We explore the potential of a new method for the estimation of profiles of turbulence statistics in the stable boundary layer (SBL). By applying gradient-based scaling to multicopter unoccupied aircraft system (UAS) profiles of temperature and wind, sampled over sea-ice during the 2018 Innovative Strategies for Observations in the Arctic Atmospheric Boundary Layer (ISOBAR18) field campaign, turbulence profiles can be derived. We first validate this method by scaling turbulence observations from three levels on a 10-m mast with the corresponding scaling parameters, and compare the resulting non-dimensional parameters to the semi-empirical similarity functions proposed for this scaling scheme. The scaled data of turbulent fluxes and variances from the three levels collapse to their corresponding similarity functions. After the successful validation, we estimate turbulence statistics from UAS profiles by computing profiles of the gradient Richardson number to which we then apply the similarity functions. These UAS profiles are processed from raw time-series data by applying low-pass filters, time-response corrections, altitude corrections, and temporal averaging across successive flights. We present three case studies covering a broad range of SBL conditions to demonstrate the validity of this approach. Comparisons against turbulence statistics from the 10-m mast and a sodar indicate the broad agreement and physically meaningful results of the method. Successful implementation of the method thus offers a powerful diagnostic tool that requires only a multicopter UAS with a simple thermodynamic sensor payload.

我们探索了一种估计稳定边界层 (SBL) 中湍流统计剖面的新方法的潜力。在 2018 年北极大气边界层观测创新策略 (ISOBAR18) 野外活动期间，通过对多旋翼无人飞机系统 (UAS) 的温度和风速剖面应用基于梯度的缩放，可以得出湍流剖面。我们首先通过使用相应的缩放参数缩放来自 10 米桅杆上三个水平的湍流观测值来验证该方法，并将得到的无量纲参数与为该缩放方案提出的半经验相似函数进行比较。来自三个水平的湍流通量和方差的缩放数据坍缩为它们相应的相似函数。验证成功后，我们通过计算梯度理查森数的分布来估计 UAS 分布的湍流统计，然后我们将相似函数应用于该分布。这些 UAS 剖面是通过应用低通滤波器、时间响应校正、高度校正和连续飞行的时间平均从原始时间序列数据处理而来的。我们提出了三个案例研究，涵盖了广泛的 SBL 条件，以证明这种方法的有效性。与来自 10 米桅杆和声雷达的湍流统计数据的比较表明该方法的广泛一致性和物理意义的结果。因此，该方法的成功实施提供了一个强大的诊断工具，它只需要一个带有简单热力学传感器有效载荷的多旋翼无人机。