Application of GRIB Data for 4D Trajectory
Prediction
Yibing Yin
(&) and Ming Tong
State Key Laboratory of Air Traffic Management System and Technology,
Nanjing 210016, China
yinyibing1992@163.com
Abstract. The accuracy of 4D trajectory prediction is the foundation of
trajectory-based operation and decision support tools of ATC. Meteorological
modeling, especially the wind modeling, is the key element for improving the
precision of trajectory prediction. Through analyzing the wind data in GRIB
(GRIdded Binary) format, wind speed and direction at each levels can be
obtained. Meanwhile, the 4D trajectory prediction model is constructed based on
the wind effect to aircraft movements, especially ground speed and heading of
aircraft. Taking arrival flights to Shanghai Pudong International Airport as an
example, a simulation is conducted through the comparison of prediction results
and the radar information. And the simulation results indicate the effectiveness
of the proposed approach.
Keywords: Trajectory prediction Á GRIB data Á Wind data Á Meteorological
modeling
1 Introduction
The aircraft 4D track is an accurate description of the spatial position (longitude,
latitude, and altitude) and time of each point in an aircraft track in space and time [1].
The accuracy prediction of 4D trajectory is the technical basis of a new generation of
air traffic control automation system based on track operation. It is widely used in flight
sorting [2], early warning and cancelation of flight conflicts [3], and track optimization.
At present, there are two main methods for aircraft flight trajectory predicting: First,
based on the hybrid estimation theory [4] to achieve track prediction, after all, aircraft
motion not only has multi-modal properties, but also needs to consider its threedimensional state in the horizontal, vertical and lateral directions. Therefore, track
prediction can be regarded as an estimation problem of stochastic linear hybrid systems
[5]. The second is based on aircraft dynamics and kinematics models, using various
performance parameters of the aircraft to achieve track prediction [6].
For aircraft dynamics and kinematics based on track prediction studies, accurate
data support can improve the effectiveness of predictions, and meteorological data is
one kind of important data. The influence of wind data in meteorological data on track
prediction is particularly important. If wind speed and direction are lacking, the
accuracy of aircraft four-dimensional trajectory prediction will be reduced. However,
due to the difficulty in obtaining wind data, in the current domestic 4D track prediction
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 422–430, 2020.
https://doi.org/10.1007/978-981-15-0187-6_50
Prediction
Yibing Yin
(&) and Ming Tong
State Key Laboratory of Air Traffic Management System and Technology,
Nanjing 210016, China
yinyibing1992@163.com
Abstract. The accuracy of 4D trajectory prediction is the foundation of
trajectory-based operation and decision support tools of ATC. Meteorological
modeling, especially the wind modeling, is the key element for improving the
precision of trajectory prediction. Through analyzing the wind data in GRIB
(GRIdded Binary) format, wind speed and direction at each levels can be
obtained. Meanwhile, the 4D trajectory prediction model is constructed based on
the wind effect to aircraft movements, especially ground speed and heading of
aircraft. Taking arrival flights to Shanghai Pudong International Airport as an
example, a simulation is conducted through the comparison of prediction results
and the radar information. And the simulation results indicate the effectiveness
of the proposed approach.
Keywords: Trajectory prediction Á GRIB data Á Wind data Á Meteorological
modeling
1 Introduction
The aircraft 4D track is an accurate description of the spatial position (longitude,
latitude, and altitude) and time of each point in an aircraft track in space and time [1].
The accuracy prediction of 4D trajectory is the technical basis of a new generation of
air traffic control automation system based on track operation. It is widely used in flight
sorting [2], early warning and cancelation of flight conflicts [3], and track optimization.
At present, there are two main methods for aircraft flight trajectory predicting: First,
based on the hybrid estimation theory [4] to achieve track prediction, after all, aircraft
motion not only has multi-modal properties, but also needs to consider its threedimensional state in the horizontal, vertical and lateral directions. Therefore, track
prediction can be regarded as an estimation problem of stochastic linear hybrid systems
[5]. The second is based on aircraft dynamics and kinematics models, using various
performance parameters of the aircraft to achieve track prediction [6].
For aircraft dynamics and kinematics based on track prediction studies, accurate
data support can improve the effectiveness of predictions, and meteorological data is
one kind of important data. The influence of wind data in meteorological data on track
prediction is particularly important. If wind speed and direction are lacking, the
accuracy of aircraft four-dimensional trajectory prediction will be reduced. However,
due to the difficulty in obtaining wind data, in the current domestic 4D track prediction
© Springer Nature Singapore Pte Ltd. 2020
Q. Liang et al. (Eds.): Artificial Intelligence in China, LNEE 572, pp. 422–430, 2020.
https://doi.org/10.1007/978-981-15-0187-6_50