| Project/Area Number |
16K05556
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Multi-year Fund |
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| Section | 一般 |
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| Research Field |
Meteorology/Physical oceanography/Hydrology
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| Research Institution | Japan, Meteorological Research Institute |
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Principal Investigator |
Sawada Masahiro 気象庁気象研究所, 台風・災害気象研究部, 併任(第一研究室) (90466524)
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| Co-Investigator(Kenkyū-buntansha) |
伊藤 耕介 琉球大学, 理学部, 准教授 (10634123)
山口 宗彦 気象庁気象研究所, 応用気象研究部, 主任研究官 (80595405)
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| Project Period (FY) |
2016-04-01 – 2020-03-31
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| Project Status |
Completed (Fiscal Year 2019)
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| Budget Amount *help |
¥4,680,000 (Direct Cost: ¥3,600,000、Indirect Cost: ¥1,080,000)
Fiscal Year 2018: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2017: ¥1,820,000 (Direct Cost: ¥1,400,000、Indirect Cost: ¥420,000)
Fiscal Year 2016: ¥1,040,000 (Direct Cost: ¥800,000、Indirect Cost: ¥240,000)
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| Keywords | 台風 / 数値モデル / 自然災害 |
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| Outline of Final Research Achievements |
We documented the strengths and weaknesses of three numerical models for tropical cyclone (TC) intensity forecast. The common issue is all models have a large error of intensity forecast of TC for the cases which experience rapid intensity changes. Each characteristics are: the statistical-dynamical model represents the smallest intensity forecast error as a statistical viewpoint, the simple axisymmetric model has a large forecast error but has a potential to reproducing the rapid intensification, and the numerical model could reproduce the temporal change of TC when it landfalls and re-intensifies.
We attempted to improve TC intensity forecast by modifying the initial condition for numerical model with atmospheric motion vector (AMV) derived from satellite observation. The results demonstrated the inclusion of AMV mitigates the slow intensification bias to some degree, but there remains the slow intensification bias.
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| Academic Significance and Societal Importance of the Research Achievements |
本研究を通じて台風強度予報における複数のモデルの強みと弱みを把握することができ、それらを台風強度予報で利用する際の指針が得られた。ここで得られた成果の一部は、気象庁における台風強度予報の5日先までへの延長に貢献した。これにより台風に伴う大雨や強風などの情報をこれまでより早い段階から提供することにより、防災関係機関などの対応より効果的に支援することが可能と期待される。
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