Turbulence Awareness via Real-Time Data Mining

We propose to create an automated, real-time, remote turbulence detection and diagnostics system for the National Airspace System (NAS). The system is remote in the sense that it does not mount any sensors onboard any aircraft, nor does it add any software to Flight Deck (FD) avionics systems. The system exploits data mining to search through thousands of aircraft surveillance measurements in real-time as aircraft fly in the NAS. We propose to use Automatic Dependent Surveillance ? Broadcast (ADS-B) information as the basis of atmospheric wave and turbulence detection, and combine this with satellite-based visual and infrared imagery to complete the diagnostics. We design the system to access a large network of ADS-B receivers across the NAS. Automated analysis of ADS-B aircraft altitude and velocity information is used to detect the presence of mountain waves and Mountain Wave Turbulence (MWT) in the vicinity of steep terrain as well as atmospheric waves and turbulence from other sources, for instance, Convective Induced Turbulence (CIT). When combined with other weather state information gained by in situ sensors, satellite, and radar-based technology in the NAS, our SBIR effort will allow for a total situational awareness of mountain wave, MWT, and CIT information in the Continental United States (CONUS). Because ADS-B is mandated by 2020, the percentage of aircraft using ADS-B will grow each year, and this in turn will benefit all who use our innovation.