Doctoral Dissertation Research: Spatial Structure of Turbulent Flows in the Atmospheric Boundary Layer

本博士论文项目将研究大气边界层（ABL）中重要变量的最佳空间采样。这一层对当地天气发展和湍流预测相当重要，但它是使用传统测量技术进行采样最困难的大气部分之一。博士生将使用小型无人驾驶飞机系统（sUAS），这是一种很有前途的技术，可以填补ABL的采样空白，并提高对当地天气动态的理解。这项研究将通过使用新的sUAS技术确定ABL中温度和相对湿度变量的适当空间采样尺度来推进知识。与塔尔萨国家气象局办公室的气象学家密切合作，sUAS捕获的大气变量剖面将与气象科学家实时共享，以纳入即时预报模型。还将通过同行审查的出版物和会议介绍向国家和国际受众介绍成果。作为博士论文研究改进奖，本项目将为有前途的学生提供支持，使其能够建立独立的研究事业。本研究通过分析这些变量在空间上相似的尺度（自相关），有助于更好地理解在气象学中至关重要的ABL中的小尺度湍流。小尺度湍流理论的基础上，沿着与地理和空间科学建立空间分析方法，本研究将开发空间采样策略，需要高效和有效的采样比例变量的ABL和开发分析方法，揭示影响天气发展的过程。具体而言，本研究将（1）使用变差函数分析确定ABL中sUAS捕获的热力学变量的最佳采样尺度;（2）调查通常用于识别各种景观类型和大气条件下湍流结构的大小和形状的参数的通用性;（3）与国家气象局塔尔萨办事处的气象学家分享真实的时间数据和研究成果，以帮助预报和数值天气预报。采样将在俄克拉荷马州进行，这是该国经常经历包括龙卷风在内的严重局部风暴的地区。俄克拉荷马州Mesonet是一个由120个自动气象和气象站组成的网络，将用于传感器校准和验证目的。Mesonet站点分布在全州的各种气候区和生态区。数据收集将针对一年中可能发生风暴的时间，如春天，以确保捕捉到不同的气象条件。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 doctoral dissertation project will investigate optimal spatial sampling of important variables in the atmospheric boundary layer (ABL). This layer is of considerable importance for local weather development and turbulence prediction, yet it is one of the most difficult portions of the atmosphere to sample using conventional measurement technologies. The doctoral student will use small unmanned aircraft systems (sUAS), a promising technology that has emerged to fill sampling gaps in the ABL and improve the understanding of local weather dynamics. This research will advance knowledge by identifying appropriate spatial sampling scales for temperature and relative humidity variables in the ABL using the new sUAS technology. Working closely with the meteorologist-in-charge at the National Weather Service office in Tulsa, profiles of atmospheric variables captured with the sUAS will be shared in real-time with weather scientists for inclusion in immediate forecast models. Results will also be presented to national and international audiences through peer-reviewed publications and conference presentations. As a Doctoral Dissertation Research Improvement award, this project will provide support to enable a promising student to establish an independent research career.This research will contribute to a better understanding of small-scale turbulence in the ABL, which is of critical importance in meteorology, by analyzing the scales at which these variables are spatially similar (autocorrelated). Building on theories of small scale turbulence along with established spatial analytical methods from geography and the spatial sciences, this research will develop the spatial sampling strategies needed for efficient and effective sampling of scaler variables in the ABL and develop analytical methods for uncovering the processes impacting weather development. Specifically, this research will (1) determine the optimal sampling scales for thermodynamic variables captured with sUAS in the ABL using variogram analysis; (2) investigate the universality of parameters commonly used to identify the size and shape of turbulent structures across a variety of landscape types and atmospheric conditions; (3) share real time data and research findings with meteorologists at the NWS Tulsa office to aid in forecasting and numerical weather prediction. Sampling will be conducted in Oklahoma, a region of the country that frequently experiences severe local storms including tornadoes. The Oklahoma Mesonet is a network of 120 automated meteorological and weather stations that will be used for sensor calibration and validation purposes. The Mesonet sites are distributed across the state in a variety of climate zones and ecoregions. Data collection will be targeted during times of the year when storms are likely, such as spring, to ensure diverse meteorological conditions are captured.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.