The CMCC Eddy-permitting Global Ocean Physical Reanalysis (C-GLORS v5, 1980-2014), links to NetCDF files

The CMCC Global Ocean Physical Reanalysis System (C-GLORS) is used to simulate the state of the ocean in the last decades. It consists of a variational data assimilation system (OceanVar), capable of assimilating all in-situ observations along with altimetry data, and a forecast step performed by th...

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Main Authors: Storto, Andrea, Masina, Simona
Format: Dataset
Language:English
Published: PANGAEA 2016
Subjects:
Online Access:https://doi.pangaea.de/10.1594/PANGAEA.857995
https://doi.org/10.1594/PANGAEA.857995
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spelling ftpangaea:oai:pangaea.de:doi:10.1594/PANGAEA.857995 2024-09-15T18:35:27+00:00 The CMCC Eddy-permitting Global Ocean Physical Reanalysis (C-GLORS v5, 1980-2014), links to NetCDF files Storto, Andrea Masina, Simona DATE/TIME START: 1980-01-01T00:00:00 * DATE/TIME END: 2014-12-31T23:59:59 2016 text/tab-separated-values, 2841 data points https://doi.pangaea.de/10.1594/PANGAEA.857995 https://doi.org/10.1594/PANGAEA.857995 en eng PANGAEA CMCC Global Ocean Reanalysis System (C-GLORS), external website [webpage]. http://c-glors.cmcc.it/index/index.html Good, Simon A; Martin, Matthew J; Rayner, Nick A (2013): EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. Journal of Geophysical Research: Oceans, 118(12), 6704-6716, https://doi.org/10.1002/2013JC009067 Madec, Gurvan (2015): NEMO ocean engine. Note du Pole de modélisation de l' Institut Pierre-Simon Laplace, Paris, France, 27, 401 pp, hdl:10013/epic.46840.d001 Markus, Thorsten; Cavalieri, Donald J (2000): An enhancement of the NASA Team sea ice algorithm. IEEE Transactions on Geoscience and Remote Sensing, 38(3), 1387-1398, https://doi.org/10.1109/36.843033 Reynolds, Richard W; Smith, Thomas M; Liu, Chunying; Chelton, Dudley B; Casey, Kenneth S; Schlax, Michael G (2007): Daily High-Resolution-Blended analyses for sea surface temperature. Journal of Climate, 20(22), 5473-5496, https://doi.org/10.1175/2007JCLI1824.1 Storto, Andrea; Masina, Simona; Navarra, Antonio (2015): Evaluation of the CMCC eddy-permitting global ocean physical reanalysis system (C-GLORS, 1982-2012) and its assimilation components. Quarterly Journal of the Royal Meteorological Society, 21 pp, https://doi.org/10.1002/qj.2673 Zhang, Jinlun; Rothrock, Drew A (2003): Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates. Monthly Weather Review, 131(5), 845-861, https://doi.org/10.1175/1520-0493(2003)131%3C0845:MGSIWA%3E2.0.CO;2 All C-GLORS v5 fíles in one zip archive (reduced resolution, 5.6 GB) (URI: https://hs.pangaea.de/model/C-GLORSv5/C-GLORSv5.zip) https://doi.pangaea.de/10.1594/PANGAEA.857995 https://doi.org/10.1594/PANGAEA.857995 CC-BY-3.0: Creative Commons Attribution 3.0 Unported Access constraints: unrestricted info:eu-repo/semantics/openAccess Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna, Italy DATE/TIME File name File size Uniform resource locator/link to file dataset 2016 ftpangaea https://doi.org/10.1594/PANGAEA.85799510.1002/2013JC00906710.1109/36.84303310.1175/2007JCLI1824.110.1002/qj.267310.1175/1520-0493(2003)131%3C0845:MGSIWA%3E2.0.CO;2 2024-07-24T02:31:33Z The CMCC Global Ocean Physical Reanalysis System (C-GLORS) is used to simulate the state of the ocean in the last decades. It consists of a variational data assimilation system (OceanVar), capable of assimilating all in-situ observations along with altimetry data, and a forecast step performed by the ocean model NEMO coupled with the LIM2 sea-ice model. KEY STRENGTHS: - Data are available for a large number of ocean parameters - An extensive validation has been conducted and is freely available - The reanalysis is performed at high resolution (1/4 degree) and spans the last 30 years KEY LIMITATIONS: - Quality may be discontinuos and depend on observation coverage - Uncertainty estimates are simply derived through verification skill scores Note (2017-02-09): C-GLORSv5 is dissemniated on PANGAEA at reduced resolution, i.e. a regular 0.5x0.5 degree grid. For full resolution data sets see "CMCC Global Ocean Reanalysis System (C-GLORS), external website" (Related to:) Dataset Sea ice PANGAEA - Data Publisher for Earth & Environmental Science
institution Open Polar
collection PANGAEA - Data Publisher for Earth & Environmental Science
op_collection_id ftpangaea
language English
topic DATE/TIME
File name
File size
Uniform resource locator/link to file
