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

预防轨道 2：卡斯卡迪亚海啸预警与数据同化和最佳传感器分布

基本信息

批准号：
1855090
负责人：
Anne Sheehan
金额：
$ 36.06万
依托单位：
University of Colorado at Boulder
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-06-15 至 2025-05-31
项目状态：
未结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1855090&HistoricalAwards=false
关键词：
PREEVENTS Track Cascadia Tsunami Warning

项目摘要

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.

卡斯卡迪亚俯冲带对美国太平洋西北部人口稠密的沿海地区构成了重大的地震和海啸危险。该项目的目标是通过多方面的研究提高该地区的海啸预警能力。第一种是使用数据同化方法，即在海啸波观测到达海洋观测点时将其用于预测海岸的海啸。第二个是测试船舶位置数据在海啸警报中的使用。海啸波在开阔的海洋中很小，但可以引起船只高度和航向的可检测变化。这些船舶数据可以为改进海啸预报提供有用的信息。该项目的最后一个方面是确定美国太平洋西北部近海海啸预警海底观测点的最佳间距。安装海底观测站的成本很高，这项研究将使用数学方法来确定考虑不同情况的海底传感器的最佳位置。该项目的预期结果将有助于为设计未来的卡斯卡迪亚和其他俯冲带近海观测网络提供信息，以提供地震和海啸预警能力，该项目的动机是提高海啸预警的速度和准确性，并最好地设计海啸预警探测阵列。该项目将探索使用数据同化和最佳传感器分布进行卡斯卡迪亚海啸预警。将利用多种基于海洋的观测数据，包括卡斯卡迪亚倡议实验的海底压力记录、国家海洋和大气管理局深海评估和海啸报告（DART）海底压力数据、沿海验潮仪数据以及船载全球导航卫星系统的海面高度数据。这项工作的重点将是（1）发展海啸警报的数据同化方法，包括快速地震大地源解决方案，船舶高度数据，和海啸波段绿色？的功能，除了密集阵列的海底压力数据，（2）检查的全球导航卫星系统派生的船舶高度数据的效用，当地海啸预警，和（3）确定海啸传感器的最佳分布在近海卡斯卡迪亚俯冲带。数据同化是一种多年来一直用于天气预报的方法，它使用实时观测来进行预报。数据同化是一个迅速发展的研究领域，直到最近才应用于海啸研究。除了利用数据同化更好地预报海啸外，这项工作还将探讨传感器的最佳间距，以便向沿海地区提供及时和准确的海啸警报。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。