基于深度学习神经网络对西南地区三维颠簸预报的研究

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Abstract This study conducts a systematic research on the prediction of aircraft turbulence in the southwest region based on deep learning neural networks.By integrating aircraft turbulence report data from March 2008 to March 2024 and ERA5 reanalysis data,a complete dataset containing 5 236 turbulence events and 15 708 nonturbulence events was constructed.The research adopted a deep learning neural network model to construct five hidden layers and ReLU and Sigmoid activation functions,achieving a digital expression of the joint prediction of turbulence intensity and height.Research has found that turbulence events are mainly concentrated in the medium to high altitude (4 200 to 6 300 meters) and moderate intensity range,and their distribution is closely related to meteorological dynamic processes such as jet stream axes and front areas.The results show that the model has a good prediction effect on medium and highlevel and moderate turbulence,with an accuracy rate of 77.35%,and the loss function converges stably to 0.15.When distinguishing between "with bumps" and "without bumps",the model's accuracy rate is as high as 99.93%.However,there is still room for improvement in the prediction of extreme intensity or high turbulence.This study provides an effective method for aviation turbulence prediction in the southwest region and points out the future optimization direction.

Key words： deep learning neural network southwest region analysis of bumpy events threedimensional prediction of turbulence

Received: 18 July 2025

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	Articles by authors
	Deng Xiaoguang
	Luo Ya
	Huang Jinquan
	Chen Yiyi
	Xu Wenwen
	Li Junyao

Cite this article:

Deng Xiaoguang,Luo Ya,Huang Jinquan等. Research on 3D bump prediction in southwest China based on deep learning neural network[J]. Meteorological Hydrological and Marine Instrument, 2025, 42(5): 47-53.

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http://www.qxswhy.com/EN/ OR http://www.qxswhy.com/EN/Y2025/V42/I5/47

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