SIC, SIT, SST and SSS from HYCOM-CICE with LAON assimilation of SIC

This data is generated using the Norwegian High-resolution (3-5km) pan-Arctic ocean and sea ice Prediction System (NorHAPS), which is based on a coupled HYCOM-CICE model with the Local Analytical Optimal Nudging (LAON) for assimilation. The twin experiment simulations cover the pan-Arctic domain fro...

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Bibliographic Details
Main Authors: Wang, Keguang, Ali, Alfatih, Wang, Caixin
Format: Other/Unknown Material
Language:unknown
Published: Zenodo 2023
Subjects:
sea ice concentration
sea ice thickness
sea surface temperature
sea surface salinity
Arctic Ocean
Sea ice
Online Access:https://doi.org/10.5281/zenodo.10025338
id ftzenodo:oai:zenodo.org:10025338
record_format openpolar
spelling ftzenodo:oai:zenodo.org:10025338 2024-09-15T17:53:31+00:00 SIC, SIT, SST and SSS from HYCOM-CICE with LAON assimilation of SIC Wang, Keguang Ali, Alfatih Wang, Caixin 2023-10-20 https://doi.org/10.5281/zenodo.10025338 unknown Zenodo https://doi.org/10.5281/zenodo.7533371 https://doi.org/10.5281/zenodo.10025338 oai:zenodo.org:10025338 info:eu-repo/semantics/openAccess Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode sea ice concentration sea ice thickness sea surface temperature sea surface salinity info:eu-repo/semantics/other 2023 ftzenodo https://doi.org/10.5281/zenodo.1002533810.5281/zenodo.7533371 2024-07-26T23:22:51Z This data is generated using the Norwegian High-resolution (3-5km) pan-Arctic ocean and sea ice Prediction System (NorHAPS), which is based on a coupled HYCOM-CICE model with the Local Analytical Optimal Nudging (LAON) for assimilation. The twin experiment simulations cover the pan-Arctic domain from 1 January 2021 to 30 April 2022, with and without assimilation of sea ice concentration. The data includes sea ice concentration and thickness, sea surface temperature and salinity. The updated version includes OSTIA skin SST data for the same period, which was obtained from CMEMS. Other/Unknown Material Arctic Ocean Sea ice Zenodo
institution Open Polar
collection Zenodo
op_collection_id ftzenodo
language unknown
topic sea ice concentration
sea ice thickness
sea surface temperature
sea surface salinity
spellingShingle sea ice concentration
sea ice thickness
sea surface temperature
sea surface salinity
Wang, Keguang
Ali, Alfatih
Wang, Caixin
SIC, SIT, SST and SSS from HYCOM-CICE with LAON assimilation of SIC
topic_facet sea ice concentration
sea ice thickness
sea surface temperature
sea surface salinity
description This data is generated using the Norwegian High-resolution (3-5km) pan-Arctic ocean and sea ice Prediction System (NorHAPS), which is based on a coupled HYCOM-CICE model with the Local Analytical Optimal Nudging (LAON) for assimilation. The twin experiment simulations cover the pan-Arctic domain from 1 January 2021 to 30 April 2022, with and without assimilation of sea ice concentration. The data includes sea ice concentration and thickness, sea surface temperature and salinity. The updated version includes OSTIA skin SST data for the same period, which was obtained from CMEMS.
format Other/Unknown Material
author Wang, Keguang
Ali, Alfatih
Wang, Caixin
author_facet Wang, Keguang
Ali, Alfatih
Wang, Caixin
author_sort Wang, Keguang
title SIC, SIT, SST and SSS from HYCOM-CICE with LAON assimilation of SIC
title_short SIC, SIT, SST and SSS from HYCOM-CICE with LAON assimilation of SIC
title_full SIC, SIT, SST and SSS from HYCOM-CICE with LAON assimilation of SIC
title_fullStr SIC, SIT, SST and SSS from HYCOM-CICE with LAON assimilation of SIC
title_full_unstemmed SIC, SIT, SST and SSS from HYCOM-CICE with LAON assimilation of SIC
title_sort sic, sit, sst and sss from hycom-cice with laon assimilation of sic
publisher Zenodo
publishDate 2023
url https://doi.org/10.5281/zenodo.10025338
genre Arctic Ocean
Sea ice
genre_facet Arctic Ocean
Sea ice
op_relation https://doi.org/10.5281/zenodo.7533371
https://doi.org/10.5281/zenodo.10025338
oai:zenodo.org:10025338
op_rights info:eu-repo/semantics/openAccess
Creative Commons Attribution 4.0 International
https://creativecommons.org/licenses/by/4.0/legalcode
op_doi https://doi.org/10.5281/zenodo.1002533810.5281/zenodo.7533371
_version_ 1810429380079386624

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