研究課題/領域番号 |
20J21462
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研究種目 |
特別研究員奨励費
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配分区分 | 補助金 |
応募区分 | 国内 |
審査区分 |
小区分17020:大気水圏科学関連
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研究機関 | 東北大学 |
研究代表者 |
王 心月 東北大学, 理学研究科, 特別研究員(DC1)
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研究期間 (年度) |
2020-04-24 – 2023-03-31
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研究課題ステータス |
交付 (2022年度)
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配分額 *注記 |
2,500千円 (直接経費: 2,500千円)
2022年度: 800千円 (直接経費: 800千円)
2021年度: 800千円 (直接経費: 800千円)
2020年度: 900千円 (直接経費: 900千円)
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キーワード | deep neural network / Deep neural network / Cloud retrieval / Himawari-8 |
研究開始時の研究の概要 |
In this study, the synoptic-scale variations of the regional Hadley cell will be first depicted and analyzed over the tropical area by utilizing observation and reanalysis data, especially from a meridional perspective. Then its modulation effect on the initialization and intensification of the tropical cyclones will be explored. During the conduction of this project, a dataset of the deep convective cloud will be generated with machine learning method. The developed algorithm can be expected to pioneer the satellite-based nighttime retrieval of deep cloud.
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研究実績の概要 |
In this year, we developed a cloud retrieval model for the Himawari-8 infrared measurements, based on a deep neural network. The model has high accuracy when validated with the the active remote sensing datasets, it performs much better than physics-based retrieval models and can provide near-real time estimate of cloud properties such as cloud top height, cloud optical thickness, and cloud mask, which can be realistically applied to severe weather monitoring and mesoscale studies.
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現在までの達成度 (区分) |
現在までの達成度 (区分)
2: おおむね順調に進展している
理由
The research progressed as expected and the obtained results are satisfying.
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今後の研究の推進方策 |
In the future, the cloud properties retrieved by the newly developed model will be applied to case studies to investigate synoptic phenomena and reveal corresponding mechanisms.
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