EAGER: A New Aerial Platform for Turbulence Measurements in the Atmospheric Surface Layer

该项目旨在推进测量风的研究。为什么它很重要？精确测量风可以改善天气预报。例如，如果我们能够更好地预测在涉及有毒气体污染的野火和火车脱轨等灾害情况下风的行为，我们就可以预测风因素在当地的行为和影响，并预防或最大限度地减少对公众的健康风险。这将有助于更好地协调应急反应和资源，并最大限度地减少对地方、州和国家各级的经济影响。为了实现这些目标，无人机技术将用于该项目。无人机的一个主要好处是，它们可以进入地面附近的大气层中对人类不安全或难以进入的位置，例如山区、湖泊和冰川。 无人机技术的另一个好处是，无人机可以在困难的地形上长时间盘旋，从而收集数据，以开发和测试风力预测模型。这种类型的人在困难的地形研究目前是不可能的或很难实现，这导致了风的行为理解和风预测模型的主要弱点。用无人机收集风数据不仅非常新颖，而且具有成本效益。使用无人机的风力收集技术将解决当前研究的弱点，并有助于在日常生活或紧急情况下保护公众。该项目旨在探索商用现货（COTS）多旋翼无人机测量湍流的能力。这种方法是创新的，因为它只依赖于使用COTS无人机的易于检索的数据。不需要定制框架和调整飞行控制参数，使该方法具有成本效益，并且易于为没有工程和/或技术背景的科学界所用。主要目标是调查1）这些基于无人机的湍流测量的准确性，以及2）准确性对无人机尺寸和重量的依赖性。在NSF资助的M2 HATS现场研究中，测量将在平坦地形上的大型最先进的声波风速计阵列旁边进行，为相互比较实验提供了前所未有的机会。将使用八个多旋翼飞行器，包括两种不同类型的COTS多旋翼无人机，以检索估计湍流所需的数据。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。

This project seeks to advance research in the measurement of winds. Why is it important? Having refined measurements of winds improves weather forecasting. For instance, if we can better predict how winds behave during disaster situations such as wildfires and train derailments that involve toxic gas pollution, we can forecast the behavior and impact of wind factors in local areas and prevent or minimize health risks to the public. This would allow better coordination of emergency response and resources and minimize the economic impact at the local, state, and national levels. To achieve these goals, drone technology will be used in this project. One major benefit of drones is that they can access locations in the atmosphere near the ground that are unsafe to humans or difficult to access otherwise, such as mountainous terrain, lakes, and glaciers. Another benefit of drone technology is that a drone can hover over difficult terrain for a long time, allowing data collection to develop and test wind forecasting models. This type of in-person research in difficult terrain is currently impossible or very difficult to achieve, which has resulted in major weaknesses in wind behavior understanding and in wind forecasting models. Collecting wind data with drones is not only very novel, but also cost-effective. The wind collection technology using drones would address the current research weaknesses and help protect the public in their daily lives or in emergency situations. The project aims to explore the ability of commercial off-the-shelf (COTS) multirotor drones to measure turbulence. The approach is innovative because it only relies on the use of easily retrievable data from COTS drones. There is no need for custom-built frames and tuning of flight control parameters, making the approach cost-efficient and easily accessible to the scientific community without an engineering and/or technical background. The major goals are to investigate 1) the accuracy of these drone-based turbulence measurements, and 2) the dependency of the accuracy on drone size and weight. Measurements will be performed next to a large array of start-of-the-art sonic anemometers over flat terrain in the NSF funded M2HATS field study, offering an unprecedented opportunity for an intercomparison experiment. Eight multi-rotor vehicles will be used, including two different types of COTS multirotor drones to retrieve data necessary to estimate turbulence.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.