The Experts below are selected from a list of 17046 Experts worldwide ranked by ideXlab platform
Laurent Ferrofamil - One of the best experts on this subject based on the ideXlab platform.
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Snowpack permittivity profile retrieval from tomographic sar data
Comptes Rendus Physique, 2017Co-Authors: Badreddine Rekioua, Stefano Tebaldini, Matthieu Davy, Laurent FerrofamilAbstract:This work deals with 3D structure characterization and permittivity profile retrieval of Snowpacks by tomographic SAR data processing. The acquisition system is a very high resolution ground based SAR system, developed and operated by the SAPHIR team, of IETR, University of Rennes-1 (France). It consists mainly of a vector network analyser and a multi-static antenna system, moving along two orthogonal directions, so as to obtain a two-dimensional synthetic array. Data were acquired during the AlpSAR campaign carried by the European Space Agency and led by ENVEO. In this study, tomographic imaging is performed using Time Domain Back Projection and consists in coherently combining the different recorded backscatter contributions. The assumption of free-space propagation during the focusing process is discussed and illustrated by focusing experimental data. An iterative method for estimating true refractive indices of the snow layers is presented. The antenna pattern is also compensated for. The obtained tomograms after refractive index correction are compared to the stratigraphy of the observed Snowpack
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1d var multilayer assimilation of x band sar data into a detailed Snowpack model
The Cryosphere, 2014Co-Authors: Xuanvu Phan, Laurent Ferrofamil, Marie Dumont, Y Durand, S Morin, Sophie Allain, Guy Durso, Alexandre GirardAbstract:The structure and physical properties of a snow- pack and their temporal evolution may be simulated using meteorological data and a snow metamorphism model. Such an approach may meet limitations related to potential diver- gences and accumulated errors, to a limited spatial resolu- tion, to wind or topography-induced local modulations of the physical properties of a snow cover, etc. Exogenous data are then required in order to constrain the simulator and im- prove its performance over time. Synthetic-aperture radars (SARs) and, in particular, recent sensors provide reflectivity maps of snow-covered environments with high temporal and spatial resolutions. The radiometric properties of a Snowpack measured at sufficiently high carrier frequencies are known to be tightly related to some of its main physical parame- ters, like its depth, snow grain size and density. SAR acqui- sitions may then be used, together with an electromagnetic backscattering model (EBM) able to simulate the reflectiv- ity of a Snowpack from a set of physical descriptors, in or- der to constrain a physical Snowpack model. In this study, we introduce a variational data assimilation scheme coupling TerraSAR-X radiometric data into the Snowpack evolution model Crocus. The physical properties of a Snowpack, such as snow density and optical diameter of each layer, are simu- lated by Crocus, fed by the local reanalysis of meteorological data (SAFRAN) at a French Alpine location. These snow- pack properties are used as inputs of an EBM based on dense media radiative transfer (DMRT) theory, which simulates the total backscattering coefficient of a dry snow medium at X and higher frequency bands. After evaluating the sensi- tivity of the EBM to Snowpack parameters, a 1D-Var data assimilation scheme is implemented in order to minimize the discrepancies between EBM simulations and observa- tions obtained from TerraSAR-X acquisitions by modifying the physical parameters of the Crocus-simulated Snowpack. The algorithm then re-initializes Crocus with the modified Snowpack physical parameters, allowing it to continue the simulation of Snowpack evolution, with adjustments based on remote sensing information. This method is evaluated us- ing multi-temporal TerraSAR-X images acquired over the specific site of the Argentiere glacier (Mont-Blanc massif, French Alps) to constrain the evolution of Crocus. Results indicate that X-band SAR data can be taken into account to modify the evolution of Snowpack simulated by Crocus.
