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Completed TRL 4 (started at 4, targeting 7)
WINDMAP addresses the emerging needs in the aviation community of providing real-time weather forecasting to improve the safety of low altitude aircraft operations. This is accomplished through the integration of real-time observations from autonomous systems, such as drones and urban air taxis, with numerical weather prediction models and flight management and safety systems. To solve this problem, several technical challenges have been identified. These include (1) developing autonomous UAS capable of conducting observations accurately and reliably; (2) determining the number and frequency of required observations and the sensitivity of these observations in data sparse regions of the lower atmosphere; (3) assimilating dense observational data into models in real-time with sufficient resolution and accuracy; (4) developing novel physics-based reduced order models capable of incorporating diverse data sets; and (5) integrating real-time forecasting into UTM and DAA (detect-and-avoid) architectures for path planning and navigation. The goal of this proposed effort is to demonstrate the value of using small UAS to collect measurements of the dynamic and thermodynamic properties of the lower atmosphere at scales that match or exceed the spatio-temporal resolution of today’s best numerical weather prediction models.
The direct assimilation of data from autonomous platforms into weather models represents a paradigm shift in the way aviation weather hazards will be detected and predicted in the future. By providing real-time predictive capabilities for real-world dynamic weather environments capable of generating fine scale information for system-wide assurance focused on advanced aerial mobile trajectory planning, this effort will provide manned- and unmanned aircraft improved situational awareness to enhance safety and efficiency, particularly for unmanned traffic management, urban air mobility, and airport operations.
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