Key Laboratory of Research on Marine Hazards Forecasting, National
Marine Environmental Forecasting Center, Ministry of Natural Resources,
Beijing, 100081, China
Jiang Zhu
Institute of Atmospheric Physics, Chinese Academy of Sciences,
Beijing, 100029, China
Junqiang Song
College of Meteorology and Oceanography, National University of
Defense Technology, Changsha, 410073, China
Hongze Leng
College of Meteorology and Oceanography, National University of
Defense Technology, Changsha, 410073, China
Viewed
Total article views: 5,158 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
3,793
1,249
116
5,158
204
118
140
HTML: 3,793
PDF: 1,249
XML: 116
Total: 5,158
Supplement: 204
BibTeX: 118
EndNote: 140
Views and downloads (calculated since 19 Jul 2022)
Cumulative views and downloads
(calculated since 19 Jul 2022)
Total article views: 4,122 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
3,179
844
99
4,122
204
103
126
HTML: 3,179
PDF: 844
XML: 99
Total: 4,122
Supplement: 204
BibTeX: 103
EndNote: 126
Views and downloads (calculated since 18 Nov 2022)
Cumulative views and downloads
(calculated since 18 Nov 2022)
Total article views: 1,036 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
614
405
17
1,036
15
14
HTML: 614
PDF: 405
XML: 17
Total: 1,036
BibTeX: 15
EndNote: 14
Views and downloads (calculated since 19 Jul 2022)
Cumulative views and downloads
(calculated since 19 Jul 2022)
Viewed (geographical distribution)
Total article views: 5,158 (including HTML, PDF, and XML)
Thereof 4,954 with geography defined
and 204 with unknown origin.
Total article views: 4,122 (including HTML, PDF, and XML)
Thereof 3,992 with geography defined
and 130 with unknown origin.
Total article views: 1,036 (including HTML, PDF, and XML)
Thereof 962 with geography defined
and 74 with unknown origin.
A high-resolution gridded dataset is crucial for understanding ocean processes at various spatiotemporal scales. Here we used a machine learning approach and successfully reconstructed a high-resolution (0.25° × 0.25°) ocean subsurface (1–2000 m) salinity dataset for the period 1993–2018 (monthly) by merging in situ salinity profile observations with high-resolution satellite remote-sensing data. This new product could be useful in various applications in ocean and climate fields.
A high-resolution gridded dataset is crucial for understanding ocean processes at various...