spellingShingle DATE/TIME
File name
File size
Uniform resource locator/link to file
Storto, Andrea
Masina, Simona
The CMCC Eddy-permitting Global Ocean Physical Reanalysis (C-GLORS v5, 1980-2014), links to NetCDF files
topic_facet DATE/TIME
File name
File size
Uniform resource locator/link to file
description The CMCC Global Ocean Physical Reanalysis System (C-GLORS) is used to simulate the state of the ocean in the last decades. It consists of a variational data assimilation system (OceanVar), capable of assimilating all in-situ observations along with altimetry data, and a forecast step performed by the ocean model NEMO coupled with the LIM2 sea-ice model. KEY STRENGTHS: - Data are available for a large number of ocean parameters - An extensive validation has been conducted and is freely available - The reanalysis is performed at high resolution (1/4 degree) and spans the last 30 years KEY LIMITATIONS: - Quality may be discontinuos and depend on observation coverage - Uncertainty estimates are simply derived through verification skill scores Note (2017-02-09): C-GLORSv5 is dissemniated on PANGAEA at reduced resolution, i.e. a regular 0.5x0.5 degree grid. For full resolution data sets see "CMCC Global Ocean Reanalysis System (C-GLORS), external website" (Related to:)
format Dataset
author Storto, Andrea
Masina, Simona
author_facet Storto, Andrea
Masina, Simona
author_sort Storto, Andrea
title The CMCC Eddy-permitting Global Ocean Physical Reanalysis (C-GLORS v5, 1980-2014), links to NetCDF files
title_short The CMCC Eddy-permitting Global Ocean Physical Reanalysis (C-GLORS v5, 1980-2014), links to NetCDF files
title_full The CMCC Eddy-permitting Global Ocean Physical Reanalysis (C-GLORS v5, 1980-2014), links to NetCDF files
title_fullStr The CMCC Eddy-permitting Global Ocean Physical Reanalysis (C-GLORS v5, 1980-2014), links to NetCDF files
title_full_unstemmed The CMCC Eddy-permitting Global Ocean Physical Reanalysis (C-GLORS v5, 1980-2014), links to NetCDF files
title_sort cmcc eddy-permitting global ocean physical reanalysis (c-glors v5, 1980-2014), links to netcdf files
publisher PANGAEA
publishDate 2016
url https://doi.pangaea.de/10.1594/PANGAEA.857995
https://doi.org/10.1594/PANGAEA.857995
op_coverage DATE/TIME START: 1980-01-01T00:00:00 * DATE/TIME END: 2014-12-31T23:59:59
genre Sea ice
genre_facet Sea ice
op_source Centro Euro-Mediterraneo sui Cambiamenti Climatici, Bologna, Italy
op_relation CMCC Global Ocean Reanalysis System (C-GLORS), external website [webpage]. http://c-glors.cmcc.it/index/index.html
Good, Simon A; Martin, Matthew J; Rayner, Nick A (2013): EN4: Quality controlled ocean temperature and salinity profiles and monthly objective analyses with uncertainty estimates. Journal of Geophysical Research: Oceans, 118(12), 6704-6716, https://doi.org/10.1002/2013JC009067
Madec, Gurvan (2015): NEMO ocean engine. Note du Pole de modélisation de l' Institut Pierre-Simon Laplace, Paris, France, 27, 401 pp, hdl:10013/epic.46840.d001
Markus, Thorsten; Cavalieri, Donald J (2000): An enhancement of the NASA Team sea ice algorithm. IEEE Transactions on Geoscience and Remote Sensing, 38(3), 1387-1398, https://doi.org/10.1109/36.843033
Reynolds, Richard W; Smith, Thomas M; Liu, Chunying; Chelton, Dudley B; Casey, Kenneth S; Schlax, Michael G (2007): Daily High-Resolution-Blended analyses for sea surface temperature. Journal of Climate, 20(22), 5473-5496, https://doi.org/10.1175/2007JCLI1824.1
Storto, Andrea; Masina, Simona; Navarra, Antonio (2015): Evaluation of the CMCC eddy-permitting global ocean physical reanalysis system (C-GLORS, 1982-2012) and its assimilation components. Quarterly Journal of the Royal Meteorological Society, 21 pp, https://doi.org/10.1002/qj.2673
Zhang, Jinlun; Rothrock, Drew A (2003): Modeling global sea ice with a thickness and enthalpy distribution model in generalized curvilinear coordinates. Monthly Weather Review, 131(5), 845-861, https://doi.org/10.1175/1520-0493(2003)131%3C0845:MGSIWA%3E2.0.CO;2
All C-GLORS v5 fíles in one zip archive (reduced resolution, 5.6 GB) (URI: https://hs.pangaea.de/model/C-GLORSv5/C-GLORSv5.zip)
https://doi.pangaea.de/10.1594/PANGAEA.857995
https://doi.org/10.1594/PANGAEA.857995
op_rights CC-BY-3.0: Creative Commons Attribution 3.0 Unported
Access constraints: unrestricted
info:eu-repo/semantics/openAccess
op_doi https://doi.org/10.1594/PANGAEA.85799510.1002/2013JC00906710.1109/36.84303310.1175/2007JCLI1824.110.1002/qj.267310.1175/1520-0493(2003)131%3C0845:MGSIWA%3E2.0.CO;2
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