Snow stratigraphic heterogeneity within ground‐based passive microwave radiometer footprints: Implications for emission modeling

Abstract

Two-dimensional measurements of snowpack properties (stratigraphic layering, density, grain size, and temperature) were used as inputs to the multilayer Helsinki University of Technology (HUT) microwave emission model at a centimeter-scale horizontal resolution, across a 4.5 m transect of ground-based passive microwave radiometer footprints near Churchill, Manitoba, Canada. Snowpack stratigraphy was complex (between six and eight layers) with only three layers extending continuously throughout the length of the transect. Distributions of one-dimensional simulations, accurately representing complex stratigraphic layering, were evaluated using measured brightness temperatures. Large biases (36 to 68 K) between simulated and measured brightness temperatures were minimized (À0.5 to 0.6 K), within measurement accuracy, through application of grain scaling factors (2.6 to 5.3) at different combinations of frequencies, polarizations, and model extinction coefficients. Grain scaling factors compensated for uncertainty relating optical specific surface area to HUT effective grain size inputs and quantified relative differences in scattering and absorption properties of various extinction coefficients. The HUT model required accurate representation of ice lenses, particularly at horizontal polarization, and large grain scaling factors highlighted the need to consider microstructure beyond the size of individual grains. As variability of extinction coefficients was strongly influenced by the proportion of large (hoar) grains in a vertical profile, it is important to consider simulations from distributions of one-dimensional profiles rather than single profiles, especially in sub-Arctic snowpacks where stratigraphic variability can be high. Model sensitivity experiments suggested that the level of error in field measurements and the new methodological framework used to apply them in a snow emission model were satisfactory. Layer amalgamation showed that a three-layer representation of snowpack stratigraphy reduced the bias of a one-layer representation by about 50%.

Intercomparison of snow density measurements: bias, precision, and vertical resolution

“…Proksch et al (2015) demonstrated the use of the SMP to reveal spatial density variations in an Antarctic snow profile. Although spatially varying density is a known problem for a broad range of applications (e.g., Rutter et al, 2014), an intercomparison of the ability of different methods to resolve spatial density variations was beyond the scope of the study presented here.…”

Sodankylä manual snow survey program

“…Therefore, vertical changes of macrostructural parameters, such as temperature and density, are related to the evolution of snow microstructure. However, the structure of snowpack is very complex and spatial variations are large even on a small scale (Rutter et al, 2014;Derksen et al, 2009). Therefore, manual observations of snow macro-and microstructure have an important role in the monitoring of temporal evolution and spatial variations of snowpack.…”

Microstructure representation of snow in coupled snowpack and microwave emission models

“…SNTHERM simulates a grain size that is closer in concept to the visual estimates of grain diameter than the other two models. The large spread when coupling snowpack evolution and microwave models, due to the differences in the modelling of snow microstructure, is consistent with the wide range of studies that have investigated how to link snowpack observations of microstructure to the microstructure parameter required in electromagnetic models (e.g Kendra et al, 1998;Du et al, 2005;Liang et al, 2008;The Cryosphere, 11, 229-246, 2017 www.the-cryosphere.net/11/229/2017/ Durand et al, 2008;Brucker et al, 2011;Xu et al, 2012;Montpetit et al, 2013;Roy et al, 2013;Rutter et al, 2014;Picard et al, 2014). Nevertheless there are differences between microwave emission models for a particular microstructure evolution model and even differences within the same family of emission models.…”

“…These fall into different categories, depending on sensor characteristics, the source of the evaluation data (ground-based, airborne, satellite) and presence of ice lenses , the treatment of the snow microstructure (Picard et al, 2014), snow type, observation angle, and the specific electromagnetic model , and the underlying substrate (Lemmetyinen et al, 2009;Derksen et al, 2014). Examples of unscaled field observations of microstructure compared with ground-based observations include the HUT simulations of , who found an RMSE of 10-34 K, and Rutter et al (2014), who found a bias of 34-68 K that was reduced to < 0.6 K upon application of grain scale factors of 2.6-5.3. Scaling, or best-fit, relationships were used by Durand et al (2008) (mean absolute error 3.1 K at V-pol and 9.3 K at H-pol), Montpetit et al (2013) , Brucker et al (2011) (RMSE 1.5 K), Picard et al (2014) (RMSE 1-11 K), and Roy et al (2013) .…”

“…This is highlighted by the treatment of field observations of SSA to derive optical diameter as even these require some form of scaling (e.g. Montpetit et al, 2013;Picard et al, 2014;Rutter et al, 2014). Use of scale factors can improve brightness temperature accuracy at some frequency and polarisations but may decrease the accuracy at others.…”