Tsunami Data Assimilation Without a Dense Observation Network

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. Plain Language Summary Data assimilation is a method to combine observation and numerical simulation and is widely used in weather forecast. The data assimilation methods have been recently applied for tsunami forecast in North America and Japan where dense observation networks exist. In this study, we proposed a data assimilation method by introducing virtual observation data from neighboring real observations. We applied the method for the Indian Ocean with the 2004 Sumatra‐Andaman earthquake tsunami and offshore New Zealand with the 2009 Dusky Sound earthquake tsunami. We found that the method greatly improved the forecasting accuracy and the method could be used for the regions with sparse observation network.