First experiments to determine snow density from diffuse near-infrared transmittance
Snow density is one of the key properties to characterize a snow cover. We present diffuse near-infrared transmittance measurements with an integrating sphere setup in the laboratory. We analyze 8 snow samples taken from melt forms, decomposed, rounded, faceted and machine made snow. Reference measu...
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| Main Authors: | , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
23 June 2010
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| In: |
Cold regions science and technology
Year: 2010, Volume: 64, Issue: 2, Pages: 81-86 |
| DOI: | 10.1016/j.coldregions.2010.06.005 |
| Online Access: | lizenzpflichtig lizenzpflichtig 
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| Author Notes: | Mathias Gergely, Martin Schneebeli, Kurt Roth |
| Summary: | Snow density is one of the key properties to characterize a snow cover. We present diffuse near-infrared transmittance measurements with an integrating sphere setup in the laboratory. We analyze 8 snow samples taken from melt forms, decomposed, rounded, faceted and machine made snow. Reference measurements of specific surface area (optically equivalent grain size) and density are done by micro-computed tomography and used as input for transmittance calculations. A diffuse flux extinction model cannot be applied to simulate transmittance as our setup cannot be approximated by an infinite snow block thickness. Calculations with a more intricate radiative transfer model (DISORT) agree with our measurements within the estimated grain size and density variability for all probed natural snow types. Only our machine made snow shows a morphology which cannot be modeled by DISORT. Thus, our results show for the first time a direct experimental correlation between transmittance and snow specific surface area and density without the need for an empirical fitting parameter. We feel this to be an important step towards a possible high-resolution, quantitative optical measurement method to determine snow density in combination with an independent specific surface area measurement. |
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| Item Description: | Gesehen am 28.02.2023 |
| Physical Description: | Online Resource |
| DOI: | 10.1016/j.coldregions.2010.06.005 |