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
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Total article views: 4,733 (including HTML, PDF, and XML)
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3,589
1,024
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4,733
229
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152
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XML: 120
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BibTeX: 121
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641
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Viewed (geographical distribution)
Total article views: 5,851 (including HTML, PDF, and XML)
Thereof 5,657 with geography defined
and 194 with unknown origin.
Total article views: 4,733 (including HTML, PDF, and XML)
Thereof 4,610 with geography defined
and 123 with unknown origin.
Total article views: 1,118 (including HTML, PDF, and XML)
Thereof 1,047 with geography defined
and 71 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...
