2828 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 49, NO. 8, AUGUST 2011 A Case Study of Using a Multilayered Thermodynamical Snow Model for Radiance Assimilation Ally M. Toure, Kalifa Goïta, Alain Royer, Edward J. Kim, Michael Durand, Steven A. Margulis, and Huizhong Lu Abstract—A microwave radiance assimilation (RA) scheme for the retrieval of snow physical state variables requires a snow- pack physical model (SM) coupled to a radiative transfer model. In order to assimilate microwave brightness temperatures (Tbs) at horizontal polarization (h-pol), an SM capable of resolving melt–refreeze crusts is required. To date, it has not been shown whether an RA scheme is tractable with the large number of state variables present in such an SM or whether melt-refreeze crust densities can be estimated. In this paper, an RA scheme is presented using the CROCUS SM which is capable of resolving melt-refreeze crusts. We assimilated both vertical (v) and hori- zontal (h) Tbs at 18.7 and 36.5 GHz. We found that assimilating Tb at both h-pol and vertical polarization (v-pol) into CROCUS dramatically improved snow depth estimates, with a bias of 1.4 cm compared to -7.3 cm reported by previous studies. Assimilation of both h-pol and v-pol led to more accurate results than assimilation of v-pol alone. The snow water equivalent (SWE) bias of the RA scheme was 0.4 cm, while the bias of the SWE estimated by an empirical retrieval algorithm was -2.9 cm. Characterization of melt-refreeze crusts via an RA scheme is demonstrated here for the first time; the RA scheme correctly identified the location of melt-refreeze crusts observed in situ. Index Terms—Assimilation, melt–refreeze crusts, radiance, snow, snowpack model (SM). I. I NTRODUCTION S EASONAL snow cover has a strong impact on cli- mate, hydrological processes, and on human activities [1]. Snow is the frozen storage term in the water balance and is also a valuable resource. Monitoring snow physical variables Manuscript received June 14, 2010; revised November 3, 2010 and January 22, 2011; accepted January 30, 2011. Date of publication April 29, 2011; date of current version July 22, 2011. This work was supported in part by the National Science and Engineering Research Council of Canada, by Environment Canada (Cryosphere System in Canada Program), and by the National Aeronautics and Space Administration Terrestrial Hydrology Program. A. M. Toure and E. J. Kim are with the National Aeronautics and Space Ad- ministration Goddard Space Flight Center, Global Modeling and Assimilation Office Code 610.1, Greenbelt, MD 20771 USA (e-mail: ally.toure@nasa.gov; edward.j.kim@nasa.gov). K. Goïta, A. Royer, and H. Lu are with Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada (e-mail: kalifa.goita@usherbrooke.ca; Alain.Royer@USherbrooke.ca; huizhong.lu@usherbrooke.ca). M. Durand is with the School of Earth Sciences, The Ohio State University, Columbus, OH 43210 USA (e-mail: durand.8@osu.edu). S. A. Margulis is with the Department of Civil and Environmental En- gineering, University of California, Los Angeles, CA 90095 USA (e-mail: margulis@seas.ucla.edu). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TGRS.2011.2118761 (extent, water equivalent, and melting conditions) is essential for weather and hydrological forecasts in many regions and pro- vides information for meteorological forecasts, hydroelectric power generation, fresh water supply, river traffic, irrigation, runoff, and flood control [2]. Passive microwave-based estimates of terrestrial snow vari- ables such as snow water equivalent (SWE) from satel- lite observations have so far been generated primarily by regression-based empirical methods. Snow variables retrieved by such methods are sometimes used in product-based data assimilation [3]–[5]. With this type of approach, the retrieval and the forward data assimilation do not necessarily have con- sistent physics or assumptions. Consistency is much easier to achieve when radiances are directly assimilated. This radiance assimilation (RA) approach has been used for years to retrieve atmospheric parameters by the operational weather forecasting community with great success [6]. This approach may also be an effective way to retrieve snow physical parameters such as snow depth and its water equivalent [7]. Snow RA requires accurate predictions of the brightness temperature (Tb) emitted by the snowpack. This imposes re- quirements on the key elements of the snow RA scheme: a snowpack model (SM), a radiative transfer model (RTM), and a data assimilation framework. In a previous study, Durand et al. [8] demonstrated that the microwave emission model for lay- ered snowpacks (MEMLS) [9] has the ability to accurately predict the radiance when the snow layering structure and other state variables, such as grain-size correlation length, density, snow depth, liquid water, and temperature, are accurately rep- resented. They also showed that the accuracy of RTMs particu- larly at horizontal polarization (h-pol) is highly sensitive to the stratigraphic representation of the snowpack and particularly to dense snow layers, such as melt–refreeze crusts. Durand et al. [10] subsequently demonstrated the potential of snow RA using the simple snow-atmosphere-soil transfer model (SAST) [11] SM coupled to MEMLS. SAST is a simple three-layer energy-balance scheme. An improvement of the simulation of snow depth was achieved through the use of an ensemble Kalman smoother scheme at this local scale for a weeklong study [10]. However, limitations of SAST (three lay- ers and no representation of melt-refreeze crusts) were evident. Indeed, only Tbs at vertical polarization (v-pol) could be assim- ilated due to the fact that SAST does not adequately represent the vertical stratigraphy of the snowpack. This is inherently suboptimal, as only half of the available information could be assimilated; the h-pol channels were ignored. Assimilation of 0196-2892/$26.00 © 2011 IEEE