Linear Kinematic Features (leads & pressure ridges) detected and tracked in sea-ice deformation simulated in an Arctic configuration of MITgcm using a 2-km horizontal grid spacing with an active 5-class ice thickness distribution from 1997 to 2008 ...

Leads and pressure ridges are dominant features of the Arctic sea ice cover. Not only do they affect heat loss and surface drag, but also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred as Linear Kinematic Features (LKFs). This data-...

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Main Author: Hutter, Nils
Format: Dataset
Language:English
Published: PANGAEA 2019
Subjects:
Online Access:https://dx.doi.org/10.1594/pangaea.909632
https://doi.pangaea.de/10.1594/PANGAEA.909632
id ftdatacite:10.1594/pangaea.909632
record_format openpolar
spelling ftdatacite:10.1594/pangaea.909632 2024-09-15T17:54:21+00:00 Linear Kinematic Features (leads & pressure ridges) detected and tracked in sea-ice deformation simulated in an Arctic configuration of MITgcm using a 2-km horizontal grid spacing with an active 5-class ice thickness distribution from 1997 to 2008 ... Hutter, Nils 2019 application/zip https://dx.doi.org/10.1594/pangaea.909632 https://doi.pangaea.de/10.1594/PANGAEA.909632 en eng PANGAEA https://dx.doi.org/10.5194/tc-14-93-2020 https://dx.doi.org/10.5194/tc-13-627-2019 https://dx.doi.org/10.1594/pangaea.898114 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 dataset Dataset 2019 ftdatacite https://doi.org/10.1594/pangaea.90963210.5194/tc-14-93-202010.5194/tc-13-627-201910.1594/pangaea.898114 2024-08-01T10:50:55Z Leads and pressure ridges are dominant features of the Arctic sea ice cover. Not only do they affect heat loss and surface drag, but also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred as Linear Kinematic Features (LKFs). This data-set includes LKFs that were detected and tracked in sea ice deformation simulated in an Arctic configuration of MITgcm using a 2-km horizontal grid spacing and an active 5-class ice thickness distribution. The model data is sampled for the entire observing period of the RADARSAT Geophysical Processor System (RGPS). The data-set spans the winter month (November to May) from 1997 to 2008 and covers the entire Arctic Ocean. A detailed description of the model configuration and the data-set is provided in: Hutter, N. and Losch, M.: Feature-based comparison of sea-ice deformation in lead-resolving sea-ice simulations, The Cryosphere, https://doi.org/10.5194/tc-2019-88, accepted for publication, 2019. A detailed ... : Data Description:The data set covers all RGPS winter data, i.e. November to May for the years 1996/97 to 2007/08. The LKFs of each winter season are saved in one TAB-delimited text-file (ASCII). In total the data-set contains in 12 files.In the csv-file each row corresponds to one pixel of an LKF in this year. In individual pixels are sorted by date and LKF. Each LKF gets a identifier number (LKF No.) that is unique in this winter. For track features the LKF No.(s) of parent LKF(s) from the previous RGPS time record are provided. The columns of the csv-files are structured in the following way:Start Year, Start Month, Start Day, End Year, End Month, End Day, Date(RGPS format), LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rateSpecific comments:Start Year, Start Month, Start Day -> Start date of the RGPS time record in which LKFs are detectedEnd Year, End Month, End Day -> End date of the RGPS time record in which LKFs are detectedDate in original RGPS format -> RGPS format ... Dataset Arctic Ocean Sea ice DataCite
institution Open Polar
collection DataCite
op_collection_id ftdatacite
language English
