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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 spacebased 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 icesheet 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.

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In Québec, Eastern Canada, snowmelt runoff contributes more than 30% of 17 the annual energy reserve for hydroelectricity production, and uncertainties in annual 18 maximum snow water equivalent (SWE) over the region are one of the main 19 constraints for improved hydrological forecasting. Current satellite-based methods for 20 mapping SWE over Québec's main hydropower basins do not meet Hydro-Québec 21 operational requirements for SWE accuracies with less than 15% error. This paper 22 assesses the accuracy of the GlobSnow-2 (GS-2) SWE product, which combines 23 microwave satellite data and in situ measurements, for hydrological applications in 24Québec. GS-2 SWE values for a 30-year period (1980 to 2009) were compared with 25 space-and time-matched values from a comprehensive dataset of in situ SWE 26 measurements (a total of 38 990 observations in Eastern Canada). The root mean 27 square error (RMSE) of the GS-2 SWE product is 94.1 ± 20.3 mm, corresponding to an 28 overall relative percentage error (RPE) of 35.9%. The main sources of uncertainty are 29 wet and deep snow conditions (when SWE is higher than 150 mm), and forest cover 30 type. However, compared to a typical stand-alone brightness temperature channel 31 https://ntrs.nasa.gov/search.jsp?R=20170003585 2020-07-04T11:59:55+00:00ZRemote Sensing of Environment. 2016 2 of 32 difference algorithm, the assimilation of surface information in the GS-2 algorithm 32 clearly improves SWE accuracy by reducing the RPE by about 30%. Comparison of 33 trends in annual mean and maximum SWE between surface observations and GS-2 34 over 1980-2009 showed agreement for increasing trends over southern Québec, but 35 less agreement on the sign and magnitude of trends over northern Québec. Extended 36 at a continental scale, the GS-2 SWE trends highlight a strong regional variability. 37Keywords: GlobSnow-2, passive microwave, in situ SWE measurements, Eastern 38Canada, land cover, water resources. 39 40 41 (Brown and Tabsoba, 2007). Optimal management of the snowmelt contribution to 57 hydroelectric production requires accurate estimates of peak snow accumulation prior 58 to spring melt (Turcotte et al. 2010). This is one of the main challenges for hydrological 59 forecasting particularly over large remote watersheds. Current operational runoff 60 forecast systems typically rely on surface snow surveys to determine pre-melt SWE, 61 which can be supplemented with geostatistical interpolation procedures to provide a 62 more detailed estimate of the spatial pattern (e.g. Tapsoba et al 2005). 63However, manual snow surveys are time-consuming and expensive which make 64 SWE estimation from satellite passive microwave (PMW) sensors an attractive option. 65 PMW sensors also offer advantages of all weather and all year coverage at good 66 temporal (daily) and moderate spatial (~25 km) resolution. The basic physics behind 67 PMW SWE retrievals is that the natural emission measured by satellite-borne 68 microwave radiometers, expressed as brightness temperature (TB), is characterized ...

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ABSTRACT. Snow grain-size characterization, its vertical and temporal evolution is a key parameter for the improvement and validation of snow and radiative transfer models (optical and microwave) as well as for remote-sensing retrieval methods. We describe two optical methods, one active and one passive shortwave infrared, for field determination of the specific surface area (SSA) of snow grains. We present a new shortwave infrared (SWIR) camera approach. This new method is compared with a SWIR laserbased system measuring snow albedo with an integrating sphere (InfraRed Integrating Sphere (IRIS)). Good accuracy (10%) and reproducibility in SSA measurements are obtained using the IRIS system on snow samples having densities greater than 200 kg m -3 , validated against X-ray microtomography measurements. The SWIRcam approach shows improved sensitivity to snow SSA when compared to a near-infrared camera, giving a better contrast of the snow stratigraphy in a snow pit.