Tsunami Data Assimilation Without a Dense Observation Network

Y. Wang, T. Maeda, K. Satake, M. Heidarzadeh, H. Su, A. F. Sheehan, A. R. Gusman

Research output: Contribution to journalArticlepeer-review

16 Citations (SciVal)


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.

Original languageEnglish
Pages (from-to)2045-2053
Number of pages9
JournalGeophysical Research Letters
Issue number4
Publication statusPublished - 28 Feb 2019


  • data assimilation
  • tsunami forecasting

ASJC Scopus subject areas

  • Geophysics
  • Earth and Planetary Sciences(all)


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