Sea Ice Numerical VP-Comparison Benchmark

This data set includes the numerical data accompanying the publication "Simulating linear kinematic features in viscous-plastic sea ice models on quadrilateral and triangular grids with different variable staggering". In addition to the raw data, the Python script is also deposited, which...

Full description

Bibliographic Details
Main Author: Richter, T (via Mendeley Data)
Language:unknown
Published: 2021
Subjects:
Online Access:http://nbn-resolving.org/urn:nbn:nl:ui:13-n1-kht0
https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:216380
id ftdans:oai:easy.dans.knaw.nl:easy-dataset:216380
record_format openpolar
spelling ftdans:oai:easy.dans.knaw.nl:easy-dataset:216380 2023-07-02T03:33:41+02:00 Sea Ice Numerical VP-Comparison Benchmark Richter, T (via Mendeley Data) 2021-07-19T07:19:18.158Z http://nbn-resolving.org/urn:nbn:nl:ui:13-n1-kht0 https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:216380 unknown 1 kj58y3sdtk http://nbn-resolving.org/urn:nbn:nl:ui:13-n1-kht0 doi:10.17632/kj58y3sdtk.1 https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:216380 OPEN_ACCESS: The data are archived in Easy, they are accessible elsewhere through the DOI https://dans.knaw.nl/en/about/organisation-and-policy/legal-information/DANSLicence.pdf Thomas Richter Interdisciplinary sciences 2021 ftdans https://doi.org/10.17632/kj58y3sdtk.1 2023-06-13T12:59:12Z This data set includes the numerical data accompanying the publication "Simulating linear kinematic features in viscous-plastic sea ice models on quadrilateral and triangular grids with different variable staggering". In addition to the raw data, the Python script is also deposited, which can be used to detect the Linear Kinematic Features, an essential parameter for comparing the different methods. The folder names correspond to the different models that have been compared in the paper. The folder "analysis" contains the Python-Script for LKF detection. Other/Unknown Material Sea ice Data Archiving and Networked Services (DANS): EASY (KNAW - Koninklijke Nederlandse Akademie van Wetenschappen)
institution Open Polar
collection Data Archiving and Networked Services (DANS): EASY (KNAW - Koninklijke Nederlandse Akademie van Wetenschappen)
op_collection_id ftdans
language unknown
topic Interdisciplinary sciences
spellingShingle Interdisciplinary sciences
Richter, T (via Mendeley Data)
Sea Ice Numerical VP-Comparison Benchmark
topic_facet Interdisciplinary sciences
description This data set includes the numerical data accompanying the publication "Simulating linear kinematic features in viscous-plastic sea ice models on quadrilateral and triangular grids with different variable staggering". In addition to the raw data, the Python script is also deposited, which can be used to detect the Linear Kinematic Features, an essential parameter for comparing the different methods. The folder names correspond to the different models that have been compared in the paper. The folder "analysis" contains the Python-Script for LKF detection.
author Richter, T (via Mendeley Data)
author_facet Richter, T (via Mendeley Data)
author_sort Richter, T (via Mendeley Data)
title Sea Ice Numerical VP-Comparison Benchmark
title_short Sea Ice Numerical VP-Comparison Benchmark
title_full Sea Ice Numerical VP-Comparison Benchmark
title_fullStr Sea Ice Numerical VP-Comparison Benchmark
title_full_unstemmed Sea Ice Numerical VP-Comparison Benchmark
title_sort sea ice numerical vp-comparison benchmark
publishDate 2021
url http://nbn-resolving.org/urn:nbn:nl:ui:13-n1-kht0
https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:216380
genre Sea ice
genre_facet Sea ice
op_relation 1
kj58y3sdtk
http://nbn-resolving.org/urn:nbn:nl:ui:13-n1-kht0
doi:10.17632/kj58y3sdtk.1
https://easy.dans.knaw.nl/ui/datasets/id/easy-dataset:216380
op_rights OPEN_ACCESS: The data are archived in Easy, they are accessible elsewhere through the DOI
https://dans.knaw.nl/en/about/organisation-and-policy/legal-information/DANSLicence.pdf
Thomas Richter
op_doi https://doi.org/10.17632/kj58y3sdtk.1
_version_ 1770273734796509184