Recent reports have highlighted the need for improved observations of the atmosphere boundary layer. In this study, we explore the combination of ground-based active and passive remote sensors deployed for thermodynamic profiling to analyze various boundary-layer observation strategies. Optimal-estimation retrievals of thermodynamic profiles from Atmospheric Emitted Radiance Interferometer (AERI) observed spectral radiance are compared with and without the addition of active sensor observations from a May–June 2017 observation period at the Atmospheric Radiation Measurement Southern Great Plains site. In all, three separate thermodynamic retrievals are considered here: retrievals including AERI data only, retrievals including AERI data and Vaisala water vapour differential-absorption lidar data, and retrievals including AERI data and Raman lidar data. First, the three retrievals are compared to each other and to reference radiosonde data over the full observation period to obtain a bulk understanding of their differences and characterize the impact of clouds on these retrieved profiles. These analyses show that the most significant differences are in the water vapour field, where the active sensors are better able to represent the moisture gradient in the entrainment zone near the boundary-layer top. We also explore how differences in retrievals may affect results of applied analyses including land–atmosphere coupling, convection indices, and severe storm environmental characterization. Overall, adding active sensors to the optimal-estimation retrieval shows some added information, particularly in the moisture field. Given the costs of such platforms, the value of that added information must be weighed for the application at hand.

最近的报告强调需要改进对大气边界层的观测。在这项研究中，我们探索了用于热力学剖面的地基有源和无源遥感器的组合，以分析各种边界层观测策略。从大气发射辐射干涉仪 (AERI) 观测到的光谱辐射对热力学剖面的最佳估计反演进行了比较，并在大气辐射测量南部大平原站点添加和不添加来自 2017 年 5 月至 6 月观测期间的主动传感器观测。总之，这里考虑了三个独立的热力学反演：仅包括 AERI 数据的反演，包括 AERI 数据和维萨拉水汽差分吸收激光雷达数据的反演，和检索包括 AERI 数据和拉曼激光雷达数据。首先，将三个检索结果相互比较，并参考整个观测期内的无线电探空仪数据，以大量了解它们的差异并描述云对这些检索到的剖面的影响。这些分析表明，最显着的差异出现在水汽场中，其中有源传感器能够更好地表示边界层顶部附近夹带区的水分梯度。我们还探讨了反演的差异如何影响应用分析的结果，包括陆地 - 大气耦合、对流指数和强风暴环境特征。总的来说，将有源传感器添加到最优估计检索显示了一些附加信息，特别是在水分领域。