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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摘要文章基于深度学习神经网络对航空器在西南地区颠簸的预报进行了系统研究。通过整合2008年3月至2024年3月的航空器颠簸报告数据和ERA5再分析数据,构建了一个包含5 236次颠簸事件和15 708次非颠簸事件的完整数据集。研究采用深度学习神经网络模型,构建了5层隐藏层和ReLU、Sigmoid激活函数,实现了对颠簸强度和高度联合预测的数字化表达。研究发现,颠簸事件主要集中在中高海拔(4 200～6 300 m)和中度强度范围内,其分布与急流轴、锋区等气象动力过程密切相关。结果表明,模型对中高层和中度颠簸的预测效果较好,准确率达77.35%,且损失函数稳定收敛至0.15。在区分“有颠簸”和“无颠簸”时,模型准确率高达99.93%。然而,对极端强度或高度颠簸的预测仍有提升空间。本研究为西南地区航空颠簸预报提供了有效方法,并指明了未来优化方向。

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	邓小光
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	陈义义
	徐雯雯
	李俊瑶

关键词 ：深度学习神经网络, 西南区域, 颠簸事件分析, 颠簸三维预测

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

收稿日期: 2025-07-18

ZTFLH:

P457.6

基金资助:中国气象局航空气象重点开放实验室面上项目(HKQXM2024023)资助。

作者简介: 邓小光(1995),男,硕士,工程师。主要从事民航气象研究。

引用本文:

邓小光, 罗娅, 黄金全, 陈义义, 徐雯雯, 李俊瑶. 基于深度学习神经网络对西南地区三维颠簸预报的研究[J]. 气象水文海洋仪器, 2025, 42(5): 47-53. Deng Xiaoguang, Luo Ya, Huang Jinquan, Chen Yiyi, Xu Wenwen, Li Junyao. Research on 3D bump prediction in southwest China based on deep learning neural network. Meteorological Hydrological and Marine Instrument, 2025, 42(5): 47-53.

链接本文:

http://www.qxswhy.com/CN/ 或 http://www.qxswhy.com/CN/Y2025/V42/I5/47

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