Tsunami warning centres face the challenging task of rapidly forecasting tsunami threat immediately after an earthquake, when there is high uncertainty due to data deficiency. Here we introduce Probabilistic Tsunami Forecasting (PTF) for tsunami early warning. PTF explicitly treats data- and forecast-uncertainties, enabling alert level definitions according to any predefined level of conservatism, which is connected to the average balance of missed-vs-false-alarms. Impact forecasts and resulting recommendations become progressively less uncertain as new data become available. Here we report an implementation for near-source early warning and test it systematically by hindcasting the great 2010 M8.8 Maule (Chile) and the well-studied 2003 M6.8 Zemmouri-Boumerdes (Algeria) tsunamis, as well as all the Mediterranean earthquakes that triggered alert messages at the Italian Tsunami Warning Centre since its inception in 2015, demonstrating forecasting accuracy over a wide range of magnitudes and earthquake types.

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Probabilistic tsunami forecasting for early warning.

The Eastern Mediterranean Basin (EMB) is under the threat of tsunami events triggered by various causes including earthquakes and landslides. We propose a deployment of Offshore Bottom Pressure Gau...

A Tsunami Warning System Based on Offshore Bottom Pressure Gauges and Data Assimilation for Crete Island in the Eastern Mediterranean Basin

Numerical modeling of tsunami: advances and future challenges after the 2011 Tohoku earthquake and tsunami

Being situated on the major subduction zones in conjunction with a considerable number of submarine active faults and coastal volcanoes, Japan has a long history of catastrophic tsunami events. Consequently, enormous efforts in disaster mitigation, particularly in relation with tsunami hazards have been made across the country. It is of our interest to review the developments of tsunami observing systems in Japan, which may lead to a global implication beyond national boundaries. In this paper, we first discuss in general the evolution of past to present tsunami observing systems available around the territory of Japan. More specifically, we identify the existing offshore observational networks that are mainly consisted of cabled ocean bottom pressure gauges and global navigation satellite system buoys, and briefly analyze their performance and viability in the long-term future. In that context, we also appraise the potential of emerging technologies in the offshore tsunami detection leveraging unconventional platforms such as commercial ships and airplanes, which have recently been introduced by several studies in Japan.

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Developments of Tsunami Observing Systems in Japan

Tsunami Data Assimilation of Cabled Ocean Bottom Pressure Records for the 2015 Torishima Volcanic Tsunami Earthquake

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.

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H. Su

Papers

Tsunami Data Assimilation of Cabled Ocean Bottom Pressure Records for the 2015 Torishima Volcanic Tsunami Earthquake

Tsunami Data Assimilation Without a Dense Observation Network