热带气象学报2026，Vol.42Issue(1)：122-131,10.DOI:10.16032/j.issn.1004-4965.2026.010
基于深度学习的机场风长时序预测
Long-term Wind Prediction at Airports Based on Deep Learning
石雨卉 1孙凯 2徐颖 2郭炜峻2
作者信息
- 1. 中国民用航空厦门空中交通管理站,福建 厦门 361006||华东空管局空管数据分析及应用实验室,福建 厦门 361006
- 2. 中国民用航空厦门空中交通管理站,福建 厦门 361006
- 折叠
摘要
Abstract
To address the issues of insufficient accuracy and poor timeliness in traditional wind field prediction methods,this study introduced the Informer model to enhance the forecast accuracy of the long-term meteorological data at Xiamen Gaoqi International Airport.The paper details the unique advantages of the Informer model in handling wind field time series data,including its probabilistic sparse self-attention mechanism and self-attention distillation technology.These features enable the model to efficiently capture long-term dependencies and complex characteristics within the data.Compared with traditional Artificial Neural Networks(ANNs)and Long Short-Term Memory(LSTM)models,the Informer model demonstrates higher prediction accuracy across different time scales.In the 60-minute predictions and seasonal variations,the Informer model demonstrated high robustness and efficiency.Additionally,a comparison of the effects of different wind field variations on the model's wind field predictions revealed that the Informer model consistently maintained stable predictive performance under varying wind field conditions,further validating its broad applicability and robustness.By enhancing prediction accuracy and timeliness,this research not only provides more accurate wind speed and direction forecasts for aviation meteorological services,aiding in flight safety,optimizing flight scheduling,and improving energy efficiency,but also has a positive impact on short-term weather forecasting and offers new research ideas and solutions.It has significant implications for advancing the application of deep learning in meteorological forecasting.关键词
深度学习/风场预测/长时间序列/Informer模型/航空气象Key words
deep learning/wind field prediction/long-term sequences/Informer model/aviation meteorology分类
天文与地球科学引用本文复制引用
石雨卉,孙凯,徐颖,郭炜峻..基于深度学习的机场风长时序预测[J].热带气象学报,2026,42(1):122-131,10.
