Use of large-eddy simulations to design an adaptive sampling strategy to assess cumulus cloud heterogeneities by remotely piloted aircraft

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, microphysical, 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 for 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 paper aims to improve exploration strategies that can be implemented by a fleet of RPA.