Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS
Linear Kinematic Features (LKFs) detected and tracked in sea-ice deformation fields simulated by sea-ice models participating in the Sea Ice Rheology Experiment (SIREx), a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). These data are the basis of t...
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Online Access: | https://dx.doi.org/10.5281/zenodo.6315226 https://zenodo.org/record/6315226 |
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ftdatacite:10.5281/zenodo.6315226 2023-05-15T15:01:01+02:00 Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS Hutter, Nils Bouchat, Amélie Dupont, Frédéric Dukhovskoy, Dmitry Koldunov, Nikolay Lee, Younjoo Lemieux, Jean-François Lique, Camille Losch, Martin Maslowski, Wieslaw Myers, Paul G. Ólason, Einar Rampal, Pierre Rasmussen, Till Talandier, Claude Tremblay, Bruno Wang, Qiang 2022 https://dx.doi.org/10.5281/zenodo.6315226 https://zenodo.org/record/6315226 unknown Zenodo https://zenodo.org/communities/sirex https://dx.doi.org/10.5281/zenodo.6315225 https://zenodo.org/communities/sirex Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess CC-BY sea ice deformation sea ice modelling satellite observations fracture Linear Kinematic Features LKFs Dataset dataset 2022 ftdatacite https://doi.org/10.5281/zenodo.6315226 https://doi.org/10.5281/zenodo.6315225 2022-04-01T08:35:56Z Linear Kinematic Features (LKFs) detected and tracked in sea-ice deformation fields simulated by sea-ice models participating in the Sea Ice Rheology Experiment (SIREx), a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). These data are the basis of the feature-based evaluation of sea-ice deformation in Hutter et al., Sea Ice Rheology Experiment (SIREx), Part II: Evaluating linear kinematic features in high-resolution sea-ice simulations, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the parameters of the LKF extraction. The LKF data sets in this archive are stored in a csv-files for each year (1997 and/or 2008), which use semi-colons as delimiters. Each row corresponds to a pixel that was identified as LKF and the following information for this pixels is stored: Start Year, Start Month, Start Day, End Year, End Month, End Day, LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate. All pixels belonging to the same LKF have the same LKF number. Tracked LKFs are linked by the parent LKF number, where "0" denotes LKFs that newly formed. Detailed information on all variables is provided in the additional notes. : Start Year, Start Month, Start Day -> Start date of the time interval in which LKFs are detected End Year, End Month, End Day -> End date of the time interval in which LKFs are detected LKF No. -> each LKF in one winter has its unique identifier number that can be used to track the feature Parent LKF No. -> LKF No. of the LKF from the previous time records, for that this LKF is a temporal continuation determined by the tracking algorithm. This entry can contain multiple numbers if the current LKF was formed from multiple LKFs. '0' is used as a fill value, if there is no parent LKF. ind_x,ind_y -> Indexes of the LKF pixel in the subset of the original netcdf data covering of Arctic Ocean. The indexes can be used to index original netcdf files of the model output, due to the subsetting for some model runs, we recommend to use the lat/lon position instead. lon, lat -> position of LKF pixel divergence and shear rate -> The divergence and shear rate of RGPS deformation data at LKF pixel. The divergence rate can be used to distinguish leads and pressure ridges in the data-set, please see Hutter et al. (2019). Hutter, N., Zampieri, L. & Losch, M. Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm. Cryosphere 13, 627–645 (2019) Dataset Arctic Arctic Ocean Sea ice DataCite Metadata Store (German National Library of Science and Technology) Arctic Arctic Ocean |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
unknown |
topic |
sea ice deformation sea ice modelling satellite observations fracture Linear Kinematic Features LKFs |
spellingShingle |
sea ice deformation sea ice modelling satellite observations fracture Linear Kinematic Features LKFs Hutter, Nils Bouchat, Amélie Dupont, Frédéric Dukhovskoy, Dmitry Koldunov, Nikolay Lee, Younjoo Lemieux, Jean-François Lique, Camille Losch, Martin Maslowski, Wieslaw Myers, Paul G. Ólason, Einar Rampal, Pierre Rasmussen, Till Talandier, Claude Tremblay, Bruno Wang, Qiang Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS |
topic_facet |
