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

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.

Automated summary

The study found that using ship elevation data for data assimilation can accurately recover tsunami models, while using ship velocity data alone cannot achieve this effect. Regarding the spatial distribution of ships, a 20 km gap layout can ensure accuracy and computational efficiency. The highest accuracy in tsunami forecasting is obtained when a sufficient number of ship data are available.