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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Bibliographic Details
Main Authors: Scarlat, Raul Cristian, Huntemann, Marcus, Paţilea, Cătălin
Format: Dataset
Language:English
Published: PANGAEA 2020
Subjects:
Online Access:https://dx.doi.org/10.1594/pangaea.912748
https://doi.pangaea.de/10.1594/PANGAEA.912748
id ftdatacite:10.1594/pangaea.912748
record_format openpolar
spelling 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
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