Snow grain-size profiles deduced from microwave snow emissivities in Antarctica

Brucker, Ludovic; Picard, Ghislain; Fily, Michel

doi:10.3189/002214310792447806

Cited by 67 publications

(53 citation statements)

References 101 publications

(140 reference statements)

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“…The rms errors reported in Table 2 show that the agreement is, in general, better by a factor of 2 for SP1 than SP2, and better by a factor of around 5 for the vertical than the horizontal polarization. However, the error is relatively independent of the frequency, which indicates that the vertical gradient of snow grain size and density is well captured by the measurements (Brucker et al, 2010).…”

Section: Relationship Between Brightness Temperature and Snow Propertiesmentioning

confidence: 91%

“…They have several advantages over other remote sensing techniques: high sensitivity to snow properties (temperature, grain size, density), subdaily coverage in the polar regions, and independence of cloud conditions and solar illumination. Typical applications for ice sheets aim to retrieve snow temperature (Shuman et al, 1995;Schneider and Steig, 2002;Schneider et al, 2004), snowmelt (e.g., Zwally, 1977;Abdalati and Steffen, 1995;Torinesi et al, 2003), snow accumulation (Vaughan et al, 1999;Arthern et al, 2006), grain size (Brucker et al, 2010;Picard et al, 2012), thermal properties (Koenig et al, 2007;Picard et al, 2009) or surface state (Shuman et al, 1993;Champollion et al, 2013). Passive microwave data are also widely used in assimilation schemes to constrain atmospheric analyses for which the surface emissivity is an issue, particularly over Antarctica (Guedj et al, 2010).…”

Section: Introductionmentioning

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: Relationship Between Brightness Temperature and Snow Propertiesmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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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“…For this, for each 10-day period from 1999 to present, the SSA in both layers is optimized so that the model predictions at 150 and 89 GHz match the satellite observations. To relate the SSA to the grain size metric r required by the DMRT theory, an empirical scaling coefficient is used according to Brucker et al (2010), such that SSA = 3/(ρ ice r 2.8 ). This method is simple because using two observations it considers only two unknowns, while the density and layer thickness are probably variable and are known to affect microwave signal as well (even if this effect is of second order compared to the SSA).…”

Section: Satellite Observationsmentioning

confidence: 99%

Summertime evolution of snow specific surface area close to the surface on the Antarctic Plateau

et al. 2015

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Abstract. On the Antarctic Plateau, snow specific surface area (SSA) close to the surface shows complex variations at daily to seasonal scales which affect the surface albedo and in turn the surface energy budget of the ice sheet. While snow metamorphism, precipitation and strong wind events are known to drive SSA variations, usually in opposite ways, their relative contributions remain unclear. Here, a comprehensive set of SSA observations at Dome C is analysed with respect to meteorological conditions to assess the respective roles of these factors. The results show an average 2-to-3-fold SSA decrease from October to February in the topmost 10 cm in response to the increase of air temperature and absorption of solar radiation in the snowpack during spring and summer. Surface SSA is also characterized by significant daily to weekly variations due to the deposition of small crystals with SSA up to 100 m 2 kg −1 onto the surface during snowfall and blowing snow events. To complement these field observations, the detailed snowpack model Crocus is used to simulate SSA, with the intent to further investigate the previously found correlation between interannual variability of summer SSA decrease and summer precipitation amount. To this end, some Crocus parameterizations have been adapted to Dome C conditions, and the model was forced by ERAInterim reanalysis. It successfully matches the observations at daily to seasonal timescales, except for the few cases when snowfalls are not captured by the reanalysis. On the contrary, the interannual variability of summer SSA decrease is poorly simulated when compared to 14 years of microwave satellite data sensitive to the near-surface SSA. A simulation with disabled summer precipitation confirms the weak influence in the model of the precipitation on metamorphism, with only 6 % enhancement. However, we found that disabling strong wind events in the model is sufficient to reconciliate the simulations with the observations. This suggests that Crocus reproduces well the contributions of metamorphism and precipitation on surface SSA, but snow compaction by the wind might be overestimated in the model.

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“…8c. For instance, Brucker et al (2010) have found the highest grain size vertical gradient in the regions of the WP zone.…”

Section: Discussionmentioning

confidence: 98%

Seasonal variations of the backscattering coefficient measured by radar altimeters over the Antarctica Ice Sheet

Adodo¹,

Rémy²,

Picard³

2017

Preprint

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Abstract. Spaceborne radar altimeter is a valuable tool for observing the Antarctica Ice Sheet. The radar wave penetration into the snow provides information both on the surface and the subsurface of the snowpack due to its dependence on the snow properties. However this penetration also induces a negative bias on the estimated surface elevation. Empirical corrections of this space and time-varying bias are usually based on the backscattering coefficient variability. We investigate the spatial and seasonal variations of the backscattering coefficient at the S (3.2 GHz), Ku (13.6 GHz) and Ka (37 GHz) bands. We identified two clearly marked zones over the continent, one with the maximum of Ku band backscattering coefficient in the winter and another with the maximum in the summer. To explain this, we performed a sensitivity study of the backscattering coefficient at the S, Ku and Ka bands to surface snow density, snow temperature and snow grain size using an electromagnetic model. The results show that the seasonal cycle of the backscattering coefficient at the Ka band, is dominated by the volume echo and is mainly explained by snow temperature. In contrast, the cycle is dominated by the surface echo at the S band. At Ku band, which intermediate in terms of wavelength between S and Ka bands, the seasonal cycle is in the first zone dominated by the volume echo and by the surface echo in the second one. Such seasonal and spatial variations of the backscattering coefficient at different radar frequencies should be taken into account the for more precise estimation of the surface elevation changes.

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Snow grain-size profiles deduced from microwave snow emissivities in Antarctica

Cited by 67 publications

References 101 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

Summertime evolution of snow specific surface area close to the surface on the Antarctic Plateau

Seasonal variations of the backscattering coefficient measured by radar altimeters over the Antarctica Ice Sheet

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