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1d var multilayer assimilation of x band sar data into a detailed Snowpack model
The Cryosphere, 2014Co-Authors: Xuanvu Phan, Laurent Ferrofamil, Marie Dumont, Y Durand, S Morin, Sophie Allain, Guy Durso, Alexandre GirardAbstract:The structure and physical properties of a snow- pack and their temporal evolution may be simulated using meteorological data and a snow metamorphism model. Such an approach may meet limitations related to potential diver- gences and accumulated errors, to a limited spatial resolu- tion, to wind or topography-induced local modulations of the physical properties of a snow cover, etc. Exogenous data are then required in order to constrain the simulator and im- prove its performance over time. Synthetic-aperture radars (SARs) and, in particular, recent sensors provide reflectivity maps of snow-covered environments with high temporal and spatial resolutions. The radiometric properties of a Snowpack measured at sufficiently high carrier frequencies are known to be tightly related to some of its main physical parame- ters, like its depth, snow grain size and density. SAR acqui- sitions may then be used, together with an electromagnetic backscattering model (EBM) able to simulate the reflectiv- ity of a Snowpack from a set of physical descriptors, in or- der to constrain a physical Snowpack model. In this study, we introduce a variational data assimilation scheme coupling TerraSAR-X radiometric data into the Snowpack evolution model Crocus. The physical properties of a Snowpack, such as snow density and optical diameter of each layer, are simu- lated by Crocus, fed by the local reanalysis of meteorological data (SAFRAN) at a French Alpine location. These snow- pack properties are used as inputs of an EBM based on dense media radiative transfer (DMRT) theory, which simulates the total backscattering coefficient of a dry snow medium at X and higher frequency bands. After evaluating the sensi- tivity of the EBM to Snowpack parameters, a 1D-Var data assimilation scheme is implemented in order to minimize the discrepancies between EBM simulations and observa- tions obtained from TerraSAR-X acquisitions by modifying the physical parameters of the Crocus-simulated Snowpack. The algorithm then re-initializes Crocus with the modified Snowpack physical parameters, allowing it to continue the simulation of Snowpack evolution, with adjustments based on remote sensing information. This method is evaluated us- ing multi-temporal TerraSAR-X images acquired over the specific site of the Argentiere glacier (Mont-Blanc massif, French Alps) to constrain the evolution of Crocus. Results indicate that X-band SAR data can be taken into account to modify the evolution of Snowpack simulated by Crocus.
Bruce Jamieson - One of the best experts on this subject based on the ideXlab platform.
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fracture propagation propensity in relation to snow slab avalanche release validating the propagation saw test
Geophysical Research Letters, 2008Co-Authors: Dave Gauthier, Bruce JamiesonAbstract:[1] The ‘Propagation Saw Test’ (PST) was designed to assess the fracture propagation propensity of weak Snowpack layers in relation to snow slab avalanche release. Its predictions were tested against independent field observations of weak layer fracture initiation and slope-scale fracture propagation (e.g., avalanche release). A total of 170 tests were performed at 23 sites. Approximately 76% of tests correctly predicted the observed slope-scale fracture propagation or lack thereof; however, 20% of tests predicted that propagation would not occur at sites that had recently propagated fractures. The predictive accuracy of the dataset improves if only test columns approximately 1.0 m long are selected. Critical fracture energy release rate calculations show that the lowest values are found in Snowpacks where fractures initiated but did not propagate. This suggests that physical descriptions of propagation propensity in weak Snowpack layers should include a sustainability term.
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Snowpack properties associated with fracture initiation and propagation resulting in skier triggered dry snow slab avalanches
Cold Regions Science and Technology, 2007Co-Authors: A Van Herwijnen, Bruce JamiesonAbstract:This study investigates Snowpack properties associated with skier-triggered dry slab avalanches, with a particular view on Snowpack conditions favoring fracture propagation. This was done by analyzing a data set of over 500 snow profiles observed next to skier-triggered slabs (including remotely triggered slab avalanches and whumpfs) and on skier-tested slopes that did not release a slab avalanche. The relation of the Snowpack variables with fracture initiation and fracture propagation, both of which are required for skier-triggering, was investigated. Specific Snowpack characteristics, including hardness difference and difference in crystal size across the failure layer, associated with skier-triggered dry slab avalanches were identified and the frequency of skier-triggering was determined. In order to assess Snowpack variables favouring fracture propagation, variables from failure layers associated with skier-triggered slabs that were not remotely triggered and relatively small were contrasted with Snowpack variables from failure layers of remotely triggered slab avalanches, whumpfs and relatively large slab avalanches. The properties of the slab overlying the weak layer, as well as the layer above the weak layer, were found to affect fracture propagation. Stiffer slabs were associated with large avalanches as well as whumpfs and remotely triggered avalanches. Furthermore, a correlation analysis of Snowpack variables with the size and width of the investigated slab avalanches further accentuated the importance of these slab properties with regards to fracture propagation.
