PREEVENTS Track 2: Cascadia Tsunami Warning with Data Assimilation and Optimal Sensor Distribution

University Of Colorado At Boulder, Boulder CO

Investigators

Abstract

The Cascadia Subduction Zone poses a significant earthquake and tsunami hazard to the densely populated coastal regions of the US Pacific Northwest. The goals of this project are to improve the capability for tsunami warning in this region by several lines of research. The first is to use the method of data assimilation, which takes tsunami wave observations as they arrive at ocean observing points and uses them to forecast the tsunami at the coast. The second is to test the use of ship position data in tsunami warnings. Tsunami waves are small in the open ocean but can cause detectable changes in ship height and heading. These ship data could provide useful information to improve tsunami forecasts. The last aspect of the project is to determine the best spacing of seafloor observing sites for tsunami warnings offshore the US Pacific Northwest. Installing seafloor observing sites is costly, and this study will use mathematical methods to determine the best places for seafloor sensors considering different scenarios. Anticipated results from this project will help inform efforts to design future observation networks offshore Cascadia and other subduction zones for providing earthquake and tsunami early warning capability. The motivation for this project is to improve the speed and accuracy of tsunami warnings and to best design detection arrays for tsunami warning. This project will explore the use of data assimilation and optimal sensor distribution for Cascadia tsunami warning. Multiple ocean-based observational data will be utilized including seafloor pressure recordings from the Cascadia Initiative experiment, National Oceanic and Atmospheric Administration Deep Ocean Assessment and Reporting of Tsunamis (DART) seafloor pressure data, coastal tide gauge data, and sea surface height data from ship-borne Global Navigation Satellite Systems. The emphasis of this work will be on (1) development of data assimilation methods for tsunami warnings by including rapid seismogeodetic source solutions, ship height data, and tsunami-band Green?s functions in addition to dense arrays of seafloor pressure data, (2) examination of the utility of GNSS-derived ship height data for local tsunami warning, and (3) determination of optimal distribution of tsunami sensors in the offshore Cascadia Subduction Zone. Data assimilation is a method that has been used in weather forecasting for years, and uses real-time observations to develop a forecast. Data assimilation is a rapidly developing line of research and has only recently been applied to tsunami studies. In addition to utilizing data assimilation to better forecast tsunamis, this work will explore the optimal spacing of sensors to provide timely and accurate tsunami warnings to the coastal regions. Instead of deploying many stations, which may be prohibitively expensive, an optimal observation network design can be explored through numerical simulations. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.