Tsunami Data Assimilation With Sparse Observations: Improvement Towards Tsunami Warning System

在过去的一年里，我一直致力于海啸数据同化的早期预警。我成功地对2016年福岛地震进行了追溯研究。利用28个S-net压力计记录进行海啸数据同化，并在三陆海岸的4个潮汐计上预测海啸波形。在35分钟的时间窗口内，预测准确率得分为74%。此外，我还参与了海上底部压力表（obpg）的优化部署工作。针对基于数据同化方法的潜在海啸灾害预警，提出了南海obpg优化部署方案。结果表明，要成功预报南海海啸，至少需要三个台站覆盖中国南部沿海地区。下一步是将海啸数据同化应用于2022年汤加火山爆发引发的海啸。将采用S-net和DONET观测。

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.