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towards a field test for fracture propagation propensity in weak Snowpack layers
Journal of Glaciology, 2006Co-Authors: Dave Gauthier, Bruce JamiesonAbstract:Slab avalanche release requires fracture initiation and propagation in a weak Snowpack layer. While field tests of weak-layer strength are useful for fracture initiation, the challenge remains to find a verified field test for fracture propagation. We introduce the two current versions of a field test for fracture propagation propensity, and report results of testing conducted in the Columbia Mountains of British Columbia, Canada, during the winter of 2005. By extending the column of a stability test approximately 3 m in the downslope direction, the test method allows for the development of a flexural wave in the slab, and thereby maintains the contribution of this wave and the associated weak-layer collapse to the fracture process. Fracture lengths collected on a day and location where the propagation propensity of the Snowpack was locally high show a bimodal distribution, with approximately 50% of observed fractures similar to those collected in stable Snowpacks, and approximately 50% with much longer fracture lengths.
Celine Vargel - One of the best experts on this subject based on the ideXlab platform.
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Improved Simulation of Arctic Circumpolar Land Area Snow Properties and Soil Temperatures
'Frontiers Media SA', 2021Co-Authors: Alain Royer, Alexandre Langlois, Ghislain Picard, Celine Vargel, Isabelle Gouttevin, Marie DumontAbstract:The impact of high latitude climate warming on Arctic snow cover and its insulating properties has key implications for the surface and soil energy balance. Few studies have investigated specific trends in Arctic Snowpack properties because there is a lack of long-term in situ observations and current detailed snow models fail to represent the main traits of Arctic Snowpacks. This results in high uncertainty in modeling snow feedbacks on ground thermal regime due to induced changes in snow insulation. To better simulate Arctic snow structure and snow thermal properties, we implemented new parameterizations of several snow physical processes—including the effect of Arctic low vegetation and wind on Snowpack—in the Crocus detailed Snowpack model. Significant improvements compared to standard Crocus snow simulations and ERA-Interim (ERAi) reanalysis snow outputs were observed for a large set of in-situ snow data over Siberia and North America. Arctic Crocus simulations produced improved Arctic snow density profiles over the initial Crocus version, leading to a soil surface temperature bias of −0.5 K with RMSE of 2.5 K. We performed Crocus simulations over the past 39 years (1979–2018) for circumpolar taiga (open forest) and pan-Arctic areas at a resolution of 0.5°, driven by ERAi meteorological data. Snowpack properties over that period feature significant increase in spring snow bulk density (mainly in May and June), a downward trend in snow cover duration and an upward trend in wet snow (mainly in spring and fall). The pan-Arctic maximum snow water equivalent shows a decrease of −0.33 cm dec−1. With the ERAi air temperature trend of +0.84 K dec−1 featuring Arctic winter warming, these snow property changes have led to an upward trend in soil surface temperature (Tss) at a rate of +0.41 K dec−1 in winter. We show that the implemented Snowpack property changes increased the Tss trend by 36% compared to the standard simulation. Winter induced changes in Tss led to a significant increase of 16% (+4 cm dec−1) in the estimated active layer thickness (ALT) over the past 39 years. An increase in ALT could have a significant impact on permafrost evolution, Arctic erosion and hydrology
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arctic and subarctic snow microstructure analysis for microwave brightness temperature simulations
