NH021-03
Data assimilation for tsunami forecast with ship-borne GNSS data in the Cascadia subduction zone
Data assimilation for tsunami forecast with ship-borne GNSS data in the Cascadia subduction zone
Thursday, 10 December 2020: 16:15
Virtual
Abstract:
An efficient and cost-effective near-field tsunami warning system is crucial for coastal communities. Offshore DART and GNSS buoys can provide important data for tsunami forecasting
but 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 in the Cascadia region. We carry out synthetic experiments by applying a data assimilation (DA) method with ship position (elevation and velocity)
data in the Cascadia Subduction zone. The ship positions (latitude/longitude) are real, obtained from Automatic Identification System (AIS) broadcasts, but the tsunami signals (height and velocity) in our study
are based on models. Our findings show that the DA method can recover the true model with high accuracy if a sufficiently dense network of ship elevation data is used. In addition, we carried out sensitivity
studies of the DA method with varying ship spatial distributions. We find that a 20 km gap between the ships works well in terms of accuracy and computational time for the example Cascadia trench source model
that we explored. The highest accuracy is obtained when data from ships traveling in and around the tsunami source area are available. In summary, the ship-borne GNSS data has potential to be used in tsunami warning
system which would be affordable for many countries, though more work is needed to characterize the precision of ship-based GNSS data and determine ways to obtain such data in real time.
but 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 in the Cascadia region. We carry out synthetic experiments by applying a data assimilation (DA) method with ship position (elevation and velocity)
data in the Cascadia Subduction zone. The ship positions (latitude/longitude) are real, obtained from Automatic Identification System (AIS) broadcasts, but the tsunami signals (height and velocity) in our study
are based on models. Our findings show that the DA method can recover the true model with high accuracy if a sufficiently dense network of ship elevation data is used. In addition, we carried out sensitivity
studies of the DA method with varying ship spatial distributions. We find that a 20 km gap between the ships works well in terms of accuracy and computational time for the example Cascadia trench source model
that we explored. The highest accuracy is obtained when data from ships traveling in and around the tsunami source area are available. In summary, the ship-borne GNSS data has potential to be used in tsunami warning
system which would be affordable for many countries, though more work is needed to characterize the precision of ship-based GNSS data and determine ways to obtain such data in real time.