IAP observational salinity gridded dataset at 0.25 resolution ... : IAP observational salinity gridded dataset at 0.25 resolution ...
This product used a machine learning approach (feed-forward neural network - FFNN) to reconstruct a high-resolution (0.25° × 0.25°) ocean subsurface (1–2000 m) salinity dataset for the period 1993–2018 by merging in situ salinity profile observations with high-resolution (0.25° × 0.25°) satellite re...
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Online Access: | https://dx.doi.org/10.57760/sciencedb.o00122.00001 https://www.scidb.cn/en/detail?dataSetId=cb35a4b7ddc2466faec736da916b5106 |
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ftdatacite:10.57760/sciencedb.o00122.00001 2023-12-03T10:13:27+01:00 IAP observational salinity gridded dataset at 0.25 resolution ... : IAP observational salinity gridded dataset at 0.25 resolution ... Lijing Cheng 2022 https://dx.doi.org/10.57760/sciencedb.o00122.00001 https://www.scidb.cn/en/detail?dataSetId=cb35a4b7ddc2466faec736da916b5106 en eng Science Data Bank Creative Commons Attribution 4.0 International https://creativecommons.org/licenses/by/4.0/legalcode cc-by-4.0 Earth science Salinity Global Physical oceanography Climate change dataset Dataset 2022 ftdatacite https://doi.org/10.57760/sciencedb.o00122.00001 2023-11-03T10:26:13Z This product used a machine learning approach (feed-forward neural network - FFNN) to reconstruct a high-resolution (0.25° × 0.25°) ocean subsurface (1–2000 m) salinity dataset for the period 1993–2018 by merging in situ salinity profile observations with high-resolution (0.25° × 0.25°) satellite remote sensing altimetry absolute dynamic topography (ADT), sea surface temperature (SST), sea surface wind (SSW) field data, and a coarse resolution (1° × 1°) gridded salinity product. The new 0.25° × 0.25° reconstruction shows more realistic spatial signals in the regions with strong mesoscale variations, e.g., the Gulf Stream, Kuroshio, and Antarctic Circumpolar Current regions, than the 1° × 1° resolution product, indicating the efficiency of the machine learning approach in bringing satellite observations together with in situ observations. The large-scale salinity patterns from 0.25° × 0.25° data are consistent with the 1° × 1°gridded salinity field, suggesting the persistence of the large-scale signals in the ... : This product used a machine learning approach (feed-forward neural network - FFNN) to reconstruct a high-resolution (0.25° × 0.25°) ocean subsurface (1–2000 m) salinity dataset for the period 1993–2018 by merging in situ salinity profile observations with high-resolution (0.25° × 0.25°) satellite remote sensing altimetry absolute dynamic topography (ADT), sea surface temperature (SST), sea surface wind (SSW) field data, and a coarse resolution (1° × 1°) gridded salinity product. The new 0.25° × 0.25° reconstruction shows more realistic spatial signals in the regions with strong mesoscale variations, e.g., the Gulf Stream, Kuroshio, and Antarctic Circumpolar Current regions, than the 1° × 1° resolution product, indicating the efficiency of the machine learning approach in bringing satellite observations together with in situ observations. The large-scale salinity patterns from 0.25° × 0.25° data are consistent with the 1° × 1°gridded salinity field, suggesting the persistence of the large-scale signals in the ... Dataset Antarc* Antarctic DataCite Metadata Store (German National Library of Science and Technology) Antarctic |
institution |
Open Polar |
collection |
DataCite Metadata Store (German National Library of Science and Technology) |
op_collection_id |
ftdatacite |
language |
English |
topic |
Earth science Salinity Global Physical oceanography Climate change |
spellingShingle |
Earth science Salinity Global Physical oceanography Climate change Lijing Cheng IAP observational salinity gridded dataset at 0.25 resolution ... : IAP observational salinity gridded dataset at 0.25 resolution ... |
topic_facet |
Earth science Salinity Global Physical oceanography Climate change |
description |
This product used a machine learning approach (feed-forward neural network - FFNN) to reconstruct a high-resolution (0.25° × 0.25°) ocean subsurface (1–2000 m) salinity dataset for the period 1993–2018 by merging in situ salinity profile observations with high-resolution (0.25° × 0.25°) satellite remote sensing altimetry absolute dynamic topography (ADT), sea surface temperature (SST), sea surface wind (SSW) field data, and a coarse resolution (1° × 1°) gridded salinity product. The new 0.25° × 0.25° reconstruction shows more realistic spatial signals in the regions with strong mesoscale variations, e.g., the Gulf Stream, Kuroshio, and Antarctic Circumpolar Current regions, than the 1° × 1° resolution product, indicating the efficiency of the machine learning approach in bringing satellite observations together with in situ observations. The large-scale salinity patterns from 0.25° × 0.25° data are consistent with the 1° × 1°gridded salinity field, suggesting the persistence of the large-scale signals in the ... : This product used a machine learning approach (feed-forward neural network - FFNN) to reconstruct a high-resolution (0.25° × 0.25°) ocean subsurface (1–2000 m) salinity dataset for the period 1993–2018 by merging in situ salinity profile observations with high-resolution (0.25° × 0.25°) satellite remote sensing altimetry absolute dynamic topography (ADT), sea surface temperature (SST), sea surface wind (SSW) field data, and a coarse resolution (1° × 1°) gridded salinity product. The new 0.25° × 0.25° reconstruction shows more realistic spatial signals in the regions with strong mesoscale variations, e.g., the Gulf Stream, Kuroshio, and Antarctic Circumpolar Current regions, than the 1° × 1° resolution product, indicating the efficiency of the machine learning approach in bringing satellite observations together with in situ observations. The large-scale salinity patterns from 0.25° × 0.25° data are consistent with the 1° × 1°gridded salinity field, suggesting the persistence of the large-scale signals in the ... |
format |
Dataset |
author |
Lijing Cheng |
author_facet |
Lijing Cheng |
author_sort |
Lijing Cheng |
title |
IAP observational salinity gridded dataset at 0.25 resolution ... : IAP observational salinity gridded dataset at 0.25 resolution ... |
title_short |
IAP observational salinity gridded dataset at 0.25 resolution ... : IAP observational salinity gridded dataset at 0.25 resolution ... |
title_full |
IAP observational salinity gridded dataset at 0.25 resolution ... : IAP observational salinity gridded dataset at 0.25 resolution ... |
title_fullStr |
IAP observational salinity gridded dataset at 0.25 resolution ... : IAP observational salinity gridded dataset at 0.25 resolution ... |
title_full_unstemmed |
IAP observational salinity gridded dataset at 0.25 resolution ... : IAP observational salinity gridded dataset at 0.25 resolution ... |
title_sort |
iap observational salinity gridded dataset at 0.25 resolution ... : iap observational salinity gridded dataset at 0.25 resolution ... |
publisher |
Science Data Bank |
publishDate |
2022 |
url |
https://dx.doi.org/10.57760/sciencedb.o00122.00001 https://www.scidb.cn/en/detail?dataSetId=cb35a4b7ddc2466faec736da916b5106 |
geographic |
Antarctic |
geographic_facet |
Antarctic |
genre |
Antarc* Antarctic |
genre_facet |
Antarc* Antarctic |
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/10.57760/sciencedb.o00122.00001 |
_version_ |
1784260226415853568 |