Interpretable semi-supervised clustering enables universal detection and intensity assessment of diverse aviation hazardous winds

Hang Gao, Chun Shen, Xuesong Wang, Pak-Wai Chan, Kai-Kwong Hon and Jianbing Li ()
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Hang Gao: National University of Defense Technology
Chun Shen: National University of Defense Technology
Xuesong Wang: National University of Defense Technology
Pak-Wai Chan: Hong Kong Observatory
Kai-Kwong Hon: Hong Kong Observatory
Jianbing Li: National University of Defense Technology

Nature Communications, 2024, vol. 15, issue 1, 1-9

Abstract: Abstract The identification of aviation hazardous winds is crucial and challenging in air traffic management for assuring flight safety, particularly during the take-off and landing phases. Existing criteria are typically tailored for special wind types, and whether there exists a universal feature that can effectively detect diverse types of hazardous winds from radar/lidar observations remains as an open question. Here we propose an interpretable semi-supervised clustering paradigm to solve this problem, where the prior knowledge and probabilistic models of winds are integrated to overcome the bottleneck of scarce labels (pilot reports). Based on this paradigm, a set of high-dimensional hazard features is constructed to effectively identify the occurrence of diverse hazardous winds and assess the intensity metrics. Verification of the paradigm across various scenarios has highlighted its high adaptability to diverse input data and good generalizability to diverse geographical and climate zones.

Date: 2024
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DOI: 10.1038/s41467-024-51597-y

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