RAPID: Mobile Infrastructure for Monitoring, Modeling, and Forecasting of Coastal Weather Events

RAPID：用于监测、建模和预测沿海天气事件的移动基础设施

• 批准号: 1713922
• 负责人: Jason Hallstrom
• 金额: $ 9.89万
• 依托单位: Florida Atlantic University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-12-15 至 2018-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1713922&HistoricalAwards=false
• 关键词: RAPID; Mobile; Infrastructure; Monitoring; Modeling

Tropical storms are among the most destructive natural phenomenon on the planet. Each year, these storms pose a threat to the Atlantic Coast. The dangers of high winds and storm-induced sea level rise are widely recognized, but these storms can also result in inland flooding. Historically, inland flooding is responsible for the majority of deaths attributed to tropical storms in the US. Existing methods do not provide accurate forecasts of inland flooding patterns. This project will improve these forecasts through a computational framework that relies on key measurements collected from mobile sensing arrays deployed in advance of incoming storms.The project will develop a computational framework to simulate inland lateral flooding processes. Collection and calibration of the key hydrologic variables, both in baseline, and in advance of future storms, is critical. The project will focus on surface water velocity measurements near key population locales, particularly as the river basins transition from storage modes to discharge modes ? resulting from the recent passage of Hurricane Matthew. Data collection will be achieved through mobile sensing arrays, comprising photogrammetry drones, GPS drifters, and a new drifter-based technology for acquiring fine-grained surface water velocity measurements. The Atlantic coast is threatened by approximately ten tropical storms per year, with more than half becoming hurricanes. The impacts can be catastrophic, resulting in loss of life and damage to property and infrastructure. Hurricane Matthew, widely viewed as a near miss, resulted in more than 40 deaths in the US, and damage to more than 100,000 homes ? most attributed to inland flooding. This project will result in improved forecasting infrastructure for inland flooding, enabling local and state governments and emergency management teams to more effectively plan and respond to tropical storms.

热带风暴是地球上最具破坏性的自然现象之一。每年，这些风暴都会对大西洋海岸构成威胁。人们普遍认识到大风和风暴导致的海平面上升的危险，但这些风暴也可能导致内陆洪水。从历史上看，内陆洪水是美国热带风暴造成的大部分死亡原因。现有的方法不能提供对内陆洪水模式的准确预测。该项目将通过一个计算框架来改进这些预报，该框架依赖于在即将到来的风暴之前部署的移动传感阵列收集的关键测量数据。该项目将开发一个计算框架来模拟内陆横向洪水过程。收集和校准关键的水文变量，无论是在基线上，还是在未来风暴之前，都至关重要。该项目将侧重于关键人口地点附近的地表水速度测量，特别是在河流流域从蓄水模式过渡到排放模式时。这是由于最近飓风马修的过境造成的。数据收集将通过移动传感阵列实现，包括摄影测量无人机、GPS漂移器和一种基于漂移器的新技术，用于获取细粒度的地表水速度测量。大西洋海岸每年受到大约10个热带风暴的威胁，其中一半以上成为飓风。其影响可能是灾难性的，导致生命损失和财产和基础设施的破坏。飓风马修被广泛认为是一次险些发生的事故，它在美国造成40多人死亡，10万多座房屋受损？大多数被归因于内陆洪水。该项目将改善内陆洪水的预报基础设施，使地方和州政府以及应急管理团队能够更有效地规划和应对热带风暴。