Articles | Volume 6, issue 4
Geosci. Model Dev., 6, 1061–1078, 2013
Geosci. Model Dev., 6, 1061–1078, 2013

Model description paper 26 Jul 2013

Model description paper | 26 Jul 2013

Simulation of the microwave emission of multi-layered snowpacks using the Dense Media Radiative transfer theory: the DMRT-ML model

G. Picard1,2, L. Brucker3,4, A. Roy5, F. Dupont1,2,5, M. Fily1,2, A. Royer5, and C. Harlow6 G. Picard et al.
  • 1CNRS, LGGE UMR5183, 38041 Grenoble, France
  • 2Univ. Grenoble Alpes, LGGE (UMR5183), 38041 Grenoble, France
  • 3NASA Goddard Space Flight Center, Cryospheric Sciences Lab., code 615 Greenbelt, MD, 20771 USA
  • 4Goddard Earth Sciences Technology and Research Studies and Investigations, Universities Space Research Association, Greenbelt, MD, 20771 USA
  • 5Centre d'applications et de recherches en télédétection (CARTEL), Université de Sherbrooke, 2500 Bd Université, Sherbrooke, QC J1K 2R1 Canada
  • 6Met Office, EX1 3PB Exeter, UK

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.