A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016)
Monitoring sea surface salinity (SSS) and density variations is crucial to investigate the global water cycle and the ocean dynamics, and to analyse how they are impacted by climate change. Historically, ocean salinity and density have suffered a poor observational coverage, which hindered an accura...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2018-03-01
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| Series: | Frontiers in Marine Science |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fmars.2018.00084/full |
| _version_ | 1819130897688952832 |
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| author | Riccardo Droghei Bruno Buongiorno Nardelli Rosalia Santoleri |
| author_facet | Riccardo Droghei Bruno Buongiorno Nardelli Rosalia Santoleri |
| author_sort | Riccardo Droghei |
| collection | DOAJ |
| description | Monitoring sea surface salinity (SSS) and density variations is crucial to investigate the global water cycle and the ocean dynamics, and to analyse how they are impacted by climate change. Historically, ocean salinity and density have suffered a poor observational coverage, which hindered an accurate assessment of their surface patterns, as well as of associated space and time variability and trends. Different approaches have thus been proposed to extend the information obtained from sparse in situ measurements and provide gap-free fields at regular spatial and temporal resolution, based on the combination of in situ and satellite data. In the framework of the Copernicus Marine Environment Monitoring Service, a daily (weekly sampled) global reprocessed dataset at ¼° × ¼° resolution has been produced by modifying a multivariate optimal interpolation (OI) technique originally developed within MyOcean project. The algorithm has been applied to in situ salinity/density measurements covering the period from 1993 to 2016, using satellite sea surface temperature differences to constrain the surface patterns. This improved algorithm and the new dataset are described and validated here with holdout approach and independent data. |
| first_indexed | 2024-12-22T09:06:55Z |
| format | Article |
| id | doaj.art-dad9dd7ecf614f61baed2109f8c33d2f |
| institution | Directory Open Access Journal |
| issn | 2296-7745 |
| language | English |
| last_indexed | 2024-12-22T09:06:55Z |
| publishDate | 2018-03-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Marine Science |
| spelling | doaj.art-dad9dd7ecf614f61baed2109f8c33d2f2022-12-21T18:31:34ZengFrontiers Media S.A.Frontiers in Marine Science2296-77452018-03-01510.3389/fmars.2018.00084309198A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016)Riccardo Droghei0Bruno Buongiorno Nardelli1Rosalia Santoleri2Institute of Atmospheric Sciences and Climate, Rome, ItalyCNR, Istituto per l'Ambiente Marino Costiero, Naples, ItalyInstitute of Atmospheric Sciences and Climate, Rome, ItalyMonitoring sea surface salinity (SSS) and density variations is crucial to investigate the global water cycle and the ocean dynamics, and to analyse how they are impacted by climate change. Historically, ocean salinity and density have suffered a poor observational coverage, which hindered an accurate assessment of their surface patterns, as well as of associated space and time variability and trends. Different approaches have thus been proposed to extend the information obtained from sparse in situ measurements and provide gap-free fields at regular spatial and temporal resolution, based on the combination of in situ and satellite data. In the framework of the Copernicus Marine Environment Monitoring Service, a daily (weekly sampled) global reprocessed dataset at ¼° × ¼° resolution has been produced by modifying a multivariate optimal interpolation (OI) technique originally developed within MyOcean project. The algorithm has been applied to in situ salinity/density measurements covering the period from 1993 to 2016, using satellite sea surface temperature differences to constrain the surface patterns. This improved algorithm and the new dataset are described and validated here with holdout approach and independent data.http://journal.frontiersin.org/article/10.3389/fmars.2018.00084/fullglobal datasetssea surface salinitysea surface densitymultivariate optimal interpolationmesoscale resolvingin situ and satellite data |
| spellingShingle | Riccardo Droghei Bruno Buongiorno Nardelli Rosalia Santoleri A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016) Frontiers in Marine Science global datasets sea surface salinity sea surface density multivariate optimal interpolation mesoscale resolving in situ and satellite data |
| title | A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016) |
| title_full | A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016) |
| title_fullStr | A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016) |
| title_full_unstemmed | A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016) |
| title_short | A New Global Sea Surface Salinity and Density Dataset From Multivariate Observations (1993–2016) |
| title_sort | new global sea surface salinity and density dataset from multivariate observations 1993 2016 |
| topic | global datasets sea surface salinity sea surface density multivariate optimal interpolation mesoscale resolving in situ and satellite data |
| url | http://journal.frontiersin.org/article/10.3389/fmars.2018.00084/full |
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