Measuring the specific surface area of snow with X-ray tomography and gas adsorption: comparison and implications for surface smoothness

Kerbrat, M.; Pinzer, B; Huthwelker, Thomas; Gäggeler, H. W.; Ammann, Markus; Schneebeli, Martin

doi:10.5194/acp-8-1261-2008

Cited by 98 publications

(107 citation statements)

References 59 publications

Supporting

Mentioning

103

Contrasting

Order By: Relevance

“…estimated it at 10%, leading to an overall error of 12%. However, Kerbrat et al (2008) showed that the CH 4 adsorption method gave results within 3% of X-ray tomography, so that it is reasonable to suggest that the systematic error due to CH 4 adsorption is 5% or less. In that case, combining a random error of 8% and a systematic error of 5%, we conclude that the uncertainty of SSA determination using IR hemispherical reflectance at 1310 nm under the current conditions is 10%.…”

Section: Recommendation For Snow With Ssa<60 M 2 Kg −1mentioning

confidence: 99%

“…The smaller structures are easily perturbed by the liquid and by microtoming, and are often unresolved by optical photography. Lastly, X-ray tomography can also produce SSA values (Flin et al, 2003;Kerbrat et al, 2008;Kaempfer and Schneebeli, 2007) but this is not easy to use in the field and the resolution is currently insufficient if SSA>70 m 2 kg −1 (Kerbrat et al, 2008), a value frequently exceeded in fresh snow.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Measurement of the specific surface area of snow using infrared reflectance in an integrating sphere at 1310 and 1550 nm

et al. 2009

View full text Add to dashboard Cite

Abstract. Even though the specific surface area (SSA) and the snow area index (SAI) of snow are crucial variables to determine the chemical and climatic impact of the snow cover, few data are available on the subject. We propose here a novel method to measure snow SSA and SAI. It is based on the measurement of the hemispherical infrared reflectance of snow samples using the DUFISSS instrument (DUal Frequency Integrating Sphere for Snow SSA measurement). DUFISSS uses the 1310 or 1550 nm radiation of laser diodes, an integrating sphere 15 cm in diameter, and InGaAs photodiodes. For SSA<60 m 2 kg −1 , we use the 1310 nm radiation, reflectance is between 15 and 50% and the accuracy of SSA determination is 10%. For SSA>60 m 2 kg −1 , snow is usually of low density (typically 30 to 100 kg m −3 ), resulting in insufficient optical depth and 1310 nm radiation reaches the bottom of the sample, causing artifacts. The 1550 nm radiation is therefore used for SSA>60 m 2 kg −1 . Reflectance is then in the range 5 to 12% and the accuracy on SSA is 12%. We propose empirical equations to determine SSA from reflectance at both wavelengths, with that for 1310 nm taking into account the snow density. DUFISSS has been used to measure the SSA of snow and the SAI of snowpacks in polar and Alpine regions.

show abstract

Section: Recommendation For Snow With Ssa<60 M 2 Kg −1mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Measurement of the specific surface area of snow using infrared reflectance in an integrating sphere at 1310 and 1550 nm

et al. 2009

View full text Add to dashboard Cite

show abstract

“…[10] Specific surface area (SSA) [Kerbrat et al, 2008] is the ratio of ice surface area over ice volume (unit m À1 ). The surface area was extracted from the voxel data using a marching cubes algorithm [Lorensen and Cline, 1987].…”

Section: Specific Surface Areamentioning

confidence: 99%

Snow metamorphism under alternating temperature gradients: Morphology and recrystallization in surface snow

Pinzer¹,

Schneebeli²

2009

Geophysical Research Letters

Self Cite

100

112

View full text Add to dashboard Cite

[1] The composition of the lower atmospheric boundary layer can be strongly influenced by snow photochemistry. Surface snow, where air-snow interactions take place, is viewed as slowly changing quasi-isothermal snow, implied by the observation of mainly rounded snow crystals. The role of snow for photochemistry is therefore expected to be purely geometric. However, large temperature gradients are often observed in these layers, which would imply high recrystallization and faceting. In controlled laboratory experiments we showed that temperature gradients on the order of 100 K m À1 do not lead necessarily to faceting if their sign changes with a daily cycle. The shape of snow crystals does not reflect necessarily the metamorphic process. With time-lapse X-ray tomography, recrystallization rates as high as 60% of the total ice mass were observed during 12 hours. The high recrystallization rates in apparently isothermal snow not only contradict the current understanding of snow metamorphism, they also might influence chemical air-snow interactions. Citation: Pinzer, B. R., and M. Schneebeli (2009), Snow metamorphism under alternating temperature gradients: Morphology and recrystallization in surface snow, Geophys. Res. Lett., 36, L23503,

show abstract

“…In snow science, the most popular denoising filter is the Gaussian filter (e.g. Kerbrat et al, 2008;Lomonaco et al, 2011;Theile et al, 2009;Schleef and Löwe, 2013), which consists in convoluting the intensity field I (i.e. the greyscale value) with a Gaussian kernel of zero mean N (0, σ ) defined as…”

Section: Denoising With a Gaussian Filtermentioning

confidence: 99%

Sensitivity of snow density and specific surface area measured by microtomography to different image processing algorithms

et al. 2016

View full text Add to dashboard Cite

Abstract. Microtomography can measure the X-ray attenuation coefficient in a 3-D volume of snow with a spatial resolution of a few microns. In order to extract quantitative characteristics of the microstructure, such as the specific surface area (SSA), from these data, the greyscale image first needs to be segmented into a binary image of ice and air. Different numerical algorithms can then be used to compute the surface area of the binary image. In this paper, we report on the effect of commonly used segmentation and surface area computation techniques on the evaluation of density and specific surface area. The evaluation is based on a set of 38 X-ray tomographies of different snow samples without impregnation, scanned with an effective voxel size of 10 and 18 µm. We found that different surface area computation methods can induce relative variations up to 5 % in the density and SSA values. Regarding segmentation, similar results were obtained by sequential and energy-based approaches, provided the associated parameters were correctly chosen. The voxel size also appears to affect the values of density and SSA, but because images with the higher resolution also show the higher noise level, it was not possible to draw a definitive conclusion on this effect of resolution.

show abstract

Measuring the specific surface area of snow with X-ray tomography and gas adsorption: comparison and implications for surface smoothness

Cited by 98 publications

References 59 publications

Measurement of the specific surface area of snow using infrared reflectance in an integrating sphere at 1310 and 1550 nm

Measurement of the specific surface area of snow using infrared reflectance in an integrating sphere at 1310 and 1550 nm

Snow metamorphism under alternating temperature gradients: Morphology and recrystallization in surface snow

Sensitivity of snow density and specific surface area measured by microtomography to different image processing algorithms

Contact Info

Product

Resources

About