Network for studying Entrainment and microPHysics of cLouds using Adaptive Exploration – NEPHELAE

Atmospheric models still suffer from a gap between ground-based and satellite measurements. As a consequence, the impacts of clouds on our climate remain one of the largest uncertainties in numerical weather prediction (NWP) and in understanding climate change. Aerosol-cloud interactions, moist convection and their impact on cloud formation, represent the most challenging burden in reducing the uncertainties in General Circulation Models (GCMs). Despite the continual efforts to increase the complexity of cloud parameterizations, uncertainties continue to persist in GCMs and NWPs. The lack of adequate measurements of cloud dynamics and key microphysical parameters that regulate cloud formation has caused a divergence in the formulation of cloud models. A full spatio-temporal 4D characterization of the microphysics and dynamics of cloud formation including the onset of precipitation has never been reached. To this end, NEPHELAE aims to develop a fleet of autonomous unmanned aerial vehicles (UAVs) that coordinate themselves in real-time as an intelligent network to address long standing questions in atmospheric science (NEPHELAE is the Greek name for nymphs of cloud and rain).

Ultimately, NEPHELAE is atmospheric science driven (focus on cloud microphysical processes) via robotic technological development (adaptive, model-guided path planning in a UAV fleet). Essentially, by merging the state-of-the-art technology, the observational tools for conducting research in atmospheric science will take a quantum leap forward. At the same time, we are using this novel approach to address decades-old questions on entrainment and the onset of precipitation that have been limited by traditional observing methods. It is not only the precision of the instruments that matters; rather it is the method in which the sampling strategy is applied.

NEPHELAE addresses the following science issues: 1. identify dominant entrainment mechanism, 2. quantify timescale of cloud development and onset of precipitation, and 3. assess impact of aerosol on entrainment and onset of precipitation. NEPHELAE directly responds to the goals of Défi 1 / Orientation 1 by creating an intelligent network to monitor the earth system with an innovative combination of state-of-the-art numerical simulations, sensors, and airborne platforms. The state-of-the-art for UAVs now covers a broad spectrum including their deployment in fleets (e.g., for cooperative ground mapping); however, the consideration of a UAV fleet to gather information within a dynamic volume is only very recent. A cooperative, control architecture will be defined, that manages the inter-UAV communication constraints, and adaptively plans and controls their flights to maximize the utility of the gathered data. NEPHELAE is an initiative with no equivalence at the national level and is on the leading edge of atmospheric and robotic research on the international level. The project uses MesoNH Large Eddy Simulations (LES) to optimize the strategy for a UAV fleet to characterize the 4D evolution of the boundary layer surrounding a cloud, as well as the cloud itself. The simulations have been exploited to define a generic conceptual model of cumulus clouds, which is the basis to define overall strategies for the fleet control. The cloud mapping and exploration algorithms will be thoroughly validated with realistic simulations that imply dense cloud models produced by MesoNH simulations, on-board sensors models and UAV dynamic models. Field trials will help to validate and refine the developments, and the UAV fleet will then be deployed in a field campaign to assess model parametrization of entrainment and the onset of precipitation.

The interdisciplinary atmospheric science and technical objectives of NEPHELAE necessitate expertise from the domains of atmospheric sciences and aeronautical / robotic technologies, which have been well-demonstrated by the equally interdisciplinary consortium proposed here.