and captures the resulting observed climate and SMB variability, suggesting that this model version can be used for continent-wide applications.

Evaluation of the Effective Microstructure Parameter of the Microwave Emission Model of Layered Snowpack for Multiple-Layer Snow

Natural snow, one of the most important components of the cryosphere, is fundamentally a layered medium. In forward simulation and retrieval, a single-layer effective microstructure parameter is widely used to represent the emission of multiple-layer snowpacks. However, in most cases, this parameter is fitted instead of calculated based on a physical theory. The uncertainty under different frequencies, polarizations, and snow conditions is uncertain. In this study, we explored different methods to reduce the layered snow properties to a set of single-layer values that can reproduce the same brightness temperature (TB) signal. A validated microwave emission model of layered snowpack (MEMLS) was used as the modelling tool. Multiple-layer snow TB from the snow’s surface was compared with the bulk TB of single-layer snow. The methods were tested using snow profile samples from the locally validated and global snow process model simulations, which follow the natural snow’s characteristics. The results showed that there are two factors that play critical roles in the stability of the bulk TB error, the single-layer effective microstructure parameter, and the reflectivity at the air–snow and snow–soil boundaries. It is important to use the same boundary reflectivity as the multiple-layer snow case calculated using the snow density at the topmost and bottommost layers instead of the average density. Afterwards, a mass-weighted average snow microstructure parameter can be used to calculate the volume scattering coefficient at 10.65 to 23.8 GHz. At 36.5 and 89 GHz, the effective microstructure parameter needs to be retrieved based on the product of the snow layer transmissivity. For thick snow, a cut-off threshold of 1/e is suggested to be used to include only the surface layers within the microwave penetration depth. The optimal method provides a root mean squared error of bulk TB of less than 5 K at 10.65 to 36.5 GHz and less than 10 K at 89 GHz for snow depths up to 130 cm.

Retrieval of Snow Depth on Arctic Sea Ice from the FY3B/MWRI

Given their high albedo and low thermal conductivity, snow and sea ice are considered key reasons for amplified warming in the Arctic. Snow-covered sea ice is a more effective insulator, which greatly limits the energy and momentum exchange between the atmosphere and surface, and further controls the thermal dynamic processes of snow and ice. In this study, using the Microwave Emission Model of Layered Snowpacks (MEMLS), the sensitivities of the brightness temperatures (TBs) from the FengYun-3B/MicroWave Radiometer Imager (FY3B/MWRI) to changes in snow depth were simulated, on both first-year and multiyear ice in the Arctic. Further, the correlation coefficients between the TBs and snow depths in different atmospheric and sea ice environments were investigated. Based on the simulation results, the most sensitive factors to snow depth, including channels of MWRI and their combination form, were determined for snow depth retrieval. Finally, using the 2012–2013 Operational IceBridge (OIB) snow depth data, retrieval algorithms of snow depth were developed for the Arctic on first-year and multiyear ice, separately. Validation using the 2011 OIB data indicates that the bias and standard deviation (Std) of the algorithm are 2.89 cm and 2.6 cm on first-year ice (FYI), respectively, and 1.44 cm and 4.53 cm on multiyear ice (MYI), respectively.

Improving the Snow Volume Scattering Algorithm in a Microwave Forward Model by Using Ground-Based Remote Sensing Snow Observations

Volume scattering (VS) estimation plays a critical role in microwave emission modeling of the snowpack. However, it is challenging to obtain VS accurately for different frequencies by using the microwave emission model of layered snowpacks (MEMLS), which is one of the representative microwave emission models. This article develops a new VS method to consider frequency and exponential correlation length based on a snowfield campaign from November 2015 to April 2016 in Altay, China. Compared with the commonly used empirical and improved Born approximation (IBA) algorithms, the proposed VS algorithm exhibits better performances at both 18 and 36 GHz with a wide range of snow grain sizes. The bias of brightness temperatures at vertical polarization from the proposed algorithm against the observed brightness temperatures are 1.1 K and −0.4 K at 18 and 36 GHz, respectively; the root mean square errors (RMSEs) are 1.8 K and 2.6 K, respectively. The RMSEs decreased by 16.2 K at 18 GHz and 6.5 K at 36 GHz compared with those from the empirical methods and by 2.1 K and 22.2 K compared with those from the IBA. This work demonstrates that the VS difference between 18 and 36 GHz is larger and the dependence of VS on grain size is weaker than those represented by existing methods.

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.