Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T47 ...
The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environment temperature SIMBA-ET and temperature change around the thermistors after a weak heating applied to each sensor (SIMBA-HT). Totally, there were 22 SIMBAs deployed in the A...
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Online Access: | https://dx.doi.org/10.1594/pangaea.938225 https://doi.pangaea.de/10.1594/PANGAEA.938225 |
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ftdatacite:10.1594/pangaea.938225 2024-10-29T17:47:35+00:00 Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T47 ... Lei, Ruibo Cheng, Bin Hoppmann, Mario Zuo, Guangyu 2021 text/tab-separated-values https://dx.doi.org/10.1594/pangaea.938225 https://doi.pangaea.de/10.1594/PANGAEA.938225 en eng PANGAEA https://dx.doi.org/10.1594/pangaea.938244 https://dx.doi.org/10.1525/elementa.2021.000089 https://dx.doi.org/10.1175/jtech-d-13-00058.1 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Arctic Ocean Ice thickness mass balance MOSAiC expedition Sea ice snow depth DATE/TIME LATITUDE LONGITUDE Snow thickness SAMS Ice Mass Balance buoy Manual identification method AF-MOSAiC-1 Akademik Fedorov Multidisciplinary drifting Observatory for the Study of Arctic Climate MOSAiC Dataset dataset 2021 ftdatacite https://doi.org/10.1594/pangaea.93822510.1594/pangaea.93824410.1525/elementa.2021.00008910.1175/jtech-d-13-00058.1 2024-10-01T10:37:48Z The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environment temperature SIMBA-ET and temperature change around the thermistors after a weak heating applied to each sensor (SIMBA-HT). Totally, there were 22 SIMBAs deployed in the Arcitic Ocean over the Distributed Network (DN) and the Central Observatory during the Legs 1a, 1 and 3 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign. The SIMBA thermistor chain is 5.12 m long, and equipped with 256 thermistors (Maxim Integrated DS28EA00) at 0.02 m spacing. Based on a manual identification method, the SIMBA-ET and SIMBA-HT were processed to yield snow depth and ice thickness. Here, we combined the two optimal methods (the ET vertical gradient and HT rise ratio) to reduce the uncertainty. To keep the consistency, we use the snow or ice surface, consequentially the snow depth, determined by the ET vertical gradient. The formations of snow ice and ... Dataset Sea ice DataCite Arctic Arctic Ocean |
institution |
Open Polar |
collection |
DataCite |
op_collection_id |
ftdatacite |
language |
English |
topic |
Arctic Ocean Ice thickness mass balance MOSAiC expedition Sea ice snow depth DATE/TIME LATITUDE LONGITUDE Snow thickness SAMS Ice Mass Balance buoy Manual identification method AF-MOSAiC-1 Akademik Fedorov Multidisciplinary drifting Observatory for the Study of Arctic Climate MOSAiC |
spellingShingle |
Arctic Ocean Ice thickness mass balance MOSAiC expedition Sea ice snow depth DATE/TIME LATITUDE LONGITUDE Snow thickness SAMS Ice Mass Balance buoy Manual identification method AF-MOSAiC-1 Akademik Fedorov Multidisciplinary drifting Observatory for the Study of Arctic Climate MOSAiC Lei, Ruibo Cheng, Bin Hoppmann, Mario Zuo, Guangyu Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T47 ... |
topic_facet |
Arctic Ocean Ice thickness mass balance MOSAiC expedition Sea ice snow depth DATE/TIME LATITUDE LONGITUDE Snow thickness SAMS Ice Mass Balance buoy Manual identification method AF-MOSAiC-1 Akademik Fedorov Multidisciplinary drifting Observatory for the Study of Arctic Climate MOSAiC |
description |
The Snow and Ice Mass Balance Array (SIMBA) is a thermistor string type IMB (Jackson et al., 2013) which measures the environment temperature SIMBA-ET and temperature change around the thermistors after a weak heating applied to each sensor (SIMBA-HT). Totally, there were 22 SIMBAs deployed in the Arcitic Ocean over the Distributed Network (DN) and the Central Observatory during the Legs 1a, 1 and 3 of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) campaign. The SIMBA thermistor chain is 5.12 m long, and equipped with 256 thermistors (Maxim Integrated DS28EA00) at 0.02 m spacing. Based on a manual identification method, the SIMBA-ET and SIMBA-HT were processed to yield snow depth and ice thickness. Here, we combined the two optimal methods (the ET vertical gradient and HT rise ratio) to reduce the uncertainty. To keep the consistency, we use the snow or ice surface, consequentially the snow depth, determined by the ET vertical gradient. The formations of snow ice and ... |
format |
Dataset |
author |
Lei, Ruibo Cheng, Bin Hoppmann, Mario Zuo, Guangyu |
author_facet |
Lei, Ruibo Cheng, Bin Hoppmann, Mario Zuo, Guangyu |
author_sort |
Lei, Ruibo |
title |
Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T47 ... |
title_short |
Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T47 ... |
title_full |
Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T47 ... |
title_fullStr |
Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T47 ... |
title_full_unstemmed |
Snow depth and sea ice thickness derived from the measurements of SIMBA buoy 2019T47 ... |
title_sort |
snow depth and sea ice thickness derived from the measurements of simba buoy 2019t47 ... |
publisher |
PANGAEA |
publishDate |
2021 |
url |
https://dx.doi.org/10.1594/pangaea.938225 https://doi.pangaea.de/10.1594/PANGAEA.938225 |
geographic |
Arctic Arctic Ocean |
geographic_facet |
Arctic Arctic Ocean |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://dx.doi.org/10.1594/pangaea.938244 https://dx.doi.org/10.1525/elementa.2021.000089 https://dx.doi.org/10.1175/jtech-d-13-00058.1 |
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.93822510.1594/pangaea.93824410.1525/elementa.2021.00008910.1175/jtech-d-13-00058.1 |
_version_ |
1814277837932199936 |