Abstract
Convective weather is an important weather phenomenon that affects aircraft operation, and the determination and analysis of convective weather characteristic thresholds is the basis and premise for airspace availability analysis and aircraft diversion. This paper innovatively proposes a threshold analysis method for aircraft to avoid convective weather. Firstly, the historical meteorological data and the track data are adopted in spatiotemporal and synchronous fusion. Secondly, the K-means clustering algorithm is used to determine the characteristic threshold range of the aircraft to avoid convective weather. Then, combined with the random forest classification algorithm, each threshold is again classified 0-1 through machine learning to determine the best avoidance threshold for weather features. Finally, the new construction index evaluation is used to evaluate the reliability of the algorithm. According to the threshold analysis method, the radar reflectivity factor is taken as the research object to carry out an example analysis. The example shows that when the radar reflectivity value is 34 dBZ, the accuracy rate is 96.58 %, the false alarm rate is 2.45 %, and the missing alarm rate is 0.97 %, all of which are better than the decision tree method, thus verifying the reliability and practicability of the algorithm.
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