MRI: Development of a Long-range Airborne Snow and Sea Ice Thickness Observing System (LASSITOS)

MRI：开发远程机载雪和海冰厚度观测系统（LASSITOS）

• 批准号: 1828743
• 负责人: Andrew Mahoney
• 金额: $ 161.54万
• 依托单位: University of Alaska Fairbanks Campus
• 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: 2018-09-15 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1828743&HistoricalAwards=false
• 关键词: MRI; Development; Long; range; Airborne

Accurate knowledge of sea ice thickness over large scales is crucial for understanding the current and future states of the Arctic ice cover, and for near- and long-term predictions of Arctic marine environments. With the Arctic ice pack undergoing a major transition from perennial to seasonal ice, ice thickness - more so than ice extent - is a key variable describing the state and evolution of the ice-ocean system. However, methods of observing sea ice thickness at regional or basin scales with sufficient accuracy and resolution to capture growth and melt processes, detect hazards, or assess habitat quality are lacking. This project will develop an Airborne electromagnetic (AEM) snow radar system capable of being integrated into long-range Unmanned Aerial Systems (UAS). This will allow acquisition of basin-scale ice thickness and snow depth data as part of a network for Arctic observations that addresses information needs of researchers, local communities and industry. This MRI development project will contribute to NSF's Navigating the New Arctic Big Idea. AEM methods offer a novel means of measuring sea ice thickness over the full range of thicknesses found in the Polar Regions. By remotely sensing the positions of the upper and lower surfaces of the ice cover, AEM measurements typically achieve an accuracy of better than 10% of the total thickness, with less sensitivity to uncertainties in snow cover or sea surface topography. Development and commissioning of the Long-range Airborne Snow and Sea Ice Thickness Observing System (LASSITOS) will also provide opportunities for education and training, including capstone projects for the University of Alaska Fairbanks' new minor in aeronautical engineering and student involvement in comprehensive calibration/validation field activities. LASSITOS is expected to generate interest among native students from coastal villages in northern Alaska, who represent another key stakeholder group for sea ice information. The leader of this project is an early-career researcher.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.

准确了解大尺度海冰厚度对于了解北极冰盖的当前和未来状态以及北极海洋环境的近期和长期预测至关重要。随着北极冰层经历从常年冰向季节性冰的重大转变，冰厚度（比冰范围更重要）是描述冰海系统状态和演化的关键变量。然而，缺乏以足够的精度和分辨率观测区域或盆地尺度海冰厚度的方法，以捕获生长和融化过程、检测危险或评估栖息地质量。该项目将开发能够集成到远程无人机系统（UAS）中的机载电磁（AEM）雪雷达系统。这将允许获取盆地规模的冰厚度和雪深度数据，作为北极观测网络的一部分，满足研究人员、当地社区和行业的信息需求。该 MRI 开发项目将为 NSF 的导航新北极大构想做出贡献。 AEM 方法提供了一种测量极地地区所有厚度范围内海冰厚度的新颖方法。通过遥感冰盖上表面和下表面的位置，AEM 测量的精度通常优于总厚度的 10%，对雪盖或海面地形的不确定性的敏感性较低。远程机载雪和海冰厚度观测系统（LASSITOS）的开发和调试还将提供教育和培训机会，包括阿拉斯加大学费尔班克斯分校新辅修航空工程的顶点项目以及学生参与综合校准/验证现场活动。 LASSITOS 预计会引起来自阿拉斯加北部沿海村庄的本地学生的兴趣，他们代表了海冰信息的另一个关键利益相关群体。该项目的领导者是一位职业生涯早期的研究人员。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。