Data Assimilation for Tsunami Forecast With Ship‐Borne GNSS Data in the Cascadia Subduction Zone

Data Assimilation for Tsunami Forecast With Ship‐Borne GNSS Data in the Cascadia Subduction Zone
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利用卡斯卡迪亚俯冲带船载 GNSS 数据进行海啸预报的数据同化

DOI:
10.1029/2020ea001390
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发表时间:
2021
影响因子:
3.1
通讯作者:
Sheehan, Anne F.
Sheehan, Anne F.
中科院分区:
地球科学3区
文献类型:
--
作者:
Hossen, M. J.;Mulia, Iyan E.;Mencin, David;Sheehan, Anne F.

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一个高效、经济的近场海啸预警系统对沿海社区至关重要。现有的海啸预报系统是基于近海深海海啸评估和报告以及许多国家负担不起的全球导航卫星系统(GNSS)浮标。一种潜在的经济有效的解决方案是利用在沿海和近海地区航行的船舶的位置数据。在本研究中,我们探讨了在海啸预报中使用船载GNSS数据的可行性。采用数据同化(DA)方法对船舶位置(高程和航速)数据进行了综合实验。研究结果表明,如果采用密集的船舶高程数据网络,该方法可以以较高的精度恢复参考模型。然而,仅使用船速数据是无法恢复参考模型的。此外,我们还进行了数据分析方法对船舶空间分布的敏感性研究。我们发现,就我们所探索的示例源模型的精度和计算时间而言,船舶之间20公里的间隙效果很好。当有足够数量的船只在海啸震源区内及其周围航行时,获得的数据精度最高。
An efficient and cost‐effective near‐field tsunami warning system is crucial for coastal communities. The existing tsunami forecasting system is based on offshore Deep‐Ocean Assessment and Reporting of Tsunamis and Global Navigation Satellite System (GNSS) buoys which are not affordable for many countries. A potential cost‐effective solution is to utilize position data from ships traveling in coastal and offshore regions. In this study, we examine the feasibility of using ship‐borne GNSS data in tsunami forecasting. We carry out synthetic experiments by applying a data assimilation (DA) method with ship position (elevation and velocity) data. Our findings show that the DA method can recover the reference model with high accuracy if a dense network of ship elevation data is used. However, the use of ship velocity data alone is unable to recover the reference model. In addition, we carried out sensitivity studies of the DA method to the ship spatial distribution. We find that a 20 km gap between the ships works well in terms of accuracy and computational time for the example source model that we explored. The highest accuracy is obtained when data from a sufficient number of ships traveling in and around the tsunami source area are available.
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