RAPID: Sensing and Modeling Infrastructure for Storm Surge Monitoring and Forecasting in Coastal Zones

RAPID：沿海地区风暴潮监测和预报的传感和建模基础设施

• 批准号: 1763294
• 负责人: Shaowu Bao
• 金额: $ 9.94万
• 依托单位: Coastal Carolina University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1763294&HistoricalAwards=false
• 关键词: RAPID; Sensing; Modeling; Infrastructure; Storm

The Atlantic Coast is threatened by disasterous storms every year. The dangers of high winds are widely publicized, but flooding has been responsible for more than half of all associated deaths. Unfortunately, existing forecasting capabilities are limited. Failure to predict significant flooding is of obvious concern, but the risks associated with perpetually inflated forecasts could be equally damaging. A citizen who evacuated Hurricane Irma's path based on storm surge warnings, only to return home to limited flooding, might consider ignoring future surge forecasts. Depending on the storm, that decision could be an enormous mistake. This project addresses surge forecasting limitations. The intellectual merit of this project lies in computer and network systems research and computational earth systems modeling research to be undertaken along two connected paths. First, the project will pursue the design, implementation, and evaluation of a fundamentally new approach to achieving high-precision in situ measurement of storm-induced surge in challenging environments. Second, the project will pursue fundamental extensions to an existing modeling system comprising coupled atmospheric, oceanic, and hydrologic models. The work will focus on assimilation of in situ surge measurements to anchor and refine model outputs prior to, during, and after tropical storms.The broader impacts of this project will be significant: The world's coastal population is growing at an unprecedented rate. In the past four decades, the U.S. coastal population grew by 45%, with more than half of the population living in coastal counties. In the coming decades, coastal states' urban centers are expected to continue to grow. The resulting population density presents significant challenges. The ability of state and local governments to efficiently prepare for and respond to storm surge events is critical. This project will support significant progress toward the development of reliable storm surge monitoring and modeling systems.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.

大西洋海岸每年都受到灾难性风暴的威胁。大风的危险被广泛宣传，但洪水造成的死亡人数占所有相关死亡人数的一半以上。不幸的是，现有的预测能力有限。未能预测重大洪灾显然令人担忧，但与永久夸大的预测相关的风险可能同样具有破坏性。如果一位市民根据风暴潮警告撤离了飓风伊尔玛的路径，结果回家时洪水有限，那么他可能会考虑忽略未来的风暴潮预报。根据风暴的情况，这一决定可能是一个巨大的错误。该项目解决了浪涌预报的局限性。该项目的智力价值在于计算机和网络系统研究和计算地球系统建模研究将沿着两条相连的道路进行。首先，该项目将致力于设计、实施和评估一种全新的方法，以在具有挑战性的环境中实现高精度的风暴潮现场测量。其次，该项目将从根本上扩展现有的模拟系统，该系统包括耦合的大气、海洋和水文模型。这项工作将侧重于同化现场涌浪测量，以在热带风暴之前、期间和之后锚定和完善模式输出。该项目的更广泛影响将是显著的：世界沿海人口正在以前所未有的速度增长。在过去的40年里，美国沿海人口增长了45%，超过一半的人口生活在沿海县。在接下来的几十年里，沿海各州的城市中心预计将继续增长。由此产生的人口密度带来了巨大的挑战。州和地方政府有效地准备和应对风暴潮事件的能力至关重要。该项目将支持在开发可靠的风暴潮监测和建模系统方面取得重大进展。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

The Need and Rationale for a Coastal Flood Risk Index

沿海洪水风险指数的必要性和基本原理

• DOI: 10.1109/oceans.2018.8604927
• 发表时间: 2018
• 期刊: OCEANS 2018 MTS/IEEE Charleston
• 作者: Bao, Shaowu;Gayes, Paul;Pietrafesa, Len
• 通讯作者: Pietrafesa, Len

Improving Numerical Model Predicted Float Trajectories by Deep Learning

• DOI: 10.1029/2022ea002362
• 发表时间: 2022-08
• 期刊: Earth and Space Science
• 影响因子: 3.1
• 作者: D. Shen;S. Bao;Len Pietrafesa;P. Gayes

Coastal Flooding and Inundation and Inland Flooding due to Downstream Blocking

下游阻塞造成的沿海洪水和内陆洪水

• DOI: 10.3390/jmse7100336
• 发表时间: 2019
• 期刊: Journal of Marine Science and Engineering
• 影响因子: 2.9
• 作者: Pietrafesa, Leonard J.;Zhang, Hongyuan;Bao, Shaowu;Gayes, Paul T.;Hallstrom, Jason O.
• 通讯作者: Hallstrom, Jason O.

Mobile Infrastructure for Monitoring, Modeling, and Forecasting of Coastal Weather Events. Phase I: Building the Smart Drifter

用于沿海天气事件监测、建模和预报的移动基础设施。

• DOI: 10.4236/jcc.2019.77009
• 发表时间: 2019
• 期刊: Journal of Computer and Communications
• 作者: Zhai, Jiannan;Hallstrom, Jason O.;Bao, Shaowu;Gayes, Paul;Pietrafesa, Len
• 通讯作者: Pietrafesa, Len