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Snow Microwave Emission Modeling of Ice Lenses Within a Snowpack Using the Microwave Emission Model for Layered Snowpacks
Abstract and Figures
Ice lens formation, which follows rain on snow events or melt-refreeze cycles in winter and spring, is likely to become more frequent as a result of increasing mean winter temperatures at high latitudes. These ice lenses significantly affect the microwave scattering and emission properties, and hence snow brightness temperatures that are widely used to monitor snow cover properties from space. To understand and interpret the spaceborne microwave signal, the modeling of these phenomena needs improvement. This paper shows the effects and sensitivity of ice lenses on simulated brightness temperatures using the microwave emission model of layered snowpacks coupled to a soil emission model at 19 and 37 GHz in both horizontal and vertical polarizations. Results when considering pure ice lenses show an improvement of 20.5 K of the root mean square error between the simulated and measured brightness temperature (Tb) using several in situ data sets acquired during field campaigns across Canada. The modeled Tbs are found to be highly sensitive to the vertical location of ice lenses within the snowpack.
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... Distribution of retrieved parameters at Ku-Band (Table 7). The range of retrieved K values from Picard et al. (2022) for both grain types (PG22) and the different values retrieved by King et al. (2018) and Montpetit et al. (2013) are also displayed (KJ18 and MB13 respectively). (Table 7), b) retrieved parameters for each site individually (distributed values shown in Figure 12) and c) the same parameterization as a) except the median values of ε ′ soil of the two clusters of Figure 12 were used. ...
... Though no significant relationship was found, the higher values of K H tend to be associated with higher depth hoar fraction (> 0.45). The median value retrieved of 1.11 is also in agreement with grain size correction factors 385 (ϕ), which can now be explained by the polydispersity (Picard et al., 2022), reported by King et al. (2018) and Montpetit et al. (2013) for Canadian Arctic tundra sites. Those studies applied a single correction factor to all layers and it is known that the microwave snow volume scattering is dominated by the depth hoar layer which tends to boost the overall polydispersity close to K H in this case. ...
Accurate snow information at high spatial and temporal resolution is needed to support climate services, water resource management, and environmental prediction services. However, snow remains the only element of the water cycle without a dedicated Earth Observation mission. The snow scientific community has shown that Ku-Band radar measurements provide quality snow information with its sensitivity to snow water equivalent and the wet/dry state of snow. With recent developments of tools like the Snow MicroPenetrometer (SMP) to retrieve snow microstructure data in the field and radiative transfer models like the Snow Microwave Radiative Transfer Model (SMRT), it becomes possible to properly characterize the snow and how it translates into radar backscatter measurements. An experiment at Trail Valley Creek (TVC), Northwest Territories, Canada was conducted during the winter of 2018/19 in order to characterize the impacts of varying snow geophysical properties on Ku-Band radar backscatter at a 100-m scale. Airborne Ku-Band data was acquired using the University of Massachusetts radar instrument. This study shows that it is possible to calibrate SMP data to retrieve statistical information on snow geophysical properties and properly characterize a representative snowpack at the experiment scale. The tundra snowpack measured during the campaign can be characterize by two layers corresponding to a rounded snow grain layer and a depth hoar layer. Using Radarsat-2 and TerraSAR-X data, soil background roughness properties were retrieved (msssoil = 0.010±0.002) and it was shown that a single value could be used for the entire domain. Microwave snow grain size polydispersity values of 0.74 and 1.11 for rounded and depth hoar snow grains, respectively, was retrieved. Using the Geometrical Optics surface backscatter model, the retrieved effective soil permittivity increased from C-Band (εsoil = 2.47) to X-Band (εsoil = 2.61), to Ku-Band (εsoil = 2.77) for the TVC domain. Using SMRT and the retrieved soil and snow parameterizations, an RMSE of 2.6 dB was obtained between the measured and simulated Ku-Band backscatter values when using a global set of parameters for all measured sites. When using a distributed set of soil and snow parameters, the RMSE drops to 0.9 dB. This study thus shows that it is possible to link Ku-Band radar backscatter measurements to snow conditions on the ground using a priori knowledge of the snow conditions to retrieve SWE at the 100 m scale.
