Estimating early-winter Antarctic sea ice thickness from deformed ice morphology
Satellites have documented variability in sea ice areal extent for decades, but there are significant challenges in obtaining analogous measurements for sea ice thickness data in the Antarctic, primarily due to difficulties in estimating snow cover on sea ice. Sea ice thickness (SIT) can be estimate...
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2019
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ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00040724 2023-05-15T14:02:33+02:00 Estimating early-winter Antarctic sea ice thickness from deformed ice morphology Mei, M. Jeffrey Maksym, Ted Weissling, Blake Singh, Hanumant 2019-11 electronic https://doi.org/10.5194/tc-13-2915-2019 https://noa.gwlb.de/receive/cop_mods_00040724 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00040346/tc-13-2915-2019.pdf https://tc.copernicus.org/articles/13/2915/2019/tc-13-2915-2019.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-13-2915-2019 https://noa.gwlb.de/receive/cop_mods_00040724 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00040346/tc-13-2915-2019.pdf https://tc.copernicus.org/articles/13/2915/2019/tc-13-2915-2019.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess CC-BY article Verlagsveröffentlichung article Text doc-type:article 2019 ftnonlinearchiv https://doi.org/10.5194/tc-13-2915-2019 2022-02-08T22:41:59Z Satellites have documented variability in sea ice areal extent for decades, but there are significant challenges in obtaining analogous measurements for sea ice thickness data in the Antarctic, primarily due to difficulties in estimating snow cover on sea ice. Sea ice thickness (SIT) can be estimated from snow freeboard measurements, such as those from airborne/satellite lidar, by assuming some snow depth distribution or empirically fitting with limited data from drilled transects from various field studies. Current estimates for large-scale Antarctic SIT have errors as high as ∼50 %, and simple statistical models of small-scale mean thickness have similarly high errors. Averaging measurements over hundreds of meters can improve the model fits to existing data, though these results do not necessarily generalize to other floes. At present, we do not have algorithms that accurately estimate SIT at high resolutions. We use a convolutional neural network with laser altimetry profiles of sea ice surfaces at 0.2 m resolution to show that it is possible to estimate SIT at 20 m resolution with better accuracy and generalization than current methods (mean relative errors ∼15 %). Moreover, the neural network does not require specification of snow depth or density, which increases its potential applications to other lidar datasets. The learned features appear to correspond to basic morphological features, and these features appear to be common to other floes with the same climatology. This suggests that there is a relationship between the surface morphology and the ice thickness. The model has a mean relative error of 20 % when applied to a new floe from the region and season. This method may be extended to lower-resolution, larger-footprint data such as such as Operation IceBridge, and it suggests a possible avenue to reduce errors in satellite estimates of Antarctic SIT from ICESat-2 over current methods, especially at smaller scales. Article in Journal/Newspaper Antarc* Antarctic Sea ice The Cryosphere Niedersächsisches Online-Archiv NOA Antarctic The Antarctic The Cryosphere 13 11 2915 2934 |
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article Verlagsveröffentlichung |
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article Verlagsveröffentlichung Mei, M. Jeffrey Maksym, Ted Weissling, Blake Singh, Hanumant Estimating early-winter Antarctic sea ice thickness from deformed ice morphology |
topic_facet |
article Verlagsveröffentlichung |
description |
Satellites have documented variability in sea ice areal extent for decades, but there are significant challenges in obtaining analogous measurements for sea ice thickness data in the Antarctic, primarily due to difficulties in estimating snow cover on sea ice. Sea ice thickness (SIT) can be estimated from snow freeboard measurements, such as those from airborne/satellite lidar, by assuming some snow depth distribution or empirically fitting with limited data from drilled transects from various field studies. Current estimates for large-scale Antarctic SIT have errors as high as ∼50 %, and simple statistical models of small-scale mean thickness have similarly high errors. Averaging measurements over hundreds of meters can improve the model fits to existing data, though these results do not necessarily generalize to other floes. At present, we do not have algorithms that accurately estimate SIT at high resolutions. We use a convolutional neural network with laser altimetry profiles of sea ice surfaces at 0.2 m resolution to show that it is possible to estimate SIT at 20 m resolution with better accuracy and generalization than current methods (mean relative errors ∼15 %). Moreover, the neural network does not require specification of snow depth or density, which increases its potential applications to other lidar datasets. The learned features appear to correspond to basic morphological features, and these features appear to be common to other floes with the same climatology. This suggests that there is a relationship between the surface morphology and the ice thickness. The model has a mean relative error of 20 % when applied to a new floe from the region and season. This method may be extended to lower-resolution, larger-footprint data such as such as Operation IceBridge, and it suggests a possible avenue to reduce errors in satellite estimates of Antarctic SIT from ICESat-2 over current methods, especially at smaller scales. |
format |
Article in Journal/Newspaper |
author |
Mei, M. Jeffrey Maksym, Ted Weissling, Blake Singh, Hanumant |
author_facet |
Mei, M. Jeffrey Maksym, Ted Weissling, Blake Singh, Hanumant |
author_sort |
Mei, M. Jeffrey |
title |
Estimating early-winter Antarctic sea ice thickness from deformed ice morphology |
title_short |
Estimating early-winter Antarctic sea ice thickness from deformed ice morphology |
title_full |
Estimating early-winter Antarctic sea ice thickness from deformed ice morphology |
title_fullStr |
Estimating early-winter Antarctic sea ice thickness from deformed ice morphology |
title_full_unstemmed |
Estimating early-winter Antarctic sea ice thickness from deformed ice morphology |
title_sort |
estimating early-winter antarctic sea ice thickness from deformed ice morphology |
publisher |
Copernicus Publications |
publishDate |
2019 |
url |
https://doi.org/10.5194/tc-13-2915-2019 https://noa.gwlb.de/receive/cop_mods_00040724 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00040346/tc-13-2915-2019.pdf https://tc.copernicus.org/articles/13/2915/2019/tc-13-2915-2019.pdf |
geographic |
Antarctic The Antarctic |
geographic_facet |
Antarctic The Antarctic |
genre |
Antarc* Antarctic Sea ice The Cryosphere |
genre_facet |
Antarc* Antarctic Sea ice 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-13-2915-2019 https://noa.gwlb.de/receive/cop_mods_00040724 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00040346/tc-13-2915-2019.pdf https://tc.copernicus.org/articles/13/2915/2019/tc-13-2915-2019.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-13-2915-2019 |
container_title |
The Cryosphere |
container_volume |
13 |
container_issue |
11 |
container_start_page |
2915 |
op_container_end_page |
2934 |
_version_ |
1766272867480109056 |