Tsunami Warning Cancellation Using Data Assimilation Approach
Description:
Tsunami warning cancellation refers to the act of rescinding issued tsunami alerts when it is confirmed that the threat of a tsunami has diminished. This plays a crucial role in mitigating tsunami hazards, avoiding unnecessary economic constraints and the burden of sustained emergency responses. For example, after the Noto Earthquake occurred on January 1, 2024, the tsunami warning was not fully cancelled until 10 a.m. (JST) the next day, approximately 18 hours after the earthquake. Rescue personnel from outside can only enter the disaster area after the tsunami warning has been cancelled.
We investigated the predictability of tsunami warning cancellation using a novel approach based on data assimilation (Maeda et al., 2015). This approach has the potential to predict tsunamis independently of seismic source information and was applied to tsunami warning issuance (e.g., 2012 Haida Gwaii earthquake; Gusman et al., 2016). We adopted this approach to tsunami warning cancellation with the aim of developing a comprehensive warning process. The originality of our research is that we integrated tsunami data assimilation with a nonlinear wave model to accurately predict the characteristics of the later phase of tsunami waves.
We focused on the coastal areas of the Kii Peninsula, Japan, using the 2011 Tohoku earthquake as a case study. We assimilated tsunami data from 10 offshore stations (DONET) and predicted the tsunami waveforms at five coastal tide gauges. The results demonstrated significant consistency (over 80%) between the predicted and observed maximum height in the tsunami later phases, suggesting that data assimilation could provide valuable insights as a guideline for tsunami warning cancellations. In the future, we will extend our research to far-field tsunamis across the Pacific Ocean. Considering the topography of different regions including Japan, Alaska, and Hawaii, we will propose corresponding countermeasures for tsunami warning cancellation in each region.
Session: Data-driven and Computational Characterization of Non-earthquake Seismoacoustic Sources [Poster]
Type: Poster
Date: 4/16/2025
Presentation Time: 08:00 AM (local time)
Presenting Author: Yuchen
Student Presenter: No
Invited Presentation:
Poster Number: 84
Authors
Yuchen Wang Presenting Author Corresponding Author ywang@jamstec.go.jp Japan Agency for Marine-Earth Science and Technology |
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Tsunami Warning Cancellation Using Data Assimilation Approach
Session
Data-driven and Computational Characterization of Non-earthquake Seismoacoustic Sources