Remote Sensing of Environment, 2020Co-Authors: Alexandre Roy, Alain Royer, Ghislain Picard, Celine Vargel, Olivier Stjeanrondeau, Vincent Sasseville, Alexandre LangloisAbstract:Abstract Passive microwave (PMW) remote sensing has proven to be a useful approach to characterize the volume of seasonal Snowpack in remote northern regions at the synoptic scale. Modeling emitted microwave brightness temperatures (TB) is made possible using a physical radiative transfer model that takes into account microstructural and stratigraphic structure of the Snowpack. However, prescribing the microstructure remains a difficult task. This paper aims to find proper microstructure parametrization and the snow emission model formulation that best optimize TB simulations for Arctic and Subarctic Snowpacks. Surfaced-based radiometric measurements in conjunction with in-situ Snowpack characterization were used for testing different configurations based on the Snow Microwave Radiative Transfer model (SMRT), with two electromagnetic models (Dense Media Radiative Transfer Quasi Crystalline Approximation, DMRT, and Improved Born Approximation, IBA) and two microstructure description theories (Sticky Hard Sphere, SHS, and Exponential, Exp). We compare the performance of three configurations (DMRT-SHS, IBA-SHS and IBA-Exp) with a unique large dataset (119 snowpits with concomitant microwave ground-based radiometer observations) covering a wide range of Arctic and Subarctic snow types in Northern and Eastern Canada. Results show that the input measured microstructure parameters must be scaled up in order to better match simulated and observed TB at 11, 19, 37 and 89 GHz. We show that the IBA-Exp gives the best results, with a Root-Mean-Square Error (RMSE) lower by up to 30% for Subarctic snow and 24% for Arctic snow compare to the other model configurations we used. In addition, we undertake a complementary experiment on isolated homogeneous snow slabs to investigate the sensitivity of the scaling factor to snow microstructure. The retrieved microwave correlation length appears significantly different than the in-situ Debye correlation length. At high frequencies, the observed variability of these scaling factors with frequency and Snowpack types means that density, SSA and estimated correlation length seem insufficient to appropriately fully characterize snow microstructure for microwave modeling purposes.
Alain Royer - One of the best experts on this subject based on the ideXlab platform.
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Improved Simulation of Arctic Circumpolar Land Area Snow Properties and Soil Temperatures
'Frontiers Media SA', 2021Co-Authors: Alain Royer, Alexandre Langlois, Ghislain Picard, Celine Vargel, Isabelle Gouttevin, Marie DumontAbstract:The impact of high latitude climate warming on Arctic snow cover and its insulating properties has key implications for the surface and soil energy balance. Few studies have investigated specific trends in Arctic Snowpack properties because there is a lack of long-term in situ observations and current detailed snow models fail to represent the main traits of Arctic Snowpacks. This results in high uncertainty in modeling snow feedbacks on ground thermal regime due to induced changes in snow insulation. To better simulate Arctic snow structure and snow thermal properties, we implemented new parameterizations of several snow physical processes—including the effect of Arctic low vegetation and wind on Snowpack—in the Crocus detailed Snowpack model. Significant improvements compared to standard Crocus snow simulations and ERA-Interim (ERAi) reanalysis snow outputs were observed for a large set of in-situ snow data over Siberia and North America. Arctic Crocus simulations produced improved Arctic snow density profiles over the initial Crocus version, leading to a soil surface temperature bias of −0.5 K with RMSE of 2.5 K. We performed Crocus simulations over the past 39 years (1979–2018) for circumpolar taiga (open forest) and pan-Arctic areas at a resolution of 0.5°, driven by ERAi meteorological data. Snowpack properties over that period feature significant increase in spring snow bulk density (mainly in May and June), a downward trend in snow cover duration and an upward trend in wet snow (mainly in spring and fall). The pan-Arctic maximum snow water equivalent shows a decrease of −0.33 cm dec−1. With the ERAi air temperature trend of +0.84 K dec−1 featuring Arctic winter warming, these snow property changes have led to an upward trend in soil surface temperature (Tss) at a rate of +0.41 K dec−1 in winter. We show that the implemented Snowpack property changes increased the Tss trend by 36% compared to the standard simulation. Winter induced changes in Tss led to a significant increase of 16% (+4 cm dec−1) in the estimated active layer thickness (ALT) over the past 39 years. An increase in ALT could have a significant impact on permafrost evolution, Arctic erosion and hydrology
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arctic and subarctic snow microstructure analysis for microwave brightness temperature simulations
