Modeling time series of microwave brightness temperature at Dome C, Antarctica, using vertically resolved snow temperature and microstructure measurements

Abstract

International audienceTime series of observed microwave brightness temperatures at Dome C, East Antarctic plateau, were modeled over 27 months with a multilayer microwave emission model based on dense-medium radiative transfer theory. The modeled time series of brightness temperature at 18.7 and 36.5 GHz were compared with Advanced Microwave Scanning Radiometer-EOS observations. The model uses in situ high-resolution vertical profiles of temperature, snow density and grain size. The snow grain-size profile was derived from near-infrared (NIR) reflectance photography of a snow pit wall in the range 850-1100 nm. To establish the snow grain-size profile, from the NIR reflectance and the specific surface area of snow, two empirical relationships and a theoretical relationship were considered. In all cases, the modeled brightness temperatures were overestimated, and the grain-size profile had to be scaled to increase the scattering by snow grains. Using a scaling factor and a constant snow grain size below 3 m depth (i.e. below the image-derived snow pit grain-size profile), brightness temperatures were explained with a root-mean-square error close to 1 K. Most of this error is due to an overestimation of the predicted brightness temperature in summer at 36.5 GHz

Influence of meter-scale wind-formed features on the variability of the microwave brightness temperature around Dome C in Antarctica

“…The SSA value at 8 m depth was used for all the layers below this depth. This simplification had a limited impact on the calculated brightness temperature since the e-folding penetration depth was about 4 m at 19 GHz and 0.75 m at 37 GHz (Surdyk, 2002;Brucker et al, 2011). The corresponding profiles of SSA, density and temperature are presented in Fig.…”

“…1) using a string of 40 temperature probes set up in December 2006 from the surface to 20 m depth (Brucker et al, 2011). Daily measurements at 50 cm depth are shown in Fig.…”

“…The spatial variations of brightness temperature are generally continuous, except in coastal and mountainous regions. Snow properties retrieved from microwave data, like the accumulation (Vaughan et al, 1999;Arthern et al, 2006) or the grain size profile (Brucker et al, 2011), are also spatially continuous. This suggests that the snowpack from one pixel to a neighbor is generally similar.…”

“…However, all the applications cited above presuppose, explicitly or not, that the snow characteristics are spatially homogeneous within the radiometer footprint. This assumption is also required when emission models are run using snow properties measured in one point and results are compared to satellite data (Macelloni et al, 2006;Brucker et al, 2011). Advanced approaches for dealing with the subpixel heterogeneity, like unmixing (e.g., Painter et al, 2009), have not yet been considered in Antarctica for passive microwave remote sensing as opposed to other continental surfaces (Kerr et al, 2012).…”

SMRT: an active–passive microwave radiative transfer model for snow with multiple microstructure and scattering formulations (v1.0)

“…Even though many applications still rely on empirical approaches to relate snowpack properties (e.g., snow water equivalent, SWE) and measured signals, it is generally accepted that a physical understanding of the interaction between snow and electromagnetic waves is necessary to improve the accuracy and overcome inherent difficulties of the retrieval as an underdetermined problem. The retrieval of snow properties is therefore often preceded by forward modeling and data as-similation (Durand and Margulis, 2007;Picard et al, 2009;Takala et al, 2011;Toure et al, 2011;Huang et al, 2012) to predict the satellite signal from prescribed snowpack properties that can be either obtained from measurements (e.g., Rosenfeld and Grody, 2000;Brucker et al, 2011a;Rees et al, 2010;Derksen et al, 2012Derksen et al, , 2014Kontu et al, 2014) or snow models (e.g., Flach et al, 2005;Brucker et al, 2011b;Andreadis and Lettenmaier, 2012;Kang and Barros, 2012;Wójcik et al, 2008;Kontu et al, 2017). The actual modeling challenge lies in the snowpack and the underlying surface (soil, ice, or water) where the coupling of various ingredients needs to be understood with sufficient accuracy to build efficient forward models.…”