Tsunami Data Assimilation Without a Dense Observation Network
Tsunami Data Assimilation Without a Dense Observation Network
复制标题
没有密集观测网络的海啸数据同化
DOI：
10.1029/2018gl080930
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发表时间：
2019
影响因子：
5.2
通讯作者：
Gusman A. R.
中科院分区：
文献类型：
--
作者：
Wang Y.;Maeda T.;Satake K.;Heidarzadeh M.;Su H.;Sheehan A. F.;Gusman A. R.
海啸数据同化方法使海啸预报直接从观测，而不需要估计海啸源。然而，它需要一个密集的观测网络来产生理想的结果。在这里，我们提出了一种改进的方法，海啸数据同化的地区与稀疏的观测网络。该方法利用虚拟站的内插波形。在两个现有的观测站之间的虚拟站的海啸波形估计偏移的到达时间与观测到的到达时间的线性插值，并通过校正其水深的振幅。在我们的新的数据同化方法中，我们采用最优插值算法的真实的观测和虚拟站，以构建一个完整的海啸传播的波前。2004年苏门答腊-安达曼地震和2009年新西兰Dusky Sound地震的应用表明，虚拟台站的增加大大有助于提高海啸预报的准确性。
The tsunami data assimilation method enables tsunami forecasting directly from observations, without the need of estimating tsunami sources. However, it requires a dense observation network to produce desirable results. Here we 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 our new data assimilation approach, we employ the Optimal Interpolation algorithm to both the real observations and virtual stations, in order to construct a complete wavefront of tsunami propagation. The application to the 2004 Sumatra‐Andaman earthquake and the 2009 Dusky Sound, New Zealand, earthquake reveals that addition of virtual stations greatly helps improve the tsunami forecasting accuracy.