Simulation of the microwave emission of multi-layered snowpacks using the dense media radiative transfer theory: the DMRT-ML model

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 user 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

Snow specific surface area simulation using the one-layer snow model in the Canadian LAnd Surface Scheme (CLASS)

“…Moreover, the density stratification is another parameter that should be considered when modeling the radiative transfer within the snowpack (e.g., Picard et al, 2012). In our case, the densification routine in CLASS-SSA is only used to calculate the depth and the position of every snow layer, and not necessarily to calculate a precise density.…”

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

“…For larger grains (>1.6 mm) where QCA-CP is not applicable for Mie scattering, an empirical approach [24] is implemented in DMRT-ML imposing a limit for scattering efficiency. However, using simulated characteristics in this study, QCA-CP is valid for non-sticky grains assumption (see Figure 6 "Range of grain sizes for which the DMRT QCA-CP non-sticky is reasonably valid" in [16]). In our simulations, it is assumed that soil layer's contribution in 37 GHz emission is minimal and snow extinction is dominant which occurs when there are numerous snow grains through emission (thick or very dense snowpack) and/or snow grains are quite large.…”

“…The Dense Media Radiative Transfer Model-Multi-Layer (DMRT-ML) is a physically-based model that couples the microstructural properties of a snowpack to its emission signature. DMRT-ML has been employed in several studies using ground-based radiometers and in situ snow measurements [16,17]. The Dense Media Radiative Transfer (DMRT) model [18] and DMRT-ML have also been employed and evaluated for SWE retrievals using spaceborne radiometry (e.g., [19,20]).…”

“…Picard et al [16] employed Quasi Crystalline Approximation-Coherent Potential (QCA-CP) to model emission for a multi-layered snowpack in a range of 1-200 GHz. QCA and QCA-CP approximations let us model particle positions pair distribution functions while coherent wave interactions are assumed.…”

“…In the implementation of DMRT-ML, theoretical simulations of extinction and scattering coefficients are calculated based on the QCA-CP theory that can only solve small particle interactions with electromagnetic radiation with respect to Rayleigh scattering [16]. For larger grains (>1.6 mm) where QCA-CP is not applicable for Mie scattering, an empirical approach [24] is implemented in DMRT-ML imposing a limit for scattering efficiency.…”