Assessment of Using UAT/ADS-B Onboard Data to Simulate 3D Aviation Weather Flow Information

  • 劉 冠志

Student thesis: Master's Thesis


The existing aviation unusual weather alert system uses observation data from several ground-based meteorological stations to simulate or predict weather information with specific computational fluid dynamic models However most ground meteorological stations are near the ground surface level As a result the simulation results from computational fluid dynamic models can only display weather conditions in two-dimensional proximately With only two-dimensional weather information it would be very difficult for the unusual weather alert system to issue a warning for unusual low-level weather conditions such as low-level wind shear and microbursts In other respects Automatic Dependent Surveillance - Broadcast (ADS-B) systems enable aircraft to update their states and observation information in real time The information includes their location altitude airspeed track angle and pressure Consequently the goal of this thesis is to verify the possibility of using aircraft onboard data broadcasted from an ADS-B system and observation data from ground meteorological stations to simulate a three-dimensional wind flow field In other words aircraft are regarded as meteorological stations with vertical profiles for the purpose of this work In order to utilize the onboard data in the ADS-B signal the ADS-B Mode S ES signal at 1090MHz and Universal Access Transceiver (UAT) signal at 978MHz have to be decoded and thus a UAT/ADS-B software-defined receiver is developed in this thesis Furthermore the CALMET/CALPUFF modeling system is selected in this thesis to simulate the three-dimensional wind flow field Finally the results of UAT/ADS-B signal decoding and the flow field simulation are demonstrated and analyzed in this work as well In this thesis we successfully establish the three-dimensional wind flow field simulation with the onboard data decoded using the UAT/ADS-B software-defined receiver and observation data from ground meteorological stations
Date of Award2014 Aug 27
Original languageEnglish
SupervisorShau-Shiun Jan (Supervisor)

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