A Study on the Precursors of Aviation Turbulence via QAR Data Analysis

QAR 데이터 분석을 통한 항공난류 조기 인지 가능성 연구

Abstract

Although continuous passenger injuries and physical damages are repeated due to the unexpected aviation turbulence encountered during operations, there is still exist the limitation for preventing recurrence of similar events because the lack of real-time information and delay in technological developments regarding various operating conditions and variable weather phenomena. The purpose of this study is to compare and analyze the meteorological data of the aviation turbulence occurred and actual flight data extracted from the Quick Access Recorder(QAR) to provide some precursors that the pilot can identify aviation turbulence early by referring thru the flight instrumentation indications. The case applied for this study was recent event, a scheduled flight from Incheon Airport, Korea to Narita Airport, Japan that suddenly encountered turbulence at an altitude of approximately 14,000 feet during approach. According to the Korea Meteorological Administration(KMA)'s Regional Data Assessment and Prediction System(RDAPS) data, it was observed that the strong amount of vorticity in the rear area of jet stream, which existed near Mount Fuji at that time. The QAR data analysis shows significant changes in the aircraft's parameters such as Pitch and Roll angle, Static Air Temperature(SAT), and wind speed and direction in tens of seconds to minutes before encounter the turbulence. If the accumulate reliability of the data in addition and verification of various parameters with continuous analysis of additional cases, it can be the precursors for the pilot's effective and pre-emptive action and conservative prevention measures against aviation turbulence to reduce subsequent passenger injuries in the aviation operations.

Fig 1. Company SIGWX Chart

Fig 2. RDAPS_700hpa Chart at 10,000ft

Fig 3. ALT vs Vertical G

Fig 4. Pitch vs Vertical G

Fig 5. Roll vs Vertical G

Fig 6. SAT vs Vertical G

Fig 7. Wind Speed vs Vertical G

Fig 8. Wind Direction vs Vertical G

Table 2. QAR Data Analysis

Table 1. QAR Data Parameter List