FT prediction method based on adaptive model of meteorological data

Abstract

Due to the various meteorological conditions encountered in the flight process, the uncertainty of FT will change. In order to obtain the FT error, the secondary surveillance radar and aviation weather forecast data are used for analysis. According to the propagation mechanism of uncertainty, an adaptive prediction model of FT uncertainty is established. The input parameters of the adaptive model include Mach number, flight distance, vector wind and temperature. Cluster analysis and linear regression analysis are used to analyze the accuracy of the model. Compared with the static time-of-flight prediction model, the dynamic time-of-flight prediction model can accurately predict the FT even if the weather conditions are very bad. The dynamic time-of-flight prediction model is applied to air traffic management to further test the accuracy of the model. The results show that the adaptive time-of-flight prediction model based on meteorological conditions can accurately predict the arrival time of a flight to a certain waypoint.

Meteorological data modeling and 3D visualization based on adaptive grid structure

“…Classify several time-varying meteorological data in WRF data, and extract relevant atmospheric data for cloud drawing. However, this method has high space complexity and a large amount of calculation [2] . The new cloud optical model COM uses slices instead of particles to optimize the cloud rendering process, and achieves interactive frame rate.…”