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Assimilation of combining satellite remote sensing and in-situ observation based on space-time four-dimensional multi-scale analysis method
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Hanyu Liu1, Wei Li1, 2, *, Guijun Han1, *, Qi Shao1, Kangzhuang Liang2, Yan Hu1, Ru Wang1, Song Hu1
Haiyang Xuebao | 2023, 45(7) : 110 - 125
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Haiyang Xuebao | 2023, 45(7): 110-125
Article
Assimilation of combining satellite remote sensing and in-situ observation based on space-time four-dimensional multi-scale analysis method
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Hanyu Liu1, Wei Li1, 2, *, Guijun Han1, *, Qi Shao1, Kangzhuang Liang2, Yan Hu1, Ru Wang1, Song Hu1
Affiliations
  • 1Tianjin Key Laboratory for Marine Environmental Research and Service, School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
  • 2Tianjin Key Laboratory for Oceanic Meteorology, Tianjin 300074, China
Published: 2023-07-01 doi: 10.12284/hyxb2023111
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Fully integrating the use of satellite remote sensing and in-situ observation information to build a high-quality underwater temperature and salinity fields is a frontier topic in the development of marine scientific research. Currently, the vast majority of assimilation systems use assimilation schemes that require the assumption that elements have some artificially predetermined relationship between the surface and underwater. As a result, the temperature and salinity analysis field was more man-made and could not objectively reflect the real state of the ocean. This paper proposed a scheme that did not make any relationship assumptions, only relied on the complementary effects of different types of observation data in time and space, and combined the satellite remote sensing and in-situ observations for spatio-temporal four-dimensional multiscale objective analysis. Compared with the experimental results of assimilating these two types of observation data separately, it was found that the method could not only obtain more accurate temperature and salinity profile structure characteristics, but also reflect the detailed information of the mesoscale changes in the sea surface, the multi-scale information in the observation data was extracted to the maximum, and the effect of “1 + 1 > 2” was realized, and constructed a completely objective temperature and salinity analysis field. The results also showed that assimilating satellite remote sensing sea surface salinity data can effectively improve the analysis of temperature and salinity, which proved that the role of satellite remote sensing sea surface salinity observation on temperature and salinity assimilation could not be ignored.

multi-scale analysis  /  satellite remote sensing  /  field observation  /  multi-grid four-dimensional variational method
Hanyu Liu, Wei Li, Guijun Han, Qi Shao, Kangzhuang Liang, Yan Hu, Ru Wang, Song Hu. Assimilation of combining satellite remote sensing and in-situ observation based on space-time four-dimensional multi-scale analysis method[J]. Haiyang Xuebao, 2023 , 45 (7) : 110 -125 . DOI: 10.12284/hyxb2023111
Year 2023 volume 45 Issue 7
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Article Info
doi: 10.12284/hyxb2023111
  • Receive Date:2022-10-01
  • Online Date:2025-12-28
  • Published:2023-07-01
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  • Received:2022-10-01
  • Revised:2022-12-03
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    1Tianjin Key Laboratory for Marine Environmental Research and Service, School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
    2Tianjin Key Laboratory for Oceanic Meteorology, Tianjin 300074, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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