Remote Sensing of Environment, 2020Co-Authors: Alexandre Roy, Alain Royer, Ghislain Picard, Celine Vargel, Olivier Stjeanrondeau, Vincent Sasseville, Alexandre LangloisAbstract:Abstract Passive microwave (PMW) remote sensing has proven to be a useful approach to characterize the volume of seasonal Snowpack in remote northern regions at the synoptic scale. Modeling emitted microwave brightness temperatures (TB) is made possible using a physical radiative transfer model that takes into account microstructural and stratigraphic structure of the Snowpack. However, prescribing the microstructure remains a difficult task. This paper aims to find proper microstructure parametrization and the snow emission model formulation that best optimize TB simulations for Arctic and Subarctic Snowpacks. Surfaced-based radiometric measurements in conjunction with in-situ Snowpack characterization were used for testing different configurations based on the Snow Microwave Radiative Transfer model (SMRT), with two electromagnetic models (Dense Media Radiative Transfer Quasi Crystalline Approximation, DMRT, and Improved Born Approximation, IBA) and two microstructure description theories (Sticky Hard Sphere, SHS, and Exponential, Exp). We compare the performance of three configurations (DMRT-SHS, IBA-SHS and IBA-Exp) with a unique large dataset (119 snowpits with concomitant microwave ground-based radiometer observations) covering a wide range of Arctic and Subarctic snow types in Northern and Eastern Canada. Results show that the input measured microstructure parameters must be scaled up in order to better match simulated and observed TB at 11, 19, 37 and 89 GHz. We show that the IBA-Exp gives the best results, with a Root-Mean-Square Error (RMSE) lower by up to 30% for Subarctic snow and 24% for Arctic snow compare to the other model configurations we used. In addition, we undertake a complementary experiment on isolated homogeneous snow slabs to investigate the sensitivity of the scaling factor to snow microstructure. The retrieved microwave correlation length appears significantly different than the in-situ Debye correlation length. At high frequencies, the observed variability of these scaling factors with frequency and Snowpack types means that density, SSA and estimated correlation length seem insufficient to appropriately fully characterize snow microstructure for microwave modeling purposes.
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Snow Microwave Emission Modeling of Ice Lenses Within a Snowpack Using the Microwave Emission Model for Layered Snowpacks
IEEE Transactions on Geoscience and Remote Sensing, 2013Co-Authors: Benoit Montpetit, Alexandre Roy, Alain Royer, Alexandre Langlois, Chris DerksenAbstract: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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simulation of the microwave emission of multi layered Snowpacks using the dense media radiative transfer theory the dmrt ml model
Geoscientific Model Development, 2012Co-Authors: Ghislain Picard, Alain Royer, Ludovic Brucker, Florent Dupont, Michel Fily, C HarlowAbstract:Abstract. DMRT-ML is a physically based numerical model designed to compute the thermal microwave emission of a given Snowpack. Its main application is the simulation of brightness temperatures at frequencies in the range 1–200 GHz similar to those acquired routinely by space-based microwave radiometers. The model is based on the Dense Media Radiative Transfer (DMRT) theory for the computation of the snow scattering and extinction coefficients and on the Discrete Ordinate Method (DISORT) to numerically solve the radiative transfer equation. The Snowpack is modeled as a stack of multiple horizontal snow layers and an optional underlying interface representing the soil or the bottom ice. The model handles both dry and wet snow conditions. Such a general design allows the model to account for a wide range of snow conditions. Hitherto, the model has been used to simulate the thermal emission of the deep firn on ice sheets, shallow Snowpacks overlying soil in Arctic and Alpine regions, and overlying ice on the large ice-sheet margins and glaciers. DMRT-ML has thus been validated in three very different conditions: Antarctica, Barnes Ice Cap (Canada) and Canadian tundra. It has been recently used in conjunction with inverse methods to retrieve snow grain size from remote sensing data. The model is written in Fortran90 and available to the snow remote sensing community as an open-source software. A convenient user interface is provided in Python.
Alexandre Girard - One of the best experts on this subject based on the ideXlab platform.
