Examining the Impact of Different Uncrewed Aerial System (UAS) Sampling Strategies on Fog Forecasts in the Ohio Valley Using Observing System Simulation Experiments (OSSEs)

Dense fog events can be highly impactful, causing major airport delays, aviation accidents, and highway pileups. However, they are often difficult to predict due in part to a lack of dense spatiotemporal sampling of the nocturnal planetary boundary layer (PBL). Uncrewed aerial systems (UASs) represent a potential source of such dense PBL observations; however, the number of UAS needed for a positive forecast impact and the best sampling strategies to pursue with them for the greatest positive impact on a model forecast are still unknown. Using a series of observing system simulation experiments (OSSEs) focusing on three impactful fog events in the Ohio valley, this study compares the impacts of two different UAS sampling strategies: a 3D mesonet-like experiment with profiling UAS located at selected surface observing sites and an experiment testing the impact of observations collected by a network of delivery drones. Results indicate that assimilating UAS observations collected with both sampling strategies similarly improves fog forecast skill in the OSSEs. However, the profiling UAS experiment led to larger improvements in analyses and forecasts of temperature and dewpoint aloft, suggesting that this network configuration may be more useful for improving predictions of aviation hazards that may be more dependent on the accuracy of the analyses well above the surface, such as low ceilings, low-level wind shear, precipitation phase, and the initiation and evolution of deep convection.