A generalized photon-tracking approach to simulate spectral snow albedo and transmittance using X-ray microtomography and geometric optics
Abstract 3 snow samples to snowpacks of arbitrary depths. While this framework has many potential applications, this study's effort is focused on simulating reflectance and transmittance in the visible and near infrared (NIR) through thin snowpacks as this is relevant for surface energy balance and remote sensing applications. We demonstrate that this framework fits well within the context of previous work and capably reproduces many known optical properties of a snow surface, including the dependence of spectral reflectance on the snow specific surface area and incident zenith angle as well as the surface bidirectional reflectance distribution function (BRDF). To evaluate the model, we compare it against reflectance data collected with a spectroradiometer at a field site in east-central Vermont. In this experiment, painted panels were inserted at various depths beneath the snow to emulate thin snow. The model compares remarkably well against the reflectance measured with a spectroradiometer, with an average RMSE of 0.03 in the 400–1600 nm range. Sensitivity simulations using this model indicate that snow transmittance is greatest in the visible wavelengths, limiting light penetration to the top 6 cm of the snowpack for fine-grain snow but increasing to 12 cm for coarse-grain snow. These results suggest that the 5 % transmission depth in snow can vary by over 6 cm according to the snow type.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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A generalized photon-tracking approach to simulate spectral snow albedo and transmittance using X-ray microtomography and geometric optics ; volume:16 ; number:10 ; year:2022 ; pages:4343-4361 ; extent:19
The Cryosphere ; 16, Heft 10 (2022), 4343-4361 (gesamt 19)
- Urheber
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Letcher, Theodore
Parno, Julie
Courville, Zoe
Farnsworth, Lauren
Olivier, Jason
- DOI
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10.5194/tc-16-4343-2022
- URN
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urn:nbn:de:101:1-2022102005164667735814
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
- 15.08.2025, 07:29 MESZ
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Beteiligte
- Letcher, Theodore
- Parno, Julie
- Courville, Zoe
- Farnsworth, Lauren
- Olivier, Jason
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