... For each pixel of the MEaSUREs database, the closest NARR pixels is used to obtain a value of PWAT to use for the correction, this approach has been used in peer-reviewed articles for over a decade (Roy 2014). NARR is a commonly used dataset for meteorological data over the Canadian Arctic and it has been coupled with microwave datasets in many studies before (Montpetit et al. 2013;Dupont et al. 2014;Picard et al. 2013;Roy 2014). The corrected measurements ( Bcorr ) were calculated using the equation ...
Climate change has a profound effect on Arctic meteorology extreme events, such as rain-on-snow (ROS), which affects surface state variable spatial and temporal variability. Passive microwave satellite images can help detect such events in polar regions where local meteorological and snow information is scarce. In this study, we use a detection algorithm using high-resolution passive microwave data to monitor spatial and temporal variability of ROS over the Canadian Arctic Archipelago from 1987 to 2019. The method is validated using data from several meteorological stations and atmospheric corrections have been applied to the passive microwave dataset. Our approach to detect ROS is based on two methods: 1) over a fixed time period (i.e., 1 November–31 May) throughout the study period and 2) using an a priori detection for snow presence before applying our ROS algorithm (i.e., length of studied winter varies yearly). Event occurrence is analyzed for each winter and separated by island groups of the Canadian Arctic Archipelago. Results show an increase in absolute ROS occurrence, mainly along the coasts, although no statistically significant trends are observed.
Significance Statement
Rain-on-snow (ROS) is known to have significant consequences on vegetation and fauna, especially widespread events. This study aimed to use a recent high-resolution dataset of passive microwave observations to investigate spatial and temporal trends in ROS occurrence in the Arctic. Results show that a global increase in event occurrence can be observed across the arctic.
... Thus, algorithms utilizing polarization differences or ratios (e.g., ASI and NASA-Team) will be influenced by the presence of such layers. How strong a certain frequency is impacted depends generally on the thickness of the ice layer (Montpetit et al., 2013). When we include such an ice layer in the SMP-based modeling, the modeled data (bottom panels in Figure 7. Histogram of simulated polarization ratio, gradient ratio, and polarization difference for 84 SnowMicroPen profiles. ...
Warm air intrusions over Arctic sea ice can change the snow and ice surface conditions rapidly and can alter sea ice concentration (SIC) estimates derived from satellite-based microwave radiometry without altering the true SIC. Here we focus on two warm moist air intrusions during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition that reached the research vessel Polarstern in mid-April 2020. After the events, SIC deviations between different satellite products, including climate data records, were observed to increase. Especially, an underestimation of SIC for algorithms based on polarization difference was found. To examine the causes of this underestimation, we used the extensive MOSAiC snow and ice measurements to model computationally the brightness temperatures of the surface on a local scale. We further investigated the brightness temperatures observed by ground-based radiometers at frequencies 6.9 GHz, 19 GHz, and 89 GHz. We show that the drop in the retrieved SIC of some satellite products can be attributed to large-scale surface glazing, that is, the formation of a thin ice crust at the top of the snowpack, caused by the warming events. Another mechanism affecting satellite products, which are mainly based on gradient ratios of brightness temperatures, is the interplay of the changed temperature gradient in the snow with snow metamorphism. From the two analyzed climate data record products, we found that one was less affected by the warming events. The low frequency channels at 6.9 GHz were less sensitive to these snow surface changes, which could be exploited in future to obtain more accurate retrievals of sea ice concentration. Strong warm air intrusions are expected to become more frequent in future and thus their influence on SIC algorithms will increase. In order to provide consistent SIC datasets, their sensitivity to warm air intrusions needs to be addressed.
... In general, the H-pol brightness temperature is more complex because it is in part controlled by snow scattering and snow temperature (exactly as V-pol), and in addition, it is sensitive to the surface density and the vertical density fluctuations in the snowpack (layering). The ice layers decrease the brightness temperature at H-pol due to the reflections on the high dielectric contrast between snow and ice in the upper part of the firn (Montpetit et al., 2013). The variations in V-pol and H-pol are correlated and of similar amplitude only if the ice layer effect is negligible. ...
