A generalized photon-tracking approach to simulate spectral snow albedo and transmittance using X-ray microtomography and geometric optics
A majority of snow radiative transfer models (RTMs) treat snow as a collection of idealized grains rather than an organized ice–air matrix. Here we present a generalized multi-layer photon-tracking RTM that simulates light reflectance and transmittance of snow based on X-ray microtomography images,...
Published in: | The Cryosphere |
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Copernicus Publications
2022
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00062886 2023-05-15T18:32:33+02:00 A generalized photon-tracking approach to simulate spectral snow albedo and transmittance using X-ray microtomography and geometric optics Letcher, Theodore Parno, Julie Courville, Zoe Farnsworth, Lauren Olivier, Jason 2022-10 electronic https://doi.org/10.5194/tc-16-4343-2022 https://noa.gwlb.de/receive/cop_mods_00062886 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00062047/tc-16-4343-2022.pdf https://tc.copernicus.org/articles/16/4343/2022/tc-16-4343-2022.pdf eng eng Copernicus Publications The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-16-4343-2022 https://noa.gwlb.de/receive/cop_mods_00062886 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00062047/tc-16-4343-2022.pdf https://tc.copernicus.org/articles/16/4343/2022/tc-16-4343-2022.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess CC-BY article Verlagsveröffentlichung article Text doc-type:article 2022 ftnonlinearchiv https://doi.org/10.5194/tc-16-4343-2022 2022-10-23T23:12:12Z A majority of snow radiative transfer models (RTMs) treat snow as a collection of idealized grains rather than an organized ice–air matrix. Here we present a generalized multi-layer photon-tracking RTM that simulates light reflectance and transmittance of snow based on X-ray microtomography images, treating snow as a coherent 3D structure rather than a collection of grains. The model uses a blended approach to expand ray-tracing techniques applied to sub-1 cm3 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. Article in Journal/Newspaper The Cryosphere Niedersächsisches Online-Archiv NOA The Cryosphere 16 10 4343 4361 |
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English |
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article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Letcher, Theodore Parno, Julie Courville, Zoe Farnsworth, Lauren Olivier, Jason A generalized photon-tracking approach to simulate spectral snow albedo and transmittance using X-ray microtomography and geometric optics |
topic_facet |
article Verlagsveröffentlichung |
description |
A majority of snow radiative transfer models (RTMs) treat snow as a collection of idealized grains rather than an organized ice–air matrix. Here we present a generalized multi-layer photon-tracking RTM that simulates light reflectance and transmittance of snow based on X-ray microtomography images, treating snow as a coherent 3D structure rather than a collection of grains. The model uses a blended approach to expand ray-tracing techniques applied to sub-1 cm3 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. |
format |
Article in Journal/Newspaper |
author |
Letcher, Theodore Parno, Julie Courville, Zoe Farnsworth, Lauren Olivier, Jason |
author_facet |
Letcher, Theodore Parno, Julie Courville, Zoe Farnsworth, Lauren Olivier, Jason |
author_sort |
Letcher, Theodore |
title |
A generalized photon-tracking approach to simulate spectral snow albedo and transmittance using X-ray microtomography and geometric optics |
title_short |
A generalized photon-tracking approach to simulate spectral snow albedo and transmittance using X-ray microtomography and geometric optics |
title_full |
A generalized photon-tracking approach to simulate spectral snow albedo and transmittance using X-ray microtomography and geometric optics |
title_fullStr |
A generalized photon-tracking approach to simulate spectral snow albedo and transmittance using X-ray microtomography and geometric optics |
title_full_unstemmed |
A generalized photon-tracking approach to simulate spectral snow albedo and transmittance using X-ray microtomography and geometric optics |
title_sort |
generalized photon-tracking approach to simulate spectral snow albedo and transmittance using x-ray microtomography and geometric optics |
publisher |
Copernicus Publications |
publishDate |
2022 |
url |
https://doi.org/10.5194/tc-16-4343-2022 https://noa.gwlb.de/receive/cop_mods_00062886 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00062047/tc-16-4343-2022.pdf https://tc.copernicus.org/articles/16/4343/2022/tc-16-4343-2022.pdf |
genre |
The Cryosphere |
genre_facet |
The Cryosphere |
op_relation |
The Cryosphere -- ˜Theœ Cryosphere -- http://www.bibliothek.uni-regensburg.de/ezeit/?2393169 -- http://www.the-cryosphere.net/ -- 1994-0424 https://doi.org/10.5194/tc-16-4343-2022 https://noa.gwlb.de/receive/cop_mods_00062886 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00062047/tc-16-4343-2022.pdf https://tc.copernicus.org/articles/16/4343/2022/tc-16-4343-2022.pdf |
op_rights |
https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5194/tc-16-4343-2022 |
container_title |
The Cryosphere |
container_volume |
16 |
container_issue |
10 |
container_start_page |
4343 |
op_container_end_page |
4361 |
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1766216735775522816 |