Microwave scattering coefficient of snow in MEMLS and DMRT-ML revisited: the relevance of sticky hard spheres and tomography-based estimates of stickiness

Löwe, Henning; Picard, Ghislain

doi:10.5194/tc-9-2101-2015

Cited by 51 publications

(37 citation statements)

References 50 publications

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“…This implies that the present dielectric model and the thermal conductivity model from Löwe et al (2013) are based on exactly the same microstructural parameters. In light of recent attempts to unify microstructural descriptions of snow for microwave modeling (Löwe and Picard, 2015), we also note that the MG formula (Eq. 6a) can be likewise obtained as the lowfrequency limit of the quasi-crystalline approximation for aligned spheroids (Ao and Kong, 2002).…”

Section: Series Expansion Of Permittivity Via Correlation Functions: mentioning

confidence: 99%

Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series

Leinß

Löwe²,

Proksch³

et al. 2016

The Cryosphere

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Abstract. The snow microstructure, i.e., the spatial distribution of ice and pores, generally shows an anisotropy which is driven by gravity and temperature gradients and commonly determined from stereology or computer tomography. This structural anisotropy induces anisotropic mechanical, thermal, and dielectric properties. We present a method based on radio-wave birefringence to determine the depth-averaged, dielectric anisotropy of seasonal snow with radar instruments from space, air, or ground. For known snow depth and density, the birefringence allows determination of the dielectric anisotropy by measuring the copolar phase difference (CPD) between linearly polarized microwaves propagating obliquely through the snowpack. The dielectric and structural anisotropy are linked by MaxwellGarnett-type mixing formulas. The anisotropy evolution of a natural snowpack in Northern Finland was observed over four winters (2009)(2010)(2011)(2012)(2013) with the ground-based radar instrument "SnowScat". The radar measurements indicate horizontal structures for fresh snow and vertical structures in old snow which is confirmed by computer tomographic in situ measurements. The temporal evolution of the CPD agreed in ground-based data compared to space-borne measurements from the satellite TerraSAR-X. The presented dataset provides a valuable basis for the development of new snow metamorphism models which include the anisotropy of the snow microstructure.

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Section: Series Expansion Of Permittivity Via Correlation Functions: mentioning

confidence: 99%

Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series

Leinß

Löwe²,

Proksch³

et al. 2016

The Cryosphere

View full text Add to dashboard Cite

show abstract

“…Two constant values were considered here: extremely cohesive or less sticky particles. Löwe and Picard (2015) have made progress in understanding stickiness from micro-CT data. There are theoretical limits, based on snow density (Löwe and Picard, 2015, Eqs.…”

Section: Discussionmentioning

confidence: 99%

“…Whilst Löwe and Picard (2015) have shown that it may be possible to determine stickiness from micro-CT measurements of the snow, an appropriate value of stickiness is not known for the field observations used in this paper. Roy et al (2013) and Löwe and Picard (2015) showed that non-sticky representation in DMRT-ML is inappropriate.…”

Section: Dmrt-mlmentioning

confidence: 99%

“…Roy et al (2013) and Löwe and Picard (2015) showed that non-sticky representation in DMRT-ML is inappropriate. For this model ensemble, two DMRT-ML configurations have been chosen to capture the range of brightness temperatures simulated: "DMRT less sticky" (τ = 0.2) and "DMRT very sticky" (τ = 0.1).…”

Section: Dmrt-mlmentioning

confidence: 99%

“…Theoretical differences between specific electromagnetic models have been examined in Löwe and Picard (2015), Pan et al (2015), and other intercomparisons carried out by . These studies are useful for interpreting differences in electromagnetic model outputs for a snapshot profile of the snowpack properties.…”

Section: Introductionmentioning

confidence: 99%

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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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Snow particles extracted from X-ray computed microtomography imagery and their single-scattering properties

Ishimoto

Adachi

Yamaguchi

et al. 2018

Journal of Quantitative Spectroscopy and Radiative Transfer

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Microwave scattering coefficient of snow in MEMLS and DMRT-ML revisited: the relevance of sticky hard spheres and tomography-based estimates of stickiness

Cited by 51 publications

References 50 publications

Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series

Anisotropy of seasonal snow measured by polarimetric phase differences in radar time series

Microstructure representation of snow in coupled snowpack and microwave emission models

Snow particles extracted from X-ray computed microtomography imagery and their single-scattering properties

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