| Project/Area Number |
16K13882
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| Research Category |
Grant-in-Aid for Challenging Exploratory Research
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| Allocation Type | Multi-year Fund |
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| Research Field |
Meteorology/Physical oceanography/Hydrology
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| Research Institution | National Institute for Environmental Studies (2018)
Kobe University (2016-2017) |
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Principal Investigator |
NAKADA Satoshi 国立研究開発法人国立環境研究所, 地域環境研究センター, 特別研究員 (70540871)
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| Co-Investigator(Kenkyū-buntansha) |
小林 志保 京都大学, フィールド科学教育研究センター, 助教 (60432340)
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| Project Period (FY) |
2016-04-01 – 2019-03-31
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| Project Status |
Completed (Fiscal Year 2018)
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| Budget Amount *help |
¥3,510,000 (Direct Cost: ¥2,700,000、Indirect Cost: ¥810,000)
Fiscal Year 2018: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2017: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
Fiscal Year 2016: ¥1,430,000 (Direct Cost: ¥1,100,000、Indirect Cost: ¥330,000)
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| Keywords | 有色溶存有機物 / 河川プリューム / 静止海色衛星 / 海面塩分 / 大阪湾 / 瀬戸内海 / データ同化 / グリーン関数 / 海表面塩分 / 陸海統合モデル |
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| Outline of Final Research Achievements |
Traditional satellite sensors to measure sea-surface salinity (SSS) have coarse resolutions and large errors to capture riverine plumes in coastal oceans. To accurately observe SSS patterns in coastal oceans, high temporal and spatial resolutions are required. We produce hourly, high-resolution (approx. 500 m) SSS maps in Seto Inland Sea based on the close relationship between SSS and chromophoric dissolved organic matter (CDOM) data collected by the Geostationary Ocean Color Imager (GOCI). However, the derived SSS maps frequently have data losses due to cloud cover and no data in nighttime, leading to difficult analysis of river plumes in coastal oceans. The data assimilation method was developed to interpolate SSS maps based on Green function method, in Osaka Bay selected as a pilot region.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究は、静止海色衛星データから沿岸域における海面塩分を高分解能で知ることができる世界初の試みである。毎時の海面塩分マップが得られれば河川プリュームを時々刻々追うことができ、海洋生態系や漁場環境への影響を推測できる。海面水温データが充実したことで海洋学や気象学が発展したように、海面塩分データを充実させ、次世代の気象学・水文学・海洋学へと貢献できると期待している。例えば、水文研究者は河川流量の検証のために、海面塩分データを利活用できるだろう。
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