Simulation, modeling, and limited observations have shown that wind farms have an impact on the near-surface atmospheric boundary layer as turbulent wakes enhance vertical mixing of momentum, heat, and moisture. The few observational datasets that do exist lack high spatial resolution due to their use of a limited number of meteorological sensors or remote sensing techniques. This study utilizes an instrumented small unmanned aerial system to gather high-resolution in situ field measurements in order to differentially map near-wake changes to relative humidity. Observations show that downstream relative humidity is differentially altered in the vertical, spanwise and downstream directions.

This paper presents enhancements to, and the demonstration of, the General Urban area Microclimate Predictions tool (GUMP), which is designed to provide hyper-local weather predictions by combining machine-learning (ML) models and computational fluid dynamic (CFD) simulations. For the further development and demonstration of GUMP, the Embry–Riddle Aeronautical University (ERAU) campus was used as a test environment. Local weather sensors provided data to train ML models, and CFD models of urban- and suburban-like areas of ERAU’s campus were created and iterated through with a wide assortment of inlet wind speed and direction combinations. ML weather sensor predictions were combined with best-fit CFD models from a database of CFD flow fields, providing flight operational areas with a fully expressed wind flow field. This field defined a risk map for uncrewed aircraft operators based on flight plans and individual flight performance metrics. The potential applications of GUMP are significant due to the immediate availability of weather predictions and its ability to easily extend to arbitrary urban and suburban locations.

The Federal Aviation Administration (FAA) has put forth a set of regulations (Part 107) that govern small unmanned aerial system (sUAS) operations. These regulations restrict unmanned aircraft (UA) from flying over people and their operation to within visual line of sight (VLOS). However, as new applications for unmanned aerial systems (UAS) are discovered, their capabilities improve, and regulations evolve, there is an increasing desire to undertake urban operations, such as urban air mobility, package delivery, infrastructure inspection, and surveillance. This built environment poses new weather hazards that include enhanced wind shear and turbulence. The smaller physical dimensions, lower mass and reduced ground speed of UA make them more susceptible to these hazards than their manned counterparts and can have a significant impact on their performance, endurance and the likelihood of accomplishing the mission. Due to current restrictions, very few flight hours have been accumulated confronting these unique weather challenges. In the absence of this data, this article explores a diverse body of literature that analyzes urban flow and discusses the relevant insight it provides to sUAS operations.

The urban boundary layer (UBL) is one of the most important and least understood atmospheric domains and, consequently, warrants deep understanding and rigorous analysis via sophisticated experimental and numerical tools. When field experiments have been undertaken, they have primarily been accomplished with either a coarse network of in-situ sensors or slow response sensors based on timing or Doppler shifts, resulting in low resolution and decreasing performance with height. Small unmanned aircraft systems (UASs) offer an opportunity to improve on traditional UBL observational strategies that may require substantive infrastructure or prove impractical in a vibrant city, prohibitively expensive, or coarse in resolution. Multirotor UASs are compact, have the ability to take-off and land vertically, hover for long periods of time, and maneuver easily in all three spatial dimensions, making them advantageous for probing an obstacle-laden environment. Fixed-wing UASs offer an opportunity to cover vast horizontal and vertical distances, at low altitudes, in a continuous manner with high spatial resolution. Hence, fixed-wing UASs are advantageous for observing the roughness sublayer above the highest building height where traditional manned aircraft cannot safely fly. This work presents a methodology for UBL investigations using meteorologically instrumented UASs and discusses lessons learned and best practices garnered from a proof of concept field campaign that focused on the urban canopy layer and roughness sublayer of a large modern city with a high-rise urban canopy.

Several investigations have shown that enhanced mixing brought about by wind turbines alters near-surface meteorological conditions within and downstream of a wind farm. When scalar meteorological parameters have been considered, the focus has most often centered on temperature changes. A subset of these works has also considered humidity to various extents. These limited investigations are complemented by just a few studies dedicated to analyzing humidity changes. With onshore wind turbines often sited in agricultural areas, any changes to the microclimate surrounding a turbine can impact plant health and the length of the growing season; any changes to the environment around an offshore wind farm can change cloud and fog formation and dissipation, among other impacts. This article provides a review of observational field campaigns and numerical investigations examining changes to humidity within wind turbine array boundary layers. Across the range of empirical observations and numerical simulations, changes to humidity were observed in stably stratified conditions. In addition to the role of atmospheric stability, this review reveals that the nature of the change depends on the upstream moisture profile; robustness of the mixing; turbine array layout; distance from the turbine, in all three directions; and vertical temperature profile.