Establishing High-Resolution Meteorological Monitoring for Safe Urban Air Mobility Operations

Urban Air Mobility (UAM) is emerging as a next-generation transportation system that not only alleviates traffic congestion in high-density urban areas but also supports emergency medical response, time-critical logistics and supply delivery, and urban and regional tourism. UAM vehicles operate at low altitudes within the atmospheric boundary layer, where airflow and turbulence are strongly modified by topography, buildings, and other urban structures. In such environments, localized meteorological phenomena, such as gusts, vertical wind shear, and turbulence, frequently develop and pose significant challenges to flight stability and operational safety.

Conventional meteorological observation networks and mesoscale numerical weather prediction models lack the spatial and temporal resolution required to resolve these microscale urban flow features. Consequently, short-term forecasting of boundary-layer winds and turbulence in complex urban environments remains highly uncertain. To support safe UAM operations, a new observation framework providing high-resolution, three-dimensional meteorological information is needed. High-frequency, multi-point surface and remote-sensing observations can capture spatiotemporal variability of meteorological conditions and provide essential inputs for data assimilation in numerical prediction models as well as for training and constraining artificial-intelligence-based forecast systems designed to generate high-fidelity, short-term meteorological fields for UAM operations.

We propose a multi-point meteorological observation network designed to characterize wind and turbulence fields within UAM corridors. The network is configured to resolve the spatiotemporal variability of winds and turbulence in the boundary layer with high fidelity. The resulting dataset can enhance the understanding of urban low-altitude meteorology and provide a foundational dataset for high-resolution forecasting and operational decision-support for safe and efficient UAM operations.

 

This work was funded by the Korea Meteorological Administration Research and Development Program under Grant (RS-2024-00404042).