Simulation of the microwave emission of multi-layered snowpacks using the Dense Media Radiative transfer theory: the DMRT-ML model

Picard, Ghislain; Brucker, Ludovic; Roy, Alexandre; Dupont, Florent; Fily, Michel; Royer, A.; Harlow, Chawn

doi:10.5194/gmd-6-1061-2013

Cited by 121 publications

(109 citation statements)

References 90 publications

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“…An important consequence was that we did not need the optimization of a second parameter to represent the mean grain size below the measured profile as used in B11; (3) the snow layers containing more ice than air per volume (i.e., density was larger than 458 kg m −3 ) were represented by air spheres embedded in an ice background instead of ice spheres in an air background. It yielded a better representation of the medium (Dierking et al, 2012;Picard et al, 2013;Dupont et al, 2014) and extended the validity of the DMRT theory towards large density values.…”

Section: Dmrt-ml Modelingmentioning

confidence: 70%

“…The model is described in detail in Picard et al (2013) and is available online (http://lgge.osug.fr/~picard/dmrtml/). Each snow layer is fully described by the temperature, density, the radius of the ice spheres representing the snow, and by the amount of liquid water.…”

Section: Dmrt-ml Modelingmentioning

confidence: 99%

“…According to electromagnetic theories (DMRT, Tsang and Kong, 2001, and an improved born approximation, Mät-zler, 1998), a larger density leads to larger absorption and to weaker scattering in snow when the density is in the range of 250-500 kg m −3 ( Fig. 7; Picard et al, 2013). Both effects combined decrease the single scattering albedo and increase the brightness temperature.…”

Section: Relationship Between Brightness Temperature and Snow Propertiesmentioning

confidence: 99%

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

et al. 2014

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Abstract. Space-borne passive microwave radiometers are widely used to retrieve information in snowy regions by exploiting the high sensitivity of microwave emission to snow properties. For the Antarctic Plateau, many studies presenting retrieval algorithms or numerical simulations have assumed, explicitly or not, that the subpixel-scale heterogeneity is negligible and that the retrieved properties were representative of whole pixels. In this paper, we investigate the spatial variations of brightness temperature over a range of a few kilometers in the Dome C area. Using ground-based radiometers towed by a vehicle, we collected brightness temperature at 11, 19 and 37 GHz at horizontal and vertical polarizations along transects with meter resolution. The most remarkable observation was a series of regular undulations of the signal with a significant amplitude reaching 10 K at 37 GHz and a quasi-period of 30-50 m. In contrast, the variability at longer length scales seemed to be weak in the investigated area, and the mean brightness temperature was close to SSM/I and WindSat satellite observations for all the frequencies and polarizations. To establish a link between the snow characteristics and the microwave emission undulations, we collected detailed snow grain size and density profiles at two points where opposite extrema of brightness temperature were observed. Numerical simulations with the DMRT-ML microwave emission model revealed that the difference in density in the upper first meter explained most of the brightness temperature variations. In addition, we found that these variations of density near the surface were linked to snow hardness. Patches of hard snow -probably formed by wind compaction -were clearly visible and covered as much as 39 % of the investigated area. Their brightness temperature was higher than in normal areas. This result implies that the microwave emission measured by satellites over Dome C is more complex than expected and very likely depends on the year-to-year areal proportion of the two different types of snow.

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Section: Dmrt-ml Modelingmentioning

confidence: 70%

Section: Dmrt-ml Modelingmentioning

confidence: 99%

Section: Relationship Between Brightness Temperature and Snow Propertiesmentioning

confidence: 99%

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

et al. 2014

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“…In HUT, the derived coefficient is κ s , whereas κ e is derived in MEMLS and κ a in DMRT-ML. Other differences between models include the representation of the phase function (single-stream model with separate up-and downwelling components in HUT, six-stream in MEMLS and multiple streams in DMRT-ML), specification of the absorption coefficient and the numerical techniques applied to solve the radiative transfer equation (Lemmetyinen et al, 2010;Picard et al, 2013;Mätzler and Wiesmann, 1999;Pan et al, 2015). Differences between models are not restated here, but options chosen within each model leading to different model versions are stated in the following subsections.…”

Section: Microwave Emission Modelsmentioning

confidence: 99%

“…The Jules Investigation Model (JIM; Essery et al, 2013) has been coupled with three widely used microwave emission models: the Dense Media Radiative Transfer Multi-Layer model (DMRT-ML; Picard et al, 2013), the Microwave Emission Model of Multi-Layer Snow (MEMLS; , and the Helsinki University of Technology (HUT) multi-layer model (Lemmetyinen et al, 2010;Pulliainen et al, 1999). Snowpack microstructure metamorphism is represented here by three different options with differing complexity for grain diameter evolution (or equivalently the specific surface area, SSA).…”

Section: Introductionmentioning

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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Density, specific surface area, and correlation length of snow measured by high‐resolution penetrometry

Proksch

Löwe²,

Schneebeli³

2015

JGR Earth Surface

141

166

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Key Points:• Statistical model relating micro-CT structure to SMP force for many snow data • New method to retrieve density, correlation length, and SSA in the field • Efficient retrieval of spatial variability and 2-D stratigraphy of snow Abstract Precise measurements of snow structural parameters are crucial to understand the formation of snowpacks by deposition and metamorphism and to characterize the stratigraphy for many applications and remote sensing in particular. The area-wide acquisition of structural parameters at high spatial resolution from state-of-the-art methods is, however, still cumbersome, since the time required for a single profile is a serious practical limitation. As a remedy we have developed a statistical model to extract three major snow structural parameters: density, correlation length, and specific surface area (SSA) solely from the SnowMicroPen (SMP), a high-resolution penetrometer, which allows a meter profile to be measured with millimeter resolution in less than 1 min. The model was calibrated by combining SMP data with 3-D microstructural data from microcomputed tomography which was used to reconstruct full-depth snow profiles from different snow climates (Alpine, Arctic, and Antarctic). Density, correlation length, and SSA were derived from the SMP with a mean relative error of 10.6%, 16.4%, and 23.1%, respectively. For validation, we compared the density and SSA derived from the SMP to traditional measurements and near-infrared profiles. We demonstrate the potential of our method by the retrieval of a two-dimensional stratigraphy at Kohnen Station, Antarctica, from a 46 m long SMP transect. The result clearly reveals past depositional and metamorphic events, and our findings show that the SMP can be used as an objective, high-resolution tool to retrieve essential snow structural parameters efficiently in the field.

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Simulation of the microwave emission of multi-layered snowpacks using the Dense Media Radiative transfer theory: the DMRT-ML model

Cited by 121 publications

References 90 publications

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

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

Microstructure representation of snow in coupled snowpack and microwave emission models

Density, specific surface area, and correlation length of snow measured by high‐resolution penetrometry

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