Tsunami Data Assimilation for Early Warning

This book details innovative methods for tsunami early warning, focusing on data assimilation techniques and real-time detection algorithms, specifically applied to the 2016 Fukushima earthquake.

The book Tsunami Data Assimilation for Early Warning presents a comprehensive approach to developing a tsunami early warning system through innovative data assimilation techniques. It emphasizes the integration of offshore data to enhance the accuracy and timeliness of tsunami forecasts. The author introduces Green’s Function-based Tsunami Data Assimilation (GFTDA), which significantly reduces computation time while maintaining high forecasting accuracy. By superposing Green’s functions between observational stations and Points of Interest (PoIs), GFTDA allows for effective tsunami waveform prediction, enabling earlier warnings for affected regions.

Additionally, the book explores a modified data assimilation method tailored for areas with sparse observation networks. This technique utilizes interpolated waveforms at virtual stations to create a complete wavefront for tsunami propagation. Case studies, including the 2009 Dusky Sound earthquake and the 2015 Illapel earthquake, demonstrate the effectiveness of this approach in improving forecasting accuracy in regions lacking dense observation systems. The author highlights the importance of virtual stations in enhancing tsunami detection capabilities.

Finally, Tsunami Data Assimilation for Early Warning introduces a real-time tsunami detection algorithm based on Ensemble Empirical Mode Decomposition (EEMD). This algorithm effectively separates tsunami signals from tidal components, seismic waves, and background noise, allowing for quick tsunami arrival detection with minimal delay. The integration of the tsunami data assimilation approach with this real-time detection algorithm is applied to the 2016 Fukushima earthquake, showcasing its practical applications in real-world scenarios.