This study applies uncrewed aircraft systems towards the investigation of surface-layer structure during the morning transition. Three uncrewed aircraft systems simultaneously measuring horizontal transects were partnered with a fourth measuring vertical profiles during two consecutive mornings as part of the 2017 Collaboration Leading Operational Unmanned Aerial System Development for Meteorology and Atmospheric Physics (CLOUDMAP) measurement campaign near Stillwater, Oklahoma, USA. Data were analyzed to extract time-dependent single-point statistics of kinematic and thermodynamic variables from the uncrewed aircraft systems. In addition, an approach is presented by which multi-point spatial statistics in the form of auto- and cross-correlations could be calculated from the measurements. The results reflect differences in the evolution of spatial statistics with altitude for each of the two days at scales smaller than 500 m, despite very similar synoptic conditions. Conditional averaging was also applied to identify the structure of sweep and ejection motions and results revealed similarities to observations from canonical wall-bounded flow.

本研究将无人驾驶飞机系统应用于早晨过渡期间的表层结构调查。作为 2017 年合作领先的气象和大气物理操作无人机系统开发 (CLOUDMAP) 测量活动的一部分，在美国俄克拉荷马州斯蒂尔沃特附近，三个同时测量水平横断面的无人驾驶飞机系统与第四个测量垂直剖面的连续两个早晨合作。分析数据以从无人飞机系统中提取运动学和热力学变量的时间相关单点统计数据。此外，提出了一种方法，通过该方法可以从测量结果中计算出自相关和互相关形式的多点空间统计数据。尽管天气条件非常相似，但结果反映了在小于 500 m 的尺度上，两天中每一天的空间统计演变与海拔的差异。条件平均也被应用于识别扫掠和喷射运动的结构，结果显示与规范壁边界流动的观察结果相似。