Surface melting on the Antarctic Ice Sheet has been monitored by satellite microwave radiometry for over 40 years. Despite this long perspective, our understanding of the microwave emission from wet snow is still limited, preventing the full exploitation of these observations to study supraglacial hydrology. Using the Snow Microwave Radiative Transfer (SMRT) model, this study investigates the sensitivity of microwave brightness temperature to snow liquid water content at frequencies from 1.4 to 37 GHz. We first determine the snowpack properties for eight selected coastal sites by retrieving profiles of density, grain size and ice layers from microwave observations when the snowpack is dry during wintertime. Second, a series of brightness temperature simulations is run with added water. The results show that (i) a small quantity of liquid water (≈0.5 kg m−2) can be detected, but the actual quantity cannot be retrieved out of the full range of possible water quantities; (ii) the detection of a buried wet layer is possible up to a maximum depth of 1 to 6 m depending on the frequency (6–37 GHz) and on the snow properties (grain size, density) at each site; (iii) surface ponds and water-saturated areas may prevent melt detection, but the current coverage of these waterbodies in the large satellite field of view is presently too small in Antarctica to have noticeable effects; and (iv) at 1.4 GHz, while the simulations are less reliable, we found a weaker sensitivity to liquid water and the maximal depth of detection is relatively shallow (<10 m) compared to the typical radiation penetration depth in dry firn (≈1000 m) at this low frequency. These numerical results pave the way for the development of improved multi-frequency algorithms to detect melt intensity and the depth of liquid water below the surface in the Antarctic snowpack.
... Field surveys have shown that there is distinct spatial heterogeneity in the distribution of snow particle size. The values of Pec can be calculated in three ways: 1) from the observed linear relationship between Pec and mean D max [19]; 2) from the optical grain size radius (measurements of snow specific surface area) and fractional volume [73]; and 3) from the mean D max and fractional volume [14]. Snow on land has zero salt content, and snow on sea ice is considered to contain weak salt content [16]. ...
Sensitivity analysis 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 paper explores the parameter sensitivity of the Microwave Emission Model of Layered Snowpacks (MEMLS) with improved Born approximation (IBA) by using a quantitative global sensitivity analysis 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 GHz 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 less than 0.25 mm and then slightly decrease with the increase of exponential correlation length and decrease along 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 the future multiparameter retrievals.
... The penetration of the incident EM wave in the snowpack depends on the extinction coefficient (k e ) of the snowpack. Here, k e represents the sum of the volume absorption coefficients (k a ) and the volume scattering coefficient (k s ) (Montpetit et al. 2013;Maslanka et al. 2019;Saberi et al. 2020). The imaginary part of the snowpack dielectric constant ðε 00 r Þ regulates this absorption coefficient (k a ), and the scattering coefficient (k s ) depends on the geometry and the inhomogeneities of the snowpack with respect to the wavelength of the incident wave (Leinß and Hajnsek 2012). ...
Seasonal alpine snow contributes significantly to the water resource. It plays a crucial role in regulating the environmental feedback and from the perspective of socio-economic sustainability in the alpine regions. While most nations are pursuing renewable energy sources, hydropower generated from snowmelt runoff is one of the primary sources. Additionally, alpine regions with snow cover are major tourist destinations that are often affected by natural disasters such as avalanches. The snowmelt runoff and early avalanche warning require timely information on the spatio-temporal aspects of the snow geophysical parameters. In this regard, advances in remote sensing of snow have been observed to be significant. Recent developments in remote sensing technology in the visible, infrared, and microwave spectrum have significantly improved our understanding of snow geophysical processes. This paper provides a review concerning the qualitative and quantitative studies of alpine snow. The electromagnetic characteristics of the
alpine snow are largely dependent upon its inherent geophysical structure and the properties of the snow. Snow behaves differently with respect to the wavelength of the incident radiation. In this paper, we provide a categorical review of the remote sensing techniques for estimating the snow geophysical properties, inclusive of permittivity, density, and wetness corresponding to the wavelength used in the remotely sensed data: (1) visible-infrared spectrum including multispectral/hyperspectral, (2) active and passive microwave spectrums. We also discuss the recent advancements in the remote sensing techniques for approximating the volumetric snowpack parameters such as the snow depth and the snow water equivalent based on active and passive microwave remote sensing. This review further discusses the limitations of the techniques reviewed and future prospects for the retrieval of snow geophysical parameters (SGP) corresponding to the recent progress in remote sensing technology. In summary, the recent advances have
laid down a foundation for rigorous assessment of seasonal snow using spaceborne remote sensing, particularly at a regional scale. Yet, the scope for improvements in the methods and payload design exists.