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1d var multilayer assimilation of x band sar data into a detailed Snowpack model
The Cryosphere, 2014Co-Authors: Xuanvu Phan, Laurent Ferrofamil, Marie Dumont, Y Durand, S Morin, Sophie Allain, Guy Durso, Alexandre GirardAbstract:The structure and physical properties of a snow- pack and their temporal evolution may be simulated using meteorological data and a snow metamorphism model. Such an approach may meet limitations related to potential diver- gences and accumulated errors, to a limited spatial resolu- tion, to wind or topography-induced local modulations of the physical properties of a snow cover, etc. Exogenous data are then required in order to constrain the simulator and im- prove its performance over time. Synthetic-aperture radars (SARs) and, in particular, recent sensors provide reflectivity maps of snow-covered environments with high temporal and spatial resolutions. The radiometric properties of a Snowpack measured at sufficiently high carrier frequencies are known to be tightly related to some of its main physical parame- ters, like its depth, snow grain size and density. SAR acqui- sitions may then be used, together with an electromagnetic backscattering model (EBM) able to simulate the reflectiv- ity of a Snowpack from a set of physical descriptors, in or- der to constrain a physical Snowpack model. In this study, we introduce a variational data assimilation scheme coupling TerraSAR-X radiometric data into the Snowpack evolution model Crocus. The physical properties of a Snowpack, such as snow density and optical diameter of each layer, are simu- lated by Crocus, fed by the local reanalysis of meteorological data (SAFRAN) at a French Alpine location. These snow- pack properties are used as inputs of an EBM based on dense media radiative transfer (DMRT) theory, which simulates the total backscattering coefficient of a dry snow medium at X and higher frequency bands. After evaluating the sensi- tivity of the EBM to Snowpack parameters, a 1D-Var data assimilation scheme is implemented in order to minimize the discrepancies between EBM simulations and observa- tions obtained from TerraSAR-X acquisitions by modifying the physical parameters of the Crocus-simulated Snowpack. The algorithm then re-initializes Crocus with the modified Snowpack physical parameters, allowing it to continue the simulation of Snowpack evolution, with adjustments based on remote sensing information. This method is evaluated us- ing multi-temporal TerraSAR-X images acquired over the specific site of the Argentiere glacier (Mont-Blanc massif, French Alps) to constrain the evolution of Crocus. Results indicate that X-band SAR data can be taken into account to modify the evolution of Snowpack simulated by Crocus.
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1d var multilayer assimilation of x band sar data into a detailed Snowpack model
The Cryosphere, 2014Co-Authors: Xuanvu Phan, Laurent Ferrofamil, Marie Dumont, Y Durand, S Morin, Sophie Allain, Guy Durso, Alexandre GirardAbstract:The structure and physical properties of a snow- pack and their temporal evolution may be simulated using meteorological data and a snow metamorphism model. Such an approach may meet limitations related to potential diver- gences and accumulated errors, to a limited spatial resolu- tion, to wind or topography-induced local modulations of the physical properties of a snow cover, etc. Exogenous data are then required in order to constrain the simulator and im- prove its performance over time. Synthetic-aperture radars (SARs) and, in particular, recent sensors provide reflectivity maps of snow-covered environments with high temporal and spatial resolutions. The radiometric properties of a Snowpack measured at sufficiently high carrier frequencies are known to be tightly related to some of its main physical parame- ters, like its depth, snow grain size and density. SAR acqui- sitions may then be used, together with an electromagnetic backscattering model (EBM) able to simulate the reflectiv- ity of a Snowpack from a set of physical descriptors, in or- der to constrain a physical Snowpack model. In this study, we introduce a variational data assimilation scheme coupling TerraSAR-X radiometric data into the Snowpack evolution model Crocus. The physical properties of a Snowpack, such as snow density and optical diameter of each layer, are simu- lated by Crocus, fed by the local reanalysis of meteorological data (SAFRAN) at a French Alpine location. These snow- pack properties are used as inputs of an EBM based on dense media radiative transfer (DMRT) theory, which simulates the total backscattering coefficient of a dry snow medium at X and higher frequency bands. After evaluating the sensi- tivity of the EBM to Snowpack parameters, a 1D-Var data assimilation scheme is implemented in order to minimize the discrepancies between EBM simulations and observa- tions obtained from TerraSAR-X acquisitions by modifying the physical parameters of the Crocus-simulated Snowpack. The algorithm then re-initializes Crocus with the modified Snowpack physical parameters, allowing it to continue the simulation of Snowpack evolution, with adjustments based on remote sensing information. This method is evaluated us- ing multi-temporal TerraSAR-X images acquired over the specific site of the Argentiere glacier (Mont-Blanc massif, French Alps) to constrain the evolution of Crocus. Results indicate that X-band SAR data can be taken into account to modify the evolution of Snowpack simulated by Crocus.
