RAPID: North Dakota Field Measurement Campaign to Improve Understanding of Fog Processes

雾是一种高影响力的天气灾害，在世界各地的许多地方造成严重中断的道路交通，海上运输，特别是航空业务。雾的主要影响是能见度降低，可能导致经济损失、严重的道路事故、毁灭性的航空灾难和生命损失。因此，对雾的形成和消散的更好理解具有广泛的社会影响，因为这些知识将导致改进的大气模型，从而实现更可靠和更有效的运输系统。这种改进对于无人机系统（UAS）在运输中的日常使用尤其重要，因为飞行需要靠近地面并且在操作员的视线之外。关于当前和不久的将来大气条件的准确和详细的信息有助于确保UAS平台的安全和有效部署。教育学生提供这一必要的大气信息为新兴的工作做好准备，使他们能够提供所需的科学进步。雾的发生和演变是许多大气过程（云、湍流、辐射）的结果，这使得雾的精确模拟成为一个挑战。准确测量大气条件对于更好地了解雾的过程和相关的大气相互作用（云、气溶胶、湍流）是必要的。获得雾过程的更好的理解，使数值天气预报模型的改进。该项目将在秋季和春季雾事件的高峰期，在赫克托国际机场（北达科他州法戈）及周边地区进行为期两个月的密集观测期间进行大气观测。现场项目将在地面位置以及气球、飞机和受天气影响的UAS平台上部署一套复杂的气象、云和气溶胶仪器。利用这些机载平台可以对边界层的垂直结构进行详细的观测，这将为科学界提供一个独特的测量数据库，以供分析。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Fog is a high-impact weather hazard over many locations worldwide causing serious disruptions to road traffic, marine transport, and especially aviation operations. The main impact of fog is visibility reduction that can lead to financial damages, severe road accidents, devastating aviation disasters, and loss of lives. Therefore, improved understanding of fog formation and dissipation has broad societal impacts since such knowledge will result in improved atmospheric models that enable a more reliable and efficient transportation system. Such improvements are especially critical for routine utilization of Unmanned Aircraft Systems (UAS) in transportation since flights need to be near the ground and out of sight of the operator. Accurate and detailed information on current, and near future atmospheric conditions, help to ensure safe and effective deployment of UAS platforms. Educating students to provide this necessary atmospheric information prepares them for emerging jobs and enables them to provide needed scientific advances.The genesis and evolution of fog is the result of many atmospheric processes (cloud, turbulence, radiation), which make accurate modeling of fog a challenge. Accurate measurements of atmospheric conditions are necessary for improved understanding of fog processes and related atmospheric interactions (clouds, aerosols, turbulence). Obtaining a better understanding of fog processes enables improvements in numerical weather prediction modeling. The project will conduct atmospheric observations during two month-long intensive observational periods at Hector International Airport (Fargo, North Dakota), and the surrounding area, during the peak period of fall and spring fog events. The field projects will deploy a complex suite of meteorological, cloud, and aerosol instruments at surface locations, and on balloon, aircraft, and weatherized UAS platforms. Utilizing these airborne platforms enables detailed observations of the vertical structure of the boundary layer, which will provide a unique database of measurements available to the scientific community for analysis.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.