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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Bibliographic Details
Main Authors: Lei, Ruibo, Cheng, Bin, Hoppmann, Mario, Zuo, Guangyu
Format: Dataset
Language:English
Published: PANGAEA 2021
Subjects:
Online Access:https://dx.doi.org/10.1594/pangaea.938225
https://doi.pangaea.de/10.1594/PANGAEA.938225
id ftdatacite:10.1594/pangaea.938225
record_format openpolar
spelling 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
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