Exposing Spatio-Temporal structures of turbulence in the Atmospheric Boundary Layer with In-Situ measurements by a fleet of Unmanned Aerial Systems

Objective

Exchange and transport processes in the atmospheric boundary layer (ABL) are driven by turbulence on a wide range of scales. Their adequate parameterization in numerical weather prediction (NWP) models is essential for a high predictive skill of forecasts. In heterogenous and complex terrain, the common simplification of turbulence to statistical models does not necessarily hold. Coherent structures such as convective cells, secondary circulations, gusts, slope and valley flows can be summarized to sub-mesoscale structures which are not well represented in models. A reason for the lack of understanding of these flow features is the challenge to adequately sample their spatio-temporal structure and their contribution to the energy budget of the ABL.

The project ESTABLIS-UAS will provide methods to expose spatio-temporal structures in the ABL with in-situ measurements by a fleet of unmanned aerial systems (UAS). For this purpose, small, rotary-wing UAS will be enabled to measure three-dimensional wind, temperature and humidity in organized, spatially distributed networks. The project will include a three-fold approach to validate single UAS measurements, fleet observations and methods to derive spatial averages and fluxes. Wind tunnel tests, field experiments and virtual measurements in numerical simulations will be performed.

The validated UAS fleet will be deployed in two campaigns in the framework of the TEAMx research programme, focusing on the mountain boundary layer (MoBL). The ESTABLIS-UAS measurements will fill observational gaps in the sub-mesoscale. The analysis of the UAS fleet data in synthesis with ground observations and remote sensing will provide unprecedented new insights into the complex MoBL flow and the components of its energy budget. The results will foster the development of new and better parameterization of the ABL in complex terrain.