2019 Fiscal Year Annual Research Report
Tsunami Data Assimilation With Sparse Observations: Improvement Towards Tsunami Warning System
| Project/Area Number |
19J20293
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| Research Institution | The University of Tokyo |
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Principal Investigator |
WANG YUCHEN 東京大学, 理学系研究科, 特別研究員(DC1) (80943290)
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| Project Period (FY) |
2019-04-25 – 2022-03-31
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| Keywords | Tsunami Forecasting / Data Assimilation / Tsunami Resonance |
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| Outline of Annual Research Achievements |
In the past one year, I have been working on the tsunami data assimilation. To reduce the cost of tsunami early warning, I propose a modified method of tsunami data assimilation for regions with a sparse observation network. The method utilizes interpolated waveforms at virtual stations. The tsunami waveforms at the virtual stations between two existing observation stations are estimated by shifting arrival times with the linear interpolation of observed arrival times and by correcting the amplitudes for their water depths. In the new data assimilation approach, The application to the 2004 Sumatra-Andaman earthquake and the 2009 Dusky Sound, New Zealand, earthquake reveals that addition of virtual stations greatly help improve the tsunami forecasting accuracy.
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| Current Status of Research Progress |
Current Status of Research Progress
1: Research has progressed more than it was originally planned.
Reason
The method of tsunami data assimilation without a dense observational network has been proposed and applied successfully. In addition to this achievement, I am also working on the optimal design of Ocean Bottom Pressure Gauges (OBPGs) for tsunami data assimilation, aimed at achieving the highest forecast accuracy with a given number of stations. This part is in collaboration with researchers from Centro de Investigacion para la Gestion del Riesgo de Desastres (CIGIDEN), Chile.
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| Strategy for Future Research Activity |
My next step is to work on the algorithm of real-time tsunami detection. Real-time tsunami detection is very important to the practice of tsunami data assimilation approach. I will propose a method of real-time tsunami detection using Ensemble Empirical Mode Decomposition (EEMD). EEMD decomposes the time series into a set of intrinsic mode functions (IMFs) adaptively. The tsunami signals of OBPGs are automatically separated from the tidal signals, seismic signals, as well as background noise. Unlike the traditional tsunami detection methods, the algorithm does not need to make a prediction of tides. It will help detect the tsunami with a short detection delay, and characterize the tsunami amplitudes accurately. Therefore, it is applicable for the tsunami data assimilation approach.
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