UAS Traffic Management: Integrating Weather Information using System Adaptability

Unmanned Aerial Vehicles (UAVs), or drones, are becoming increasingly prevalent in the commercial and defense sectors, operating throughout the continental United States airspace. UAVs have wing sizes ranging from 0.25 centimeters to 61 meters, and they perform a myriad of roles from job site surveillance to 3D mapping of agricultural fields. However, a UAV is not a standalone system, but joins a ground control system, pilot and communications network to form an Unmanned Aircraft System (UAS) platform. With the various UAS platforms of different UAV sizes and missions, the Federal Aviation Administration (FAA) is developing a UAS Traffic Management (UTM) system to safely integrate them into the airspace. UAS ground systems require ingestion, processing, compilation and dissemination of weather data specific to a local area. The NOAA Advanced Quantitative Precipitation Information (AQPI) system has proven to reliably perform these functions. Expanding the mission for a system changes the requirements originally used to design the system. The ability of a system to adjust is the foundation of System Adaptability. Using the concept of System Adaptability and leveraging the existing Digital Twin of the Bay Area AQPI, this paper presents the theoretical application of adapting the San Francisco Bay Area AQPI system to provide meteorological information to UAS platforms operating under the Bay Area UTM.