Dense media radiative transfer theory based on quasicrystalline approximation with applications to passive microwave remote sensing of snow

Tsang, Leung; Chen, Chi-Te; Chang, A. T. C.; Guo, Junhong; Ding, Kung‐Hau

doi:10.1029/1999rs002270

Cited by 215 publications

(138 citation statements)

References 12 publications

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“…The HUT model (Lemmetyinen et al, 2010) is a semiempirical model based on strong forward scattering assumptions, the MEMLS model ) is of intermediate complexity and contains the improved Born approximation (Mätzler, 1998), and the DMRT-ML model is the most physically complex and is based on quasi-crystalline approximation with coherent potential (QCA-CP). Many other microwave emission models have been developed, such as Mie scattering approach of Boyarskii and Tikhonov (2000), Chang et al (1976), and Eom et al (1983), strong fluctuation theory (Stogryn, 1986;Song and Zhang, 2007), distorted Born approximation (Tsang et al, 2000), the quasi-crystalline approximation (Grody, 2008), other QCA-CP models (Rosenfeld and Grody, 2000;Jin, 1997), or the numerical method of Maxwell's equations in 3-D (Xu et al, 2012). These references are not exhaustive but do give an illustration of the range of models available.…”

Section: Microwave Emission Modelsmentioning

confidence: 99%

Microstructure representation of snow in coupled snowpack and microwave emission models

Sandells¹,

Essery

Rutter

et al. 2017

The Cryosphere

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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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Section: Microwave Emission Modelsmentioning

confidence: 99%

Microstructure representation of snow in coupled snowpack and microwave emission models

Sandells¹,

Essery

Rutter

et al. 2017

The Cryosphere

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“…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%

“…To our knowledge, this approximation is not implemented in any available model. To additionally relax constraints on grain size, the DMRT-QCA Mie formulation is needed (Tsang et al, 2000a), allowing simulations at frequencies higher than 37-89 GHz. DMRT-QMS is the only model to implement this advanced assumption.…”

Section: Introductionmentioning

confidence: 99%

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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“…Purely theoretical models for snow emission have also been developed (e.g. Tsang et al, 2000). However, these models tend to be very complex and due to the diversity of ancillary information required, their use in practical SWE retrieval from satellite observations is limited.…”

Section: Introductionmentioning

confidence: 99%

Arctic Snow Microstructure Experiment for the development of snow emission modelling

Maslanka

Leppänen

Kontu

et al. 2016

Geosci. Instrum. Method. Data Syst.

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Abstract. The Arctic Snow Microstructure Experiment (ASMEx) took place in Sodankylä, Finland in the winters of 2013-2014 and 2014-2015. Radiometric, macro-, and microstructure measurements were made under different experimental conditions of homogenous snow slabs, extracted from the natural seasonal taiga snowpack. Traditional and modern measurement techniques were used for snow macroand microstructure observations. Radiometric measurements of the microwave emission of snow on reflector and absorber bases were made at frequencies 18.7, 21.0, 36.5, 89.0, and 150.0 GHz, for both horizontal and vertical polarizations. Two measurement configurations were used for radiometric measurements: a reflecting surface and an absorbing base beneath the snow slabs. Simulations of brightness temperatures using two microwave emission models, the Helsinki University of Technology (HUT) snow emission model and Microwave Emission Model of Layered Snowpacks (MEMLS), were compared to observed brightness temperatures. RMSE and bias were calculated; with the RMSE and bias values being smallest upon an absorbing base at vertical polarization. Simulations overestimated the brightness temperatures on absorbing base cases at horizontal polarization. With the other experimental conditions, the biases were small, with the exception of the HUT model 36.5 GHz simulation, which produced an underestimation for the reflector base cases. This experiment provides a solid framework for future research on the extinction of microwave radiation inside snow.

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Dense media radiative transfer theory based on quasicrystalline approximation with applications to passive microwave remote sensing of snow

Cited by 215 publications

References 12 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)

Arctic Snow Microstructure Experiment for the development of snow emission modelling

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