Tsunami Data Assimilation for Early Warning

This book proposes a tsunami early warning system using data assimilation of offshore data, including GFTDA, modified tsunami data assimilation, and real-time tsunami detection algorithms. It can forecast the waveform at Points of Interest (PoIs) by superposing Greens functions, improve tsunami forecasting accuracy for regions with a sparse observation network, and detect tsunami arrival with a short detection delay and accurately characterize the tsunami amplitude.

Format: Hardback
Length: 97 pages
Publication date: 27 October 2022
Publisher: Springer Verlag, Singapore

This book delves into the development of a tsunami early warning system through the utilization of offshore data assimilation techniques. Initially, the authors propose the Greens Function-based Tsunami Data Assimilation (GFTDA) method, aimed at expediting the computation time required for assimilation. By superimposing Greens functions between observational stations and Points of Interest (PoIs), GFTDA enables the forecasting of waveforms at these critical locations. Notably, GFTDA achieves comparable accuracy in tsunami forecasting to previous approaches, while significantly reducing the computation time necessary for early warning.

Furthermore, the book explores a modified tsunami data assimilation approach tailored for regions with a sparse observation network. This method employs interpolated waveforms at virtual stations to construct a comprehensive wavefront for tsunami propagation. The application of this method to the 2009 Dusky Sound, New Zealand earthquake, and the 2015 Illapel earthquake demonstrates its effectiveness in improving tsunami forecasting accuracy in areas with limited observation coverage.

To enhance the real-time detection of tsunamis, the authors present an Ensemble Empirical Mode Decomposition (EEMD)-based real-time tsunami detection algorithm. This algorithm effectively separates tsunami signals from tidal components, seismic waves, and background noise, enabling the automatic detection of tsunami arrival with a short detection delay. Moreover, the algorithm accurately characterizes the tsunami amplitude, providing valuable insights into the nature and magnitude of impending tsunamis.

In conclusion, this book offers a comprehensive exploration of tsunami early warning systems, highlighting the significance of data assimilation techniques in improving tsunami forecasting accuracy and enabling timely warning. The proposed GFTDA method, modified data assimilation approach, and real-time tsunami detection algorithm demonstrate the potential for developing robust and effective tsunami early warning systems that can safeguard communities vulnerable to coastal hazards.

Weight: 348g
Dimension: 235 x 155 (mm)
ISBN-13: 9789811973383
Edition number: 1st ed. 2022