... No grain size measurements were recorded in layers classified as ice. 2) SSA was estimated from short wave infrared (SWIR) using a laser-based reflectance system [30] from which the physical relationship between albedo and SSA is described in [40]. At each site, an SSA profile was constructed by collecting several 6 cm thick samples of the snowpack at various depths. ...
Satellite based passive microwave observations provide the best available continuous observational estimates of global snow water storage due to their broad geographic footprint and low sensitivity to clouds and precipitation. However, these observations are subject to substantial uncertainty due to the complex radiative properties of snow and from interference in forested areas. Physically based radiative transfer models can be leveraged to improve the fidelity of these observations and as a data-assimilation tool. In this study, the Dense Media Radiative Transfer model with Multiple Layers (DMRT-ML) is used to simulate snow brightness temperatures from data collected from snow pits excavated during a two-day long field study performed a temperate forest in the Northeast United States. The simulations are evaluated against surface-based radiometer observations collected at the snow pits. The DMRT-ML is configured with varying complexity to determine the snowpack characteristics most essential towards simulating brightness temperature within a temperate forest with complicated snow stratigraphy. In general, the single layer configurations were not sufficiently complex to accurately simulate snow brightness temperature without significant tuning. The most accurate simulation was a two-layer configuration with a prescribed ice-layer separating the snow layers. This simulation had a RMSE <15K for the 37GHz frequency. More complicated snowpack stratigraphy configurations did not substantively improve the results over the two-layer model configuration. The DMRT-ML was also used to examine differences between redundant datasets of density and grain-size. It was determined that similar snow data collection and radiative transfer model configuration techniques are critical to ensure cross-study comparability.
... In addition to the aspects discussed above, the experimental results of g will be influenced by the near-surface freeze/thaw state (Zhao et al. 2011 and other geometric conditions (Montpetit et al. 2013), for instance, the difference in height of trees, thickness of forest (from radiometer to edge of the forest), distance of radiometer to the nearest tree. Even though the observations on forest transmissivity are complex and challenging, we have found that the influence of the main factors discussed above on the transmissivity calculation based on radiometric measurements is relatively small. ...
Forests have invariably been considered as an obstacle in retrieving land surface parameters from spaceborne passive microwave brightness temperature (TB) observations. For quantifying the effect of forests on microwave signals, several models have been developed. However, these models rarely reveal the dependence of microwave radiation on forest types, which can hardly meet the needs of high-accuracy retrieval of terrestrial parameters in forested regions. A ground-based microwave radiometric observation experiment was designed to investigate the dependence of microwave radiation on frequency, polarization, and forest type. Downward TB at 18.7- and 36.5-GHz for horizontal- and vertical-polarization from the forest canopy was measured at 14 sample plots in Northeast China, along with snowpack and forest structural parameters. By providing fits to experimental data, new empirical transmissivity models for three forest types were developed, as a function of woody stem volume and depending on the frequency/polarization. The proposed models give diverse asymptotic transmissivity saturation levels and the corresponding saturation point of woody stem volume for different forest types. Root-mean-square error results between TB simulations and Advanced Microwave Scanning Radiometer-2 observations are approximately 3–6 K. This study provides an experimental and theoretical reference for further development of inversion models for snow parameters in forested areas.
Rain-on-snow has decimated ungulate herds in the North and warmed
permafrost significantly in Spitsbergen. As the permafrost temperatures
are used as an integrated signal of the climate change, there is an
urgent need to characterize the relationship between rain-on-snow and
permafrost temperatures. By incorporating reanalysis based (ERA40)
climate forcing into the land model (CLM3) and introducing an artificial
rain on snow event on all model pixels the areas with thick snow cover
(>0.5 m) experienced season average permafrost warming, sites with
intermediate snow depths (0.15-0.5 m) experienced cooling, while sites
with thin snow cover were more sensitive to other factors.
