EbookBell.com

Most ebook files are in PDF format, so you can easily read them using various software such as Foxit Reader or directly on the Google Chrome browser.
Some ebook files are released by publishers in other formats such as .awz, .mobi, .epub, .fb2, etc. You may need to install specific software to read these formats on mobile/PC, such as Calibre.

Please read the tutorial at this link: https://ebookbell.com/faq

We offer FREE conversion to the popular formats you request; however, this may take some time. Therefore, right after payment, please email us, and we will try to provide the service as quickly as possible.

For some exceptional file formats or broken links (if any), please refrain from opening any disputes. Instead, email us first, and we will try to assist within a maximum of 6 hours.

EbookBell Team

Tsunami Data Assimilation For Early Warning Yuchen Wang

Name: Tsunami Data Assimilation For Early Warning Yuchen Wang
SKU: BELL-46867850
Price: 31.00 USD
Availability: InStock
Rating: 4.4 (32 reviews)

SKU: BELL-46867850

$ 31.00 ~~$ 45.00~~ (-31%)

4.4

32 reviews

Tsunami Data Assimilation For Early Warning Yuchen Wang instant download after payment.

Publisher: Springer

File Extension: PDF

File size: 4.81 MB

Pages: 107

Author: Yuchen Wang

ISBN: 9789811973383, 9811973385

Language: English

Year: 2022

Product desciption

Tsunami Data Assimilation For Early Warning Yuchen Wang by Yuchen Wang 9789811973383, 9811973385 instant download after payment.

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.