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具有相当重要的意义，但它是使用传统测量技术最难取样的大气部分之一。博士生将使用小型无人驾驶飞机系统（sUAS），这是一项有前途的技术，已经出现，以填补ABL的采样空白，并提高对当地天气动力学的理解。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负责气象的气象学家密切合作，用sUAS捕获的大气变量概况将与气象科学家实时共享，以纳入即时预报模型。研究结果还将通过同行评议的出版物和会议发言向国内和国际听众介绍。作为博士论文研究进步奖，该项目将提供支持，使有前途的学生建立一个独立的研究生涯。通过分析这些变量在空间上相似（自相关）的尺度，本研究将有助于更好地理解ABL中的小尺度湍流，这在气象学中至关重要。基于小尺度湍流理论以及地理学和空间科学中已建立的空间分析方法，本研究将开发ABL中高效和有效采样尺度变量所需的空间采样策略，并开发揭示影响天气发展过程的分析方法。具体而言，本研究将(1)利用方差分析确定sUAS在ABL中捕获的热力学变量的最佳采样尺度；(2)研究了用于识别不同景观类型和大气条件下湍流结构大小和形状的常用参数的通用性；(3)与美国国家气象局塔尔萨办事处的气象学家分享实时数据和研究成果，以协助预报和数值天气预报。采样将在俄克拉何马州进行，该地区经常经历包括龙卷风在内的严重局部风暴。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遗址分布在全州不同的气候带和生态区。数据收集将集中在一年中可能发生风暴的时期，如春季，以确保捕捉到不同的气象条件。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。

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.