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净压力计资料进行海啸资料同化，对三陆沿海4个验潮仪的海啸波形进行预报。对于35分钟的时间窗口，预测准确率得分为74%。此外，我还致力于海上海底压力计(OBPG)的优化部署。基于数据同化方法，提出了一种基于数据同化方法的南海区大洋钻探优化布放方案，旨在预警潜在的海啸灾害。结果表明，至少需要3个台站覆盖中国南部沿海，才能成功预报中国南部海域的海啸。下一步是将海啸数据同化应用于2022年汤加火山喷发引发的海啸。将采用S网和多奈的观测。

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.