Tsunami Data Assimilation With Sparse Observations: Improvement Towards Tsunami Warning System
| Project/Area Number |
19J20293
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| Research Category |
Grant-in-Aid for JSPS Fellows
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| Allocation Type | Single-year Grants |
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| Section | 国内 |
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| Review Section |
Basic Section 17040:Solid earth sciences-related
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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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| Project Status |
Completed (Fiscal Year 2021)
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| Budget Amount *help |
¥2,800,000 (Direct Cost: ¥2,800,000)
Fiscal Year 2021: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2020: ¥900,000 (Direct Cost: ¥900,000)
Fiscal Year 2019: ¥1,000,000 (Direct Cost: ¥1,000,000)
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| Keywords | Tsunami / Tsunami Early Warning / Data Assimilation / Bottom Pressure Gauge / Real-Time Detection / Resonance Analysis / Tsunami Forecasting / Tsunami Resonance |
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| Outline of Research at the Start |
Data assimilation is a method to combine observation and numerical simulation, and widely used in weather forecast. The data assimilation methods have been recently applied for tsunami forecast in North America and Japan where dense observation networks exist. In my planned study, I proposed a data assimilation method by introducing virtual observation data from neighboring real observations. Based on this method, we will be able to forecast the tsunamis in the Bay of Bengal if an earthquake happens around Sumatra Island.
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| Outline of Annual Research Achievements |
In the past year, I have been working on tsunami data assimilation for early warning. I successfully conducted a retroactive study of the 2016 Fukushima earthquake. I used 28 S-net pressure gauge records for tsunami data assimilation and forecasted the tsunami waveforms at four tide gauges on the Sanriku coast. The forecast accuracy score is 74% for a time window of 35 min. In addition, I also worked on the optimal deployment of offshore bottom pressure gauges (OBPGs). I proposed an optimal deployment scheme of OBPGs in the South China Sea, aiming at early warning of potential tsunami hazards based on the data assimilation approach. The results indicated that at least three stations are required to cover the coast along southern China to forecast the tsunami in the South China Sea successfully. The next step is to apply tsunami data assimilation to the tsunami generated by the 2022 Tonga volcanic eruption. The S-net and DONET observation will be adopted.
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| Research Progress Status |
令和3年度が最終年度であるため、記入しない。
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| Strategy for Future Research Activity |
令和3年度が最終年度であるため、記入しない。
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Report
(3 results)
Research Products
(37 results)
