After comparative analysis, it can be seen that the track prediction result of adding
wind data is more accurate, indicating the integration of wind data in the GRIB data
format for meteorological modeling can effectively improve the accuracy of the track
prediction tool.
5 Conclusion
In this paper, the processing flow of wind information in GRIB data format is given in
the process of track prediction, and the meteorological model supported by wind data is
integrated into the track prediction model to carry out four-dimensional track prediction. For more complex meteorological modeling, and the impact of weather forecast
information on track prediction, it will be an important research direction in the future.
References
1. Korn B, Helmke H, Kuenz A (2006 July) 4D trajectory management in the extended TMA:
coupling AMAN and 4D FMS for optimized approach trajectories. In: Silicon Valley,
California: 25th International Congress of the Aeronautical Sciences (ICAS 2006)
2. Schroeder JA (2009) A perspective on NASA Ames air traffic management research. AIAA,
2009-7054
3. Ruiz S, Piera M, Pozo I (2013) A medium term conflict detection and resolution system for
terminal maneuvering area based on spatial data and 4D trajectories. Transp Res Part C
26:396–417
4. Hwang I (2003) Air traffic surveillance and control using hybrid estimation and protocol—
based conflict resolution. Stanford University, USA
5. Liu W, Hwang I (2011) Probabilistic 4D trajectory prediction and conflict detection for air
traffic control. In: IEEE conference on decision & control
Table 1. Trajectory prediction analysis of flight CCA4503
Route
point
Pass time (without wind data)
Actual arrival time
Estimated arrival time
VMB
7:14:35
7:14:35
7:14:35
EKIMU
7:21:00
7:20:08
7:20:50
JTN
7:24:04
7:22:58
7:23:48
PD019
7:26:11
7:24:59
7:25:54
IAF
7:27:21
7:26:10
7:27:11
PD015
7:28:17
7:26:55
7:27:57
PD014
7:29:39
7:28:10
7:29:19
PD013
7:31:43
7:30:23
7:31:23
PD012
7:33:05
7:32:00
7:33:45
PD023
7:34:33
7:33:56
7:34:25
IF
7:37:17
7:37:03
7:37:07
FAF
7:37:55
7:37:41
7:37:47
Application of GRIB Data for 4D Trajectory Prediction
429
wind data is more accurate, indicating the integration of wind data in the GRIB data
format for meteorological modeling can effectively improve the accuracy of the track
prediction tool.
5 Conclusion
In this paper, the processing flow of wind information in GRIB data format is given in
the process of track prediction, and the meteorological model supported by wind data is
integrated into the track prediction model to carry out four-dimensional track prediction. For more complex meteorological modeling, and the impact of weather forecast
information on track prediction, it will be an important research direction in the future.
References
1. Korn B, Helmke H, Kuenz A (2006 July) 4D trajectory management in the extended TMA:
coupling AMAN and 4D FMS for optimized approach trajectories. In: Silicon Valley,
California: 25th International Congress of the Aeronautical Sciences (ICAS 2006)
2. Schroeder JA (2009) A perspective on NASA Ames air traffic management research. AIAA,
2009-7054
3. Ruiz S, Piera M, Pozo I (2013) A medium term conflict detection and resolution system for
terminal maneuvering area based on spatial data and 4D trajectories. Transp Res Part C
26:396–417
4. Hwang I (2003) Air traffic surveillance and control using hybrid estimation and protocol—
based conflict resolution. Stanford University, USA
5. Liu W, Hwang I (2011) Probabilistic 4D trajectory prediction and conflict detection for air
traffic control. In: IEEE conference on decision & control
Table 1. Trajectory prediction analysis of flight CCA4503
Route
point
Pass time (without wind data)
Actual arrival time
Estimated arrival time
VMB
7:14:35
7:14:35
7:14:35
EKIMU
7:21:00
7:20:08
7:20:50
JTN
7:24:04
7:22:58
7:23:48
PD019
7:26:11
7:24:59
7:25:54
IAF
7:27:21
7:26:10
7:27:11
PD015
7:28:17
7:26:55
7:27:57
PD014
7:29:39
7:28:10
7:29:19
PD013
7:31:43
7:30:23
7:31:23
PD012
7:33:05
7:32:00
7:33:45
PD023
7:34:33
7:33:56
7:34:25
IF
7:37:17
7:37:03
7:37:07
FAF
7:37:55
7:37:41
7:37:47
Application of GRIB Data for 4D Trajectory Prediction
429