Abstract. Trade wind cumulus clouds have a significant impact on the earth's radiative balance, due to their ubiquitous presence and significant coverage in subtropical regions. Many numerical studies and field campaigns have focused on better understanding the thermodynamic and macroscopic properties of cumulus clouds with ground-based and satellite remote sensing as well as in-situ observations. Aircraft flights have provided a significant contribution, but their resolution remains limited by rectilinear transects and fragmented temporal data of individual clouds. To provide a higher spatial and temporal resolution, Remotely Piloted Aircraft (RPA) can now be employed for direct observations, using numerous technological advances, to map the microphysical cloud structure and to study entrainment mixing. In fact, the numerical representation of mixing processes between a cloud and the surrounding air has been a key issue in model parameterizations for decades. To better study these mixing processes as well as their impacts on cloud microphysical properties, the manuscript aims to improve exploration strategies that can be implemented by a fleet of RPAs. Here, we use a Large-Eddy simulation (LES) of oceanic cumulus clouds to design adaptive sampling strategies. An implementation of the RPA flight simulator within high-frequency LES outputs (every 5 s) allows to track individual clouds. A Rosette sampling strategy is used to explore clouds of different sizes, static in time and space. The adaptive sampling carried out by these explorations is optimized using one ors two RPAs and with or without Gaussian Process Regression (GPR) mapping, 1by comparing the results obtained with those of a reference simulation, in particular the total liquid water content (LWC) and the LWC distributions in a horizontal cross section. Also, a sensitivity test of lengthscale for GPR mapping is performed. The results of exploring a static cloud are then extended to a dynamic case of a cloud evolving with time, to assess the application of this exploration strategy to study the evolution of cloud heterogeneities.

中文翻译：

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使用大涡模拟来设计自适应采样策略，以评估远程驾驶飞机的积云异质性

摘要。贸易风积云由于普遍存在并且在亚热带地区具有广泛的覆盖面，因此对地球的辐射平衡具有重大影响。许多数值研究和野外研究都集中在通过地面和卫星遥感以及原位观测来更好地理解积云的热力学和宏观性质。飞机飞行做出了重大贡献，但是其分辨率仍然受直线样条线和各个云的时间数据碎片的限制。为了提供更高的空间和时间分辨率，现在可以使用远程驾驶飞机（RPA），利用众多技术进步进行直接观测，以绘制微物理云结构图并研究夹带混合。实际上，几十年来，云与周围空气之间混合过程的数值表示一直是模型参数化中的关键问题。为了更好地研究这些混合过程及其对云微物理性质的影响，该手稿旨在改进可由RPA车队实施的勘探策略。在这里，我们使用海洋积云的大涡模拟（LES）设计自适应采样策略。在高频LES输出（每5秒）内实施RPA飞行模拟器可跟踪单个云。Rosette采样策略用于探索不同大小的云，这些云在时间和空间上都是静态的。这些探索执行的自适应采样是使用一个或两个RPA进行优化的，并带有或不带有高斯过程回归（GPR）映射，通过将获得的结果与参考模拟的结果进行比较，特别是在水平横截面中的总液态水含量（LWC）和LWC分布，可以得出图1所示的结果。同样，进行了用于GPR映射的长度标度的敏感性测试。然后，将探索静态云的结果扩展到云随时间演变的动态案例中，以评估这种探索策略在研究云异质性演化中的应用。