Modeling of Millimeter Wave Backscatter of Time-Varying Snowcover

Abstract. This is the first study to encompass a wide range of coupled snow evolution and microwave emission models in a common modelling framework in order to generalise the link between snowpack microstructure predicted by the snow evolution models and microstructure required to reproduce observations of brightness temperature as simulated by snow emission models. Brightness temperatures at 18.7 and 36.5 GHz were simulated by 1323 ensemble members, formed from 63 Jules Investigation Model snowpack simulations, three microstructure evolution functions, and seven microwave emission model configurations. Two years of meteorological data from the Sodankylä Arctic Research Centre, Finland, were used to drive the model over the 2011-2012 and 2012-2013 winter periods. Comparisons between simulated snow grain diameters and field measurements with an IceCube instrument showed that the evolution functions from SNTHERM simulated snow grain diameters that were too large (mean error 0.12 to 0.16 mm), whereas MOSES and SNICAR microstructure evolution functions simulated grain diameters that were too small (mean error −0.16 to −0.24 mm for MOSES and −0.14 to −0.18 mm for SNICAR). No model (HUT, MEMLS, or DMRT-ML) provided a consistently good fit across all frequencies and polarisations. The smallest absolute values of mean bias in brightness temperature over a season for a particular frequency and polarisation ranged from 0.7 to 6.9 K.Optimal scaling factors for the snow microstructure were presented to compare compatibility between snowpack model microstructure and emission model microstructure. Scale factors ranged between 0.3 for the SNTHERMempirical MEMLS model combination (2011)(2012) and 3.3 for DMRT-ML in conjunction with MOSES microstructure (2012)(2013). Differences in scale factors between microstructure models were generally greater than the differences between microwave emission models, suggesting that more accurate simulations in coupled snowpack-microwave model systems will be achieved primarily through improvements in the snowpack microstructure representation, followed by improvements in the emission models. Other snowpack parameterisations in the snowpack model, mainly densification, led to a mean brightness temperature difference of 11 K at 36.5 GHz H-pol and 18 K at V-pol when the Jules Investigation Model ensemble was applied to the MOSES microstructure and empirical MEMLS emission model for the 2011-2012 season. The impact of snowpack parameterisation increases as the microwave scattering increases. Consistency between snowpack microstructure and microwave emission models, and the choice of snowpack densification algorithms should be considered in the design of snow mass retrieval systems and microwave data assimilation systems.

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“…These two values represent reasonable values used by others (e.g. Tsang et al, 2007;Shih et al, 1997).…”

Section: Dmrt-mlsupporting

confidence: 73%

Microstructure representation of snow in coupled snowpack and microwave emission models

Sandells¹,

Essery

Rutter

et al. 2017

The Cryosphere

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“…The QCA-CP version is formulated according to Shih et al (1997). The first-order effective dielectric constant eff,0 is obtained by solving the following quadratic equation (Eq.…”

Section: Appendix B: Independent Rayleigh Scatterersmentioning

confidence: 99%

“…The first-order effective dielectric constant eff,0 is obtained by solving the following quadratic equation (Eq. 3 in Shih et al (1997) with a = 0 or Eq. 5.3.125 in Tsang and Kong, 2001):…”

Section: Appendix B: Independent Rayleigh Scatterersmentioning

confidence: 99%

“…Though insightful and necessary, these efforts did not lead to a reduction of the number of models as none of the studies considered the entirety of models and none showed a clear superiority of a single model. The latter fact was partly explained in Löwe and Picard (2015), who demonstrated the near equivalence of two approaches, namely improved Born approximation (IBA) (Mätzler, 1998) and DMRT (Tsang et al, 1985;Shih et al, 1997), which were previously considered to be different. This was achieved by relating the microstructural foundations of either approach, demonstrating the necessity to compare different microstructural formulations.…”

Section: Introductionmentioning

confidence: 99%

“…This makes formulations based on fundamental principles (Maxwell equations) attractive. For instance, the DMRT theory (Tsang et al, 1985(Tsang et al, , 2000aWest et al, 1993;Shih et al, 1997) is used by several models (e.g., DMRT-ML, DMRT-QMS, Longepe et al (2009), etc.). DMRT represents snow as a collection of ice spheres whose relative positions are constrained by the sticky hard sphere (SHS) model.…”

Section: Introductionmentioning

confidence: 99%

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

Picard

Sandells²,

Löwe³

2018

Geosci. Model Dev.

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Abstract. The Snow Microwave Radiative Transfer (SMRT) thermal emission and backscatter model was developed to determine uncertainties in forward modeling through intercomparison of different model ingredients. The model differs from established models by the high degree of flexibility in switching between different electromagnetic theories, representations of snow microstructure, and other modules involved in various calculation steps. SMRT v1.0 includes the dense media radiative transfer theory (DMRT), the improved Born approximation (IBA), and independent Rayleigh scatterers to compute the intrinsic electromagnetic properties of a snow layer. In the case of IBA, five different formulations of the autocorrelation function to describe the snow microstructure characteristics are available, including the sticky hard sphere model, for which close equivalence between the IBA and DMRT theories has been shown here. Validation is demonstrated against established theories and models. SMRT was used to identify that several former studies conducting simulations with in situ measured snow properties are now comparable and moreover appear to be quantitatively nearly equivalent. This study also proves that a third parameter is needed in addition to density and specific surface area to characterize the microstructure. The paper provides a comprehensive description of the mathematical basis of SMRT and its numerical implementation in Python. Modularity supports model extensions foreseen in future versions comprising other media (e.g., sea ice, frozen lakes), different scattering theories, rough surface models, or new microstructure models.

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Comparison of commonly-used microwave radiative transfer models for snow remote sensing

Royer

Roy

Montpetit

et al. 2017

Remote Sensing of Environment

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Modeling of Millimeter Wave Backscatter of Time-Varying Snowcover

Cited by 19 publications

References 35 publications

Microstructure representation of snow in coupled snowpack and microwave emission models

Microstructure representation of snow in coupled snowpack and microwave emission models

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

Comparison of commonly-used microwave radiative transfer models for snow remote sensing

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