Abstract:
Uncrewed aircraft systems (UAS) have been used to improve understanding of atmospheric phenomena that are typically poorly observed by conventional observing systems. By virtue of their controlled/directed flight in conditions too hazardous for manned instrumentation and their ability to collect thermodynamic observations with greater accuracy and higher fidelity than other sensing systems, UAS can expose the structure of atmospheric phenomena enabling refined understanding of foundational mechanisms that control the evolution of these phenomena. Additionally, evidence is growing to justify the operationalization of UAS for weather forecasting. In this vision for a modernized meteorological observing network, regular observations of the atmosphere by UAS would be available in real-time to forecasters, assimilated into numerical weather prediction models, and used to train and initialize AI models.
In this talk, results will be presented from research using UAS data to better understand severe deep convection and the hazards it produces. Results will also be presented from experiments aimed at evaluating the impact of UAS data on numerical weather prediction.