Aircraft used as a sensor for atmospheric behaviour determination. Practical case: pressure estimation using automatic dependent surveillance-broadcast

Knowledge of the characteristics of the atmosphere will benefit future air traffic management systems, particularly the so-called 'four-dimensional trajectory management'. Trajectory prediction should incorporate variables such as air pressure (p), wind velocity (w) and temperature (T), as well as clouds/visibility, gusts/microbursts and the concentration of particles or other elements that can affect aircraft travel through the atmosphere. Today's commercial aircraft normally carry equipment that contains automatic dependent surveillance-broadcast system which broadcast usable information that can be used for different purposes. This article explores the characteristics of the information supplied by automatic dependent surveillance-broadcast, not for surveillance purposes, but for the provision of valuable atmospheric information that could support four-dimensional trajectory management. It also provides information on the strengths and weaknesses of automatic dependent surveillance-broadcast for supporting atmospheric status estimation and on the main differences between the regulatory framework and the information that is currently available from aircraft. Based on data obtained from an automatic dependent surveillance-broadcast receiver, the study investigates the possibility to determine the actual atmospheric pressure at different points and then uses a mathematical model to estimate the atmospheric pressure at any desired altitude at any time. It goes on further to assess the accuracy of the international standard atmosphere model based on the recorded data. During the data processing it was realised that not all of the data transmitted from the aircraft referred to the same reference surface, that is either the mean sea level or the height above ellipsoid were used. This ambiguity in the reference surface complicated the data processing, especially when the geoid ondulation value was close to the deviation value of the data.