Sea Ice Concentration and thin Sea Ice Thickness in the Arctic retrieved with different configurations of an Optimal Estimation Method ...
Research on improving the prediction skill of climate models requires refining the quality of observational data used for initializing and tuning the models. This is especially true in the Polar Regions where uncertainties about the interactions between sea ice, ocean and atmosphere are driving ongo...
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Online Access: | https://dx.doi.org/10.1594/pangaea.912748 https://doi.pangaea.de/10.1594/PANGAEA.912748 |
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ftdatacite:10.1594/pangaea.912748 2024-09-15T18:34:08+00:00 Sea Ice Concentration and thin Sea Ice Thickness in the Arctic retrieved with different configurations of an Optimal Estimation Method ... Scarlat, Raul Cristian Huntemann, Marcus Paţilea, Cătălin 2020 text/tab-separated-values https://dx.doi.org/10.1594/pangaea.912748 https://doi.pangaea.de/10.1594/PANGAEA.912748 en eng PANGAEA https://dx.doi.org/10.1029/2019jc015749 https://dx.doi.org/10.1109/igarss.2014.6947266 https://dx.doi.org/10.1109/jstars.2017.2739858 Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 AMSR2 CIMR sea ice concentration Sea ice thickness Time coverage File content File format File size Uniform resource locator/link to file dataset Dataset 2020 ftdatacite https://doi.org/10.1594/pangaea.91274810.1029/2019jc01574910.1109/igarss.2014.694726610.1109/jstars.2017.2739858 2024-08-01T10:57:41Z Research on improving the prediction skill of climate models requires refining the quality of observational data used for initializing and tuning the models. This is especially true in the Polar Regions where uncertainties about the interactions between sea ice, ocean and atmosphere are driving ongoing monitoring efforts.The Copernicus Imaging Microwave Radiometer (CIMR) is an European Space Agency (ESA) candidate mission which promises to offer high resolution, low uncertainty observation capabilities at the 1.4, 6.9,10.65,18.7 and 36.5 GHz frequencies. To assess the potential impact of CIMR for sea ice parameter retrieval, a comparison is made between retrievals based on present AMSR2 observations and a retrieval using future CIMR equivalent observations over a data set of validated sea ice concentration (SIC) values. An optimal estimation retrieval method (OEM) is used which can use input from different channel combinations to retrieve seven geophysical parameters (sea ice concentration, multi year ice ... Dataset Sea ice DataCite |
institution |
Open Polar |
collection |
DataCite |
op_collection_id |
ftdatacite |
language |
English |
topic |
AMSR2 CIMR sea ice concentration Sea ice thickness Time coverage File content File format File size Uniform resource locator/link to file |
spellingShingle |
AMSR2 CIMR sea ice concentration Sea ice thickness Time coverage File content File format File size Uniform resource locator/link to file Scarlat, Raul Cristian Huntemann, Marcus Paţilea, Cătălin Sea Ice Concentration and thin Sea Ice Thickness in the Arctic retrieved with different configurations of an Optimal Estimation Method ... |
topic_facet |
AMSR2 CIMR sea ice concentration Sea ice thickness Time coverage File content File format File size Uniform resource locator/link to file |
description |
Research on improving the prediction skill of climate models requires refining the quality of observational data used for initializing and tuning the models. This is especially true in the Polar Regions where uncertainties about the interactions between sea ice, ocean and atmosphere are driving ongoing monitoring efforts.The Copernicus Imaging Microwave Radiometer (CIMR) is an European Space Agency (ESA) candidate mission which promises to offer high resolution, low uncertainty observation capabilities at the 1.4, 6.9,10.65,18.7 and 36.5 GHz frequencies. To assess the potential impact of CIMR for sea ice parameter retrieval, a comparison is made between retrievals based on present AMSR2 observations and a retrieval using future CIMR equivalent observations over a data set of validated sea ice concentration (SIC) values. An optimal estimation retrieval method (OEM) is used which can use input from different channel combinations to retrieve seven geophysical parameters (sea ice concentration, multi year ice ... |
format |
Dataset |
author |
Scarlat, Raul Cristian Huntemann, Marcus Paţilea, Cătălin |
author_facet |
Scarlat, Raul Cristian Huntemann, Marcus Paţilea, Cătălin |
author_sort |
Scarlat, Raul Cristian |
title |
Sea Ice Concentration and thin Sea Ice Thickness in the Arctic retrieved with different configurations of an Optimal Estimation Method ... |
title_short |
Sea Ice Concentration and thin Sea Ice Thickness in the Arctic retrieved with different configurations of an Optimal Estimation Method ... |
title_full |
Sea Ice Concentration and thin Sea Ice Thickness in the Arctic retrieved with different configurations of an Optimal Estimation Method ... |
title_fullStr |
Sea Ice Concentration and thin Sea Ice Thickness in the Arctic retrieved with different configurations of an Optimal Estimation Method ... |
title_full_unstemmed |
Sea Ice Concentration and thin Sea Ice Thickness in the Arctic retrieved with different configurations of an Optimal Estimation Method ... |
title_sort |
sea ice concentration and thin sea ice thickness in the arctic retrieved with different configurations of an optimal estimation method ... |
publisher |
PANGAEA |
publishDate |
2020 |
url |
https://dx.doi.org/10.1594/pangaea.912748 https://doi.pangaea.de/10.1594/PANGAEA.912748 |
genre |
Sea ice |
genre_facet |
Sea ice |
op_relation |
https://dx.doi.org/10.1029/2019jc015749 https://dx.doi.org/10.1109/igarss.2014.6947266 https://dx.doi.org/10.1109/jstars.2017.2739858 |
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.91274810.1029/2019jc01574910.1109/igarss.2014.694726610.1109/jstars.2017.2739858 |
_version_ |
1810475864156012544 |