description Leads and pressure ridges are dominant features of the Arctic sea ice cover. Not only do they affect heat loss and surface drag, but also provide insight into the underlying physics of sea ice deformation. Due to their elongated shape they are referred as Linear Kinematic Features (LKFs). This data-set includes LKFs that were detected and tracked in sea ice deformation simulated in an Arctic configuration of MITgcm using a 2-km horizontal grid spacing and an active 5-class ice thickness distribution. The model data is sampled for the entire observing period of the RADARSAT Geophysical Processor System (RGPS). The data-set spans the winter month (November to May) from 1997 to 2008 and covers the entire Arctic Ocean. A detailed description of the model configuration and the data-set is provided in: Hutter, N. and Losch, M.: Feature-based comparison of sea-ice deformation in lead-resolving sea-ice simulations, The Cryosphere, https://doi.org/10.5194/tc-2019-88, accepted for publication, 2019. A detailed ... : Data Description:The data set covers all RGPS winter data, i.e. November to May for the years 1996/97 to 2007/08. The LKFs of each winter season are saved in one TAB-delimited text-file (ASCII). In total the data-set contains in 12 files.In the csv-file each row corresponds to one pixel of an LKF in this year. In individual pixels are sorted by date and LKF. Each LKF gets a identifier number (LKF No.) that is unique in this winter. For track features the LKF No.(s) of parent LKF(s) from the previous RGPS time record are provided. The columns of the csv-files are structured in the following way:Start Year, Start Month, Start Day, End Year, End Month, End Day, Date(RGPS format), LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rateSpecific comments:Start Year, Start Month, Start Day -> Start date of the RGPS time record in which LKFs are detectedEnd Year, End Month, End Day -> End date of the RGPS time record in which LKFs are detectedDate in original RGPS format -> RGPS format ...
format Dataset
author Hutter, Nils
spellingShingle Hutter, Nils
Linear Kinematic Features (leads & pressure ridges) detected and tracked in sea-ice deformation simulated in an Arctic configuration of MITgcm using a 2-km horizontal grid spacing with an active 5-class ice thickness distribution from 1997 to 2008 ...
author_facet Hutter, Nils
author_sort Hutter, Nils
title Linear Kinematic Features (leads & pressure ridges) detected and tracked in sea-ice deformation simulated in an Arctic configuration of MITgcm using a 2-km horizontal grid spacing with an active 5-class ice thickness distribution from 1997 to 2008 ...
title_short Linear Kinematic Features (leads & pressure ridges) detected and tracked in sea-ice deformation simulated in an Arctic configuration of MITgcm using a 2-km horizontal grid spacing with an active 5-class ice thickness distribution from 1997 to 2008 ...
title_full Linear Kinematic Features (leads & pressure ridges) detected and tracked in sea-ice deformation simulated in an Arctic configuration of MITgcm using a 2-km horizontal grid spacing with an active 5-class ice thickness distribution from 1997 to 2008 ...
title_fullStr Linear Kinematic Features (leads & pressure ridges) detected and tracked in sea-ice deformation simulated in an Arctic configuration of MITgcm using a 2-km horizontal grid spacing with an active 5-class ice thickness distribution from 1997 to 2008 ...
title_full_unstemmed Linear Kinematic Features (leads & pressure ridges) detected and tracked in sea-ice deformation simulated in an Arctic configuration of MITgcm using a 2-km horizontal grid spacing with an active 5-class ice thickness distribution from 1997 to 2008 ...
title_sort linear kinematic features (leads & pressure ridges) detected and tracked in sea-ice deformation simulated in an arctic configuration of mitgcm using a 2-km horizontal grid spacing with an active 5-class ice thickness distribution from 1997 to 2008 ...
publisher PANGAEA
publishDate 2019
url https://dx.doi.org/10.1594/pangaea.909632
https://doi.pangaea.de/10.1594/PANGAEA.909632
genre Arctic Ocean
Sea ice
genre_facet Arctic Ocean
Sea ice
op_relation https://dx.doi.org/10.5194/tc-14-93-2020
https://dx.doi.org/10.5194/tc-13-627-2019
https://dx.doi.org/10.1594/pangaea.898114
op_rights Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
cc-by-4.0
op_doi https://doi.org/10.1594/pangaea.90963210.5194/tc-14-93-202010.5194/tc-13-627-201910.1594/pangaea.898114
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