Snow cover characteristics have significant effects on upwelling naturally emitted microwave radiation through processes of forward scattering. This study simulates numerically the electromagnetic responses from snow in the UK using the radiative transfer-based semiempirical model developed at the Helsinki University of Technology (HUT), which takes into account the influence of soil surface, forest canopy and atmosphere on space-borne observed brightness temperature by using empirical and semiempirical formulas. A sensitivity analysis of the HUT model was conducted to determine the most sensitive parameter affecting upwelling radiation from snow in the UK. The model-based results were compared with observed Special Sensor Microwave Imager (SSM/I) brightness temperatures to better understand the SSM/I response to snow. The available ensemble of data required for input to the HUT model comprise surface physical temperature, ground level pressure and water vapour content, forest stem volume and land cover water fraction. Based on the sensitivity analyses, numerical parameters representing physical snow pack quantities (e.g. snow grain size, snow moisture and snow depth (SD)) were varied and the method of root mean square error (RMSE) minimization was used to invert the SD. The HUT model was applied to different days in 3 months (23-31 January, 1-5 and 26-27 February and 1-7 March 1995) of records of daily SD and SSM/I observations. The results show that the HUT model both underestimates and overestimates SD prediction. For the month of January 1995, the HUT model underestimated SD with a bias of -0.59 cm, whereas for February and March 1995 the HUT model overestimated the SD with a bias of 1.89 cm and 1.64 cm, respectively. This study demonstrates that microwave remote sensing of snow can be used successfully in the UK, where most research on snow cover is conducted by using a visible and infrared radiometer. It is also evident from this work that application of algorithms to snow pack monitoring needs local calibration for effective and reasonable results.
Satellite-passive microwave remote sensing has been extensively used to
estimate snow water equivalent (SWE) in northern regions. Although
passive microwave sensors operate independent of solar illumination and
the lower frequencies are independent of atmospheric conditions, the
coarse spatial resolution introduces uncertainties to SWE retrievals due
to the surface heterogeneity within individual pixels. In this article,
we investigate the coupling of a thermodynamic multilayered snow model
with a passive microwave emission model. Results show that the snow
model itself provides poor SWE simulations when compared to field
measurements from two major field campaigns. Coupling the snow and
microwave emission models with successive iterations to correct the
influence of snow grain size and density significantly improves SWE
simulations. This method was further validated using an additional
independent data set, which also showed significant improvement using
the two-step iteration method compared to standalone simulations with
the snow model.
In the past it has often been difficult to compare results of different types of snow-structural information. Grain-size and correlation length are such parameters of granular media, and there exist different definitions and different measurement methods for both of them. The relation between these parameters is analyzed from theoretical and from experimental points of view, considering optical and microwave properties. For spherical ice grains the connecting formulas are simple, but for other shapes the two parameters are not directly related. Care must be taken in the measurement procedure. Especially if grain-size is regarded as the maximum extent of connected ice particles, the results are likely to lead to extreme overestimates. Therefore it is concluded that grain-size should be complemented by an additional size parameter, namely, the surface-to-volume ratio of equivalent spheres, i.e. a measure of the correlation length. Methods to determine this quantity in the laboratory have been known for a long time. Methods to obtain such measurements in the field are described here.
The polar ice sheets and glacier ice contain the majority of the terrestrial water-ice mass. Snow, the freshly precipitated form of ice, covers, to a variable degree, very large parts of the terrestrial surface during the winter season. These icy bodies possess spectral and polarimetric signatures in the microwave range which are suitable for both active (radar) and passive (radiometric) remote sensing. The signatures are related to the special dielectric properties on the one hand, and on the other, to the characteristic structural behavior, ranging from microscopic to macroscopic scale, and being different for different parts of the cryosphere.
The microwave emission model of layered snowpacks (MEMLS) is a multilayer and multiple-scattering radiative transfer model developed for dry winter snow using an empirical parametrization of the scattering coefficient (se the copanion article). A limitation is in the applicable range of frequencies and correlation lengths. In order to extend the model, a physical determination of the volume-scattering coefficients, describing the coupling between the six fluxes, is developed here, based on the improved Born approximation. An exponential spatial autocorrelation function was selected. With this addition, MEMLS obtains a complete physical basis. The extended model is void of free parameters. The validation was done with two types of experiments made at the alpine test site, Weissfluhjoch: 1) radiometry at 11 GHz, 21 GHz, 35 GHz, 48 GHz, and 94 GHz of winter snow samples on a blackbody and on a metal plate, respectively, and 2) radiometric monitoring at 4.9 GHz, 10.4 GHz, 21 GHz, 35 GHz, and 94 GHz of coarse-grained crusts growing and decaying during melt-and-refreeze cycles. Digitized snow sections were used to measure snow structure in both experiments. The coarsest grains were found in the refrozen crusts with a correlation length up to 0.71 mm; the winter snow samples had smaller values, from 0.035 mm for new snow to about 0.33 mm for depth hoar. Good results have been obtained in all cases studied so far.