sea ice deformation sea ice modelling satellite observations fracture Linear Kinematic Features LKFs |
description |
Linear Kinematic Features (LKFs) detected and tracked in sea-ice deformation fields simulated by sea-ice models participating in the Sea Ice Rheology Experiment (SIREx), a model intercomparison project of the Forum of Arctic Modeling and Observational Synthesis (FAMOS). These data are the basis of the feature-based evaluation of sea-ice deformation in Hutter et al., Sea Ice Rheology Experiment (SIREx), Part II: Evaluating linear kinematic features in high-resolution sea-ice simulations, Journal of Geophysical Research: Oceans (2022). This paper also provides further details on the parameters of the LKF extraction. The LKF data sets in this archive are stored in a csv-files for each year (1997 and/or 2008), which use semi-colons as delimiters. Each row corresponds to a pixel that was identified as LKF and the following information for this pixels is stored: Start Year, Start Month, Start Day, End Year, End Month, End Day, LKF No., Parent LKF No., lon, lat, ind_x, ind_y, divergence rate, shear rate. All pixels belonging to the same LKF have the same LKF number. Tracked LKFs are linked by the parent LKF number, where "0" denotes LKFs that newly formed. Detailed information on all variables is provided in the additional notes. : Start Year, Start Month, Start Day -> Start date of the time interval in which LKFs are detected End Year, End Month, End Day -> End date of the time interval in which LKFs are detected LKF No. -> each LKF in one winter has its unique identifier number that can be used to track the feature Parent LKF No. -> LKF No. of the LKF from the previous time records, for that this LKF is a temporal continuation determined by the tracking algorithm. This entry can contain multiple numbers if the current LKF was formed from multiple LKFs. '0' is used as a fill value, if there is no parent LKF. ind_x,ind_y -> Indexes of the LKF pixel in the subset of the original netcdf data covering of Arctic Ocean. The indexes can be used to index original netcdf files of the model output, due to the subsetting for some model runs, we recommend to use the lat/lon position instead. lon, lat -> position of LKF pixel divergence and shear rate -> The divergence and shear rate of RGPS deformation data at LKF pixel. The divergence rate can be used to distinguish leads and pressure ridges in the data-set, please see Hutter et al. (2019). Hutter, N., Zampieri, L. & Losch, M. Leads and ridges in Arctic sea ice from RGPS data and a new tracking algorithm. Cryosphere 13, 627–645 (2019) |
format |
Dataset |
author |
Hutter, Nils Bouchat, Amélie Dupont, Frédéric Dukhovskoy, Dmitry Koldunov, Nikolay Lee, Younjoo Lemieux, Jean-François Lique, Camille Losch, Martin Maslowski, Wieslaw Myers, Paul G. Ólason, Einar Rampal, Pierre Rasmussen, Till Talandier, Claude Tremblay, Bruno Wang, Qiang |
author_facet |
Hutter, Nils Bouchat, Amélie Dupont, Frédéric Dukhovskoy, Dmitry Koldunov, Nikolay Lee, Younjoo Lemieux, Jean-François Lique, Camille Losch, Martin Maslowski, Wieslaw Myers, Paul G. Ólason, Einar Rampal, Pierre Rasmussen, Till Talandier, Claude Tremblay, Bruno Wang, Qiang |
author_sort |
Hutter, Nils |
title |
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS |
title_short |
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS |
title_full |
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS |
title_fullStr |
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS |
title_full_unstemmed |
Linear Kinematic Feature detected and tracked in sea-ice deformation simulationed by all models participating in the Sea Ice Rheology Experiment and from RGPS |
title_sort |
linear kinematic feature detected and tracked in sea-ice deformation simulationed by all models participating in the sea ice rheology experiment and from rgps |
publisher |
Zenodo |
publishDate |
2022 |
url |
https://dx.doi.org/10.5281/zenodo.6315226 https://zenodo.org/record/6315226 |
geographic |
Arctic Arctic Ocean |
geographic_facet |
Arctic Arctic Ocean |
genre |
Arctic Arctic Ocean Sea ice |
genre_facet |
Arctic Arctic Ocean Sea ice |
op_relation |
https://zenodo.org/communities/sirex https://dx.doi.org/10.5281/zenodo.6315225 https://zenodo.org/communities/sirex |
op_rights |
Open Access Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 info:eu-repo/semantics/openAccess |
op_rightsnorm |
CC-BY |
op_doi |
https://doi.org/10.5281/zenodo.6315226 https://doi.org/10.5281/zenodo.6315225 |
_version_ |
1766333076716126208 |