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dc.contributor.author | Miyashita, Takuya | en |
dc.contributor.author | Nishino, Ai | en |
dc.contributor.author | Ho, Tung-Cheng | en |
dc.contributor.author | Yasuda, Tomohiro | en |
dc.contributor.author | Mori, Nobuhito | en |
dc.contributor.author | Shimura, Tomoya | en |
dc.contributor.author | Fukui, Nobuki | en |
dc.contributor.alternative | 宮下, 卓也 | ja |
dc.contributor.alternative | 森, 信人 | ja |
dc.contributor.alternative | 志村, 智也 | ja |
dc.date.accessioned | 2023-10-20T00:19:06Z | - |
dc.date.available | 2023-10-20T00:19:06Z | - |
dc.date.issued | 2023-09 | - |
dc.identifier.uri | http://hdl.handle.net/2433/285563 | - |
dc.description.abstract | The 2022 Hunga Tonga-Hunga Ha’apai eruption generated tsunamis that propagated across the Pacific Ocean. Along the coast of Japan, nearshore amplification led to amplitudes of nearly 1 m at some locations, with varying peak tsunami occurrence times. The leading tsunami wave can generally be reproduced by Lamb waves, which are a type of air-pressure wave generated by an eruption. However, subsequent tsunamis that occurred several hours after the leading wave tended to be larger for unknown reasons. This study performs multi-scale numerical simulations to investigate subsequent tsunami waves in the vicinity of Japan induced by air pressure waves caused by the eruption. The atmospheric pressure field was created using a dispersion relation of atmospheric gravity wave and tuned by physical parameters based on observational records. The tsunami simulations used the adaptive mesh refinement method, incorporating detailed bathymetry and topography to solve the tsunami at various spatial scales. The simulations effectively reproduced the tsunami waveforms observed at numerous coastal locations, and results indicate that the factors contributing to the maximum tsunami amplitude differ by region. In particular, bay resonance plays a major role in determining the maximum amplitude at many sites along the east coast of Japan. However, large tsunami amplification at some west coast locations was not replicated, probably because it was caused by amplification during oceanic wave propagation rather than meteorological factors. These findings enhance our understanding of meteotsunami complexity and help distinguish tsunami amplification factors. | en |
dc.language.iso | eng | - |
dc.publisher | Springer Nature | en |
dc.rights | © 2023 The Author(s), corrected publication 2023 | en |
dc.rights | This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. | en |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | - |
dc.subject | 2022 Hunga Tonga-Hunga Ha’apai eruption and tsunami | en |
dc.subject | meteotsunami | en |
dc.subject | far-field tsunami | en |
dc.subject | multi-scale tsunami simulation | en |
dc.title | Multi-scale Simulation of Subsequent Tsunami Waves in Japan Excited by Air Pressure Waves Due to the 2022 Tonga Volcanic Eruption | en |
dc.type | journal article | - |
dc.type.niitype | Journal Article | - |
dc.identifier.jtitle | Pure and Applied Geophysics | en |
dc.identifier.volume | 180 | - |
dc.identifier.issue | 9 | - |
dc.identifier.spage | 3195 | - |
dc.identifier.epage | 3223 | - |
dc.relation.doi | 10.1007/s00024-023-03332-9 | - |
dc.textversion | publisher | - |
dcterms.accessRights | open access | - |
datacite.awardNumber | 20K22432 | - |
datacite.awardNumber | 23K13413 | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-20K22432/ | - |
datacite.awardNumber.uri | https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-23K13413/ | - |
dc.identifier.pissn | 0033-4553 | - |
dc.identifier.eissn | 1420-9136 | - |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.funderName | 日本学術振興会 | ja |
jpcoar.awardTitle | 津波即時予測のための周波数特性を考慮した地形による津波増幅効果の定量化 | ja |
jpcoar.awardTitle | 観測記録と数値実験を相互活用した地形による津波の周波数応答特性の推定 | ja |
出現コレクション: | 学術雑誌掲載論文等 |
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