A Dataset of Linear Kinematic Features (LKFs) to Evaluate Sea Ice Deformation
The Arctic sea ice deforms continuously due to stresses imposed by winds, ocean currents and interaction with coastlines. The most dominant features produced by this deformation in the ice cover are leads and pressure ridges that are often referred to as Linear Kinematic Features (LKFs). With increa...
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ftawi:oai:epic.awi.de:47707 2024-09-15T18:34:08+00:00 A Dataset of Linear Kinematic Features (LKFs) to Evaluate Sea Ice Deformation Hutter, Nils Zampieri, Lorenzo Losch, Martin 2018-06-22 https://epic.awi.de/id/eprint/47707/ https://hdl.handle.net/10013/epic.f329a53d-44e5-4891-9383-520855da2f78 unknown Hutter, N. orcid:0000-0003-3450-9422 , Zampieri, L. orcid:0000-0003-1703-4162 and Losch, M. orcid:0000-0002-3824-5244 (2018) A Dataset of Linear Kinematic Features (LKFs) to Evaluate Sea Ice Deformation , POLAR 2018, Davos, Swiss, 19 June 2018 - 23 June 2018 . hdl:10013/epic.f329a53d-44e5-4891-9383-520855da2f78 EPIC3POLAR 2018, Davos, Swiss, 2018-06-19-2018-06-23 Conference notRev 2018 ftawi 2024-06-24T04:19:47Z The Arctic sea ice deforms continuously due to stresses imposed by winds, ocean currents and interaction with coastlines. The most dominant features produced by this deformation in the ice cover are leads and pressure ridges that are often referred to as Linear Kinematic Features (LKFs). With increasing resolution of classical (viscous-plastic) sea ice models, or using new rheological frameworks (e.g. Maxwell elasto-brittle), sea-ice models start to resolve this small-scale deformation. Typical measures for evaluating the modelled LKFs include scaling properties of sea-ice deformation or lead area density. These metrics avoid the problem of detecting individual LKFs by applying statistics over continuous fields such as sea ice deformation or concentration. In this way, these statistical metrics can provide specific information, but lack a comprehensive description of LKFs. We detect individual LKFs in sea ice deformation fields from satellite observations with an object detection algorithm. Combining this information with the sea ice drift fields used to derive the deformation fields, the LKFs are tracked in time. In doing so, the spatial characteristics (density, length, orientation, intersection angle, curvature) and the temporal evolution can be extracted from the same data-set. Our algorithm can be applied to both observed and modelled sea-ice deformation and drift making possible a consistent comparison and thorough evaluation. Conference Object Sea ice Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) |
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Alfred Wegener Institute for Polar- and Marine Research (AWI): ePIC (electronic Publication Information Center) |
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description |
The Arctic sea ice deforms continuously due to stresses imposed by winds, ocean currents and interaction with coastlines. The most dominant features produced by this deformation in the ice cover are leads and pressure ridges that are often referred to as Linear Kinematic Features (LKFs). With increasing resolution of classical (viscous-plastic) sea ice models, or using new rheological frameworks (e.g. Maxwell elasto-brittle), sea-ice models start to resolve this small-scale deformation. Typical measures for evaluating the modelled LKFs include scaling properties of sea-ice deformation or lead area density. These metrics avoid the problem of detecting individual LKFs by applying statistics over continuous fields such as sea ice deformation or concentration. In this way, these statistical metrics can provide specific information, but lack a comprehensive description of LKFs. We detect individual LKFs in sea ice deformation fields from satellite observations with an object detection algorithm. Combining this information with the sea ice drift fields used to derive the deformation fields, the LKFs are tracked in time. In doing so, the spatial characteristics (density, length, orientation, intersection angle, curvature) and the temporal evolution can be extracted from the same data-set. Our algorithm can be applied to both observed and modelled sea-ice deformation and drift making possible a consistent comparison and thorough evaluation. |
format |
Conference Object |
author |
Hutter, Nils Zampieri, Lorenzo Losch, Martin |
spellingShingle |
Hutter, Nils Zampieri, Lorenzo Losch, Martin A Dataset of Linear Kinematic Features (LKFs) to Evaluate Sea Ice Deformation |
author_facet |
Hutter, Nils Zampieri, Lorenzo Losch, Martin |
author_sort |
Hutter, Nils |
title |
A Dataset of Linear Kinematic Features (LKFs) to Evaluate Sea Ice Deformation |
title_short |
A Dataset of Linear Kinematic Features (LKFs) to Evaluate Sea Ice Deformation |
title_full |
A Dataset of Linear Kinematic Features (LKFs) to Evaluate Sea Ice Deformation |
title_fullStr |
A Dataset of Linear Kinematic Features (LKFs) to Evaluate Sea Ice Deformation |
title_full_unstemmed |
A Dataset of Linear Kinematic Features (LKFs) to Evaluate Sea Ice Deformation |
title_sort |
dataset of linear kinematic features (lkfs) to evaluate sea ice deformation |
publishDate |
2018 |
url |
https://epic.awi.de/id/eprint/47707/ https://hdl.handle.net/10013/epic.f329a53d-44e5-4891-9383-520855da2f78 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_source |
EPIC3POLAR 2018, Davos, Swiss, 2018-06-19-2018-06-23 |
op_relation |
Hutter, N. orcid:0000-0003-3450-9422 , Zampieri, L. orcid:0000-0003-1703-4162 and Losch, M. orcid:0000-0002-3824-5244 (2018) A Dataset of Linear Kinematic Features (LKFs) to Evaluate Sea Ice Deformation , POLAR 2018, Davos, Swiss, 19 June 2018 - 23 June 2018 . hdl:10013/epic.f329a53d-44e5-4891-9383-520855da2f78 |
_version_ |
1810475868349267968 |