Abstract

Abstract. The structure and physical properties of a snowpack and their temporal evolution may be simulated using meteorological data and a snow metamorphism model. Such an approach may meet limitations related to potential divergences and accumulated errors, to a limited spatial resolution, 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 improve 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 parameters, like its depth, snow grain size and density. SAR acquisitions may then be used, together with an electromagnetic backscattering model (EBM) able to simulate the reflectivity of a snowpack from a set of physical descriptors, in order 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 simulated by Crocus, fed by the local reanalysis of meteorological data (SAFRAN) at a French Alpine location. These snowpack 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 sensitivity of the EBM to snowpack parameters, a 1D-Var data assimilation scheme is implemented in order to minimize the discrepancies between EBM simulations and observations 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 using multi-temporal TerraSAR-X images acquired over the specific site of the Argentière 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.

Highlights

  • Accurate knowledge of snowpack internal structure is critical for better understanding the snowpack evolution over time, and is essential for snow forecasting, water resource monitoring and prediction of natural hazards, such as avalanches

  • The strong fluctuation theory (SFT) has been tested and verified in the literature (Wang et al, 2000; Tsang et al, 2007). It is used in the dense media radiative transfer (DMRT) model of multilayer snowpack developed by Longepe et al (2009). This model is capable of simulating the interaction of electromagnetic waves with a layer of snow based on the physical parameters

  • This study reports, for the first time, on a new process based on the DMRT model and on the one-dimensional variational analysis (1D-Var) to assimilate TerraSAR-X data into the snow model Crocus

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Summary

Introduction

Accurate knowledge of snowpack internal structure is critical for better understanding the snowpack evolution over time, and is essential for snow forecasting, water resource monitoring and prediction of natural hazards, such as avalanches. It is used in the DMRT model of multilayer snowpack developed by Longepe et al (2009) This model is capable of simulating the interaction of electromagnetic waves with a layer of snow based on the physical parameters (thickness, optical diameter, snow density). The advantage of this model is the simple implementation and its moderate computation time, which is crucial in order to run the data assimilation process, where the electromagnetic model is repeatedly executed multiple times. This study reports, for the first time, on a new process based on the DMRT model and on the one-dimensional variational analysis (1D-Var) to assimilate TerraSAR-X data into the snow model Crocus.

Snowpack model Crocus
Main components of the total backscattering coefficient
Air–snow interface backscattering
Snow volume backscattering
Dry snow permittivity
Transmission between two layers
Attenuation
Scattering by the particles
Calculation of the volume backscattering
Sensitivity of the EBM to snowpack parameters
Introduction to data assimilation
Adjoint operator and minimization algorithm
Estimation of error covariance matrices
General comments on the chosen analysis process
Study site
Sensitivity of TerraSAR-X data
Simulation of Crocus snowpack data
Evaluation of the process and discussions
Conclusions
Full Text
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