3 plots with a time series showing turbulence kinetic energy (top), UAV battery drainage due to elevated turbulence conditions (center and bottom).
Time series for Dallas of: (a) turbulence kinetic energy (b) and battery drainage due to elevated turbulence conditions experienced a small UAV (unmanned aerial vehicle) with a constant speed of 1 m s−1 and with a SW-NE direction (c). Each line corresponds to the weather conditions of one of the 31 days of January 2018 at 12 LT (gray), with colored lines indicating the maximum TKE day (11 January, red), minimum TKE day (6 January, blue) and the representative weather for that month and time of the day (19 January, black). The black dash-dotted line in (c) is the reference for weak turbulence that would not require any additional power to be overcome, and that would last for a 25-min long flight. The distribution of battery reduction factors is displayed at the bottom left portion of that same panel. Credit: Muñoz-Esparza et al. [2021], Figure 10
Source: AGU Advances

New modes of aerial operations are emerging in the urban environment, collectively known as Advanced Air Mobility (AAM). These include electrically propelled vertical takeoff and landing aerial vehicles for infrastructure surveillance, goods delivery, and passenger transportation. However, ultra-fine weather and turbulence guidance products are needed that contribute to safe and efficient deployment of these activities. In fact, initial testing/demonstration exercises are planned to occur in the very near future, thus the timely and relevant nature of the present work.

To enable successful operation of these new aerial operations in the urban environment, the meteorological community must provide relevant guidance to inform and support these activities. Muñoz-Esparza et al. [2021] demonstrate how seasonal, diurnal, day-to-day, and rapidly evolving sub-hourly meteorological phenomena create unique wind and turbulence distributions within the urban canopy. They showcase the potential for efficient ultra-fine resolution atmospheric models to understand and predict urban weather impacts that are critical to these AAM operations.

Citation: Muñoz-Esparza, D., Shin, H., Sauer, J. et al. [2021]. Efficient GPU Modeling of Street-Scale Weather Effects in Support of Aerial Operations in the Urban Environment. AGU Advances, 2, e2021AV000432. https://doi.org/10.1029/2021AV000432

—Donald Wuebbles, Editor, AGU Advances

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