MRI: Development of Grand-Scale Atmospheric Imaging Apparatus (GAIA) for Field Characterization of Atmospheric Flows and Particle Transport

了解大气环境中颗粒（如雪、沙、花粉等）的流动和运输对于风能、气象学（如雪沉降）、地形学（如沙漠迁移）、海洋学（如喷雾产生）、农业（如花粉传播）、公共卫生（如空气传播疾病）等相关应用至关重要。这些过程涉及大范围的空间和时间尺度的流动和复杂的大气现象，在实验室中不可能完全再现。这些过程的常规现场测量（例如气象塔、激光雷达、激光雷达和雷达）没有足够的分辨率来探索其详细的底层物理。为了弥补这一差距，由流动物理学家、计算机科学家和工程师组成的团队，该提案旨在开发一种大规模大气成像设备（GAIA），这是一种独立的、基于成像的现场测量系统，能够以前所未有的时空分辨率量化大样本区域的大气流动和粒子输送。通过与全球11所大学、国家实验室和行业的合作，GAIA将推动工程、地球科学和计算机科学领域的基础和应用研究，并将支持一些涉及弱势群体和少数民族的现有教育项目。该项目的目标是开发一种大规模大气成像设备（GAIA），设想作为一种现场仪器，通过利用自然存在于大气中的颗粒（例如，雪，沙子，花粉，液滴等）来进行颗粒图像/跟踪速度测量（PIV/PTV），以研究流动（使用它们作为示踪剂）和颗粒本身的运输，这取决于它们相对于流动的惯性特性。GAIA的开发创新了传统PIV/PTV的每一个组件，包括硬件和处理软件，以解决在恶劣现场条件下进行高分辨率流成像的关键挑战。具体而言，GAIA涉及多模式和多配置乐高设计和硬件的机械自动化，以及PIV/PTV概念与最先进的机器学习多视图3D场景重建的集成，用于数据处理。这种创新使GAIA能够在大范围内进行高分辨率的流动和粒子传输成像，其样本量比传统的PIV/ ptv大几个数量级。此外，GAIA集成了几个独特的传感器（例如，数字内嵌全息），用于现场表征气象条件和颗粒特性（例如形状，浓度等），具有前所未有的细节。GAIA将与先进的3D多普勒扫描激光雷达一起在不同的野外条件下进行测试。这种整合使得首次测量从亚米到公里尺度的大气流动和粒子输运成为可能，不仅为大气流动和粒子输运的基础研究提供了基准数据集，而且为计算机科学中基于学习的运动重建提供了基础数据集。该奖项反映了美国国家科学基金会的法定使命，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。

Understanding the flow and transport of particles (e.g., snow, sand, pollens, etc.) in atmospheric environments is critical for applications related to wind energy, meteorology (e.g., snow settling), geomorphology (e.g., desert migration), oceanography (e.g., spray generation), agriculture (e.g., pollen dispersal), public health (e.g., airborne disease transmission), etc. These processes involve flows over a broad range of spatial and temporal scales and complex atmospheric phenomena which are impossible to be fully reproduced in the laboratory. Conventional field measurements (e.g. meteorological tower, LiDAR, Sodar and Radar) of these processes do not have sufficient resolutions to probe into their detailed underlying physics. To bridge this gap, with a team of flow physicists, computer scientists, and engineers, the proposal aims to develop a Grand-scale Atmospheric Imaging Apparatus (GAIA), a stand-alone and imaging-based field measuring system, able to quantify atmospheric flows and particle transport over large sample regions with unprecedented spatiotemporal resolution. Though collaboration with 11 university, national labs and industries across the globe, GAIA will enable fundamental and applied research across engineering, geoscience and computer science, and will support a number of existing educational programs involving underrepresented groups and minorities. The goal of the project is to develop a Grand-scale Atmospheric Imaging Apparatus (GAIA), envisioned as a field instrument conducting particle image/tracking velocimetry (PIV/PTV) by exploiting particles (e.g., snow, sand, pollen, droplets, etc.) naturally present in the atmosphere to investigate both flow (using them as tracers) and the transport of the particles themselves depending on their inertial properties with respect to the flow. The development of GAIA innovates every single component of conventional PIV/PTV including both the hardware and processing software to address key challenges in conducting high-resolution flow imaging under harsh field conditions. Specifically, GAIA involves multi-mode and multi configuration Lego design and mechanical automation for the hardware and an integration of PIV/PTV concept with state-of-the-art machine learning multiview 3D scene reconstruction for data processing. Such innovation enables GAIA to conduct high-resolution imaging of flow and particle transport across a broad range of scales with sample volumes up to orders of magnitude larger than those of conventional PIV/PTVs. In addition, GAIA incorporates several unique sensors (e.g., digital inline holography) for in situ characterization of meteorological conditions and particle properties (e.g. shape, concentration, etc.) with unprecedented details. The GAIA will be tested under different field conditions in conjunction with cutting-edge 3D Doppler scanning LiDARs. Such integration enables the first-ever measurements of atmospheric flow and particle transport from sub-meter to kilometer scales, providing benchmark datasets not only for the fundamental study of atmospheric flow and particle transport, but also for learning-based motion reconstruction in computer science.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.