Tsunami Data Assimilation for Early Warning
Language: English
Published by Springer Nature Singapore Okt 2023, 2023
- Softcover
- New
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermanyBuchWeltWeit Ludwig Meier e.K.
AbeBooks seller since January 11, 2012
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Add to basketItem description from seller
This item is printed on demand - it takes 3-4 days longer - Neuware -This book focuses on proposing a tsunami early warning system using data assimilation of offshore data. First, Green's Function-based Tsunami Data Assimilation (GFTDA) is proposed to reduce the computation time for assimilation. It can forecast the waveform at Points of Interest (PoIs) by superposing Green's functions between observational stations and PoIs. GFTDA achieves an equivalently high accuracy of tsunami forecasting to the previous approaches, while saving sufficient time to achieve an early warning. Second, a modified tsunami data assimilation method is explored for regions with a sparse observation network. The method uses interpolated waveforms at virtual stations to construct the complete wavefront for tsunami propagation. Its application to the 2009 Dusky Sound, New Zealand earthquake, and the 2015 Illapel earthquake revealed that adopting virtual stations greatly improved the tsunami forecasting accuracy for regions without a dense observation network. Finally, a real-time tsunami detection algorithm using Ensemble Empirical Mode Decomposition (EEMD) is presented. The tsunami signals of the offshore bottom pressure gauge can be automatically separated from the tidal components, seismic waves, and background noise. The algorithm could detect tsunami arrival with a short detection delay and accurately characterize the tsunami amplitude. Furthermore, the tsunami data assimilation approach is combined with the real-time tsunami detection algorithm, which is applied to the tsunami of the 2016 Fukushima earthquake. The proposed tsunami data assimilation approach can be put into practice with the help of the real-time tsunami detection algorithm. 116 pp. Englisch.…
Seller Inventory # 9789811973413
- Title
- Tsunami Data Assimilation for Early Warning
- Author
- Yuchen Wang
- Publisher
- Springer Nature Singapore Okt 2023
- Publication year
- 2023
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 9811973415
- ISBN 13
- 9789811973413
- Item weight
- 189 grams
- Dimensions
- 235x155x7 mm
"Synopsis" may belong to another edition of this title.
About the Author
Dr. Yuchen Wang is a postdoctoral researcher at Japan Agency for Marine-Earth Science and Technology. He received the bachelor’s degree in physics at Peking University. He received the master’s degree and Ph.D. degree in earth and planetary science at the University of Tokyo. His research interest is giant earthquakes and tsunamis. He has been working on tsunami early warning for disaster mitigation. He improved data assimilation algorithm to achieve a rapid and accuracy tsunami forecast. He has published 21 peer-reviewed journal articles and worked as the reviewer for 9 journals including Nature Communications, Journal of Geophysical Research: Solid Earth, and Natural Hazards and Earth System Sciences. He is the principal investigator of the KAKENHI 19J20203 on tsunami data assimilation sponsored by the Japan Society for the Promotion of Science. His research is in collaboration with researchers all over the world.
"About the title" may belong to another edition of this title.
BuchWeltWeit Ludwig Meier e.K.
Bergisch Gladbach, Germany
AbeBooks seller since January 11, 2012
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