双向长短期记忆网络在激光雷达风廓线预测的应用  

Application of Bidirectional Long Short⁃Term Memory Network in Doppler Lidar Wind Profile Prediction

作　　者：廉文超 宋小全[1,2] 郝朝阳 姜萍 Lian Wenchao;Song Xiaoquan;Hao Zhaoyang;Jiang Ping(College of Marine Technology,Faculty of Information Science and Engineering,Ocean University of China,Qingdao 266100,Shandong,China;Laboratory for Regional Oceanography and Numerical Modeling,Qingdao Marine Science and Technology Center,Qingdao 266237,Shandong,China)

机构地区：[1]中国海洋大学信息科学与工程学部海洋技术学院,山东青岛266100 [2]青岛海洋科技中心区域海洋动力学与数值模拟功能实验室,山东青岛266237

出　　处：《光学学报》2024年第24期50-59,共10页Acta Optica Sinica

基　　金：国家重点研发计划(2022YFC3700402,2022YFB3901700);崂山实验室科技创新项目(LSKJ202201202)。

摘　　要：当利用多普勒激光雷达进行测量时,由于断电、低云、降水等影响会产生风廓线数据的缺测。使用双向长短期记忆(Bi-LSTM)网络对风廓线进行预测,当风廓线观测结果缺失时使用预测结果进行插补,以提升风场数据的获取率和连续性。2021年4月,多普勒激光雷达在辽宁省觉华岛地区开展大气风场探测实验,针对因断电和天气条件导致的风廓线数据缺测,使用2021年4月18日-22日的10 min平均风廓线集,探究基于时序的Bi-LSTM模型在风廓线预测方面的性能,并与非时序卷积神经网络(CNN)的预测性能进行对比。实验结果表明,Bi-LSTM模型具有良好的风廓线短期预测能力,基于连续完整的443条10 min平均风廓线训练的Bi-LSTM模型,对未来15条10 min平均的风廓线预测效果较好:u分量和v分量的决定系数分别高于0.6和0.5,均方根误差分别低于2 m·s^(-1)和3 m·s^(-1),平均绝对误差分别低于2 m·s^(-1)和3 m·s^(-1),其预测评估指标值均优于CNN模型。在本案例中,使用Bi-LSTM模型的预测结果对缺测风廓线插补,1000 m高度以下的数据获取率平均提升8百分点。Objective High spatiotemporal resolution atmospheric wind field detection has important applications in pollution transport and diffusion,extreme weather monitoring,numerical weather forecasting,wind resource assessment,and other areas.Coherent Doppler lidar,as an active laser remote sensing device,acquires high spatiotemporal resolution vector wind field verticalstructure information.However,in practical applications,factors such as platform or power supply stability,and weather conditions can lead to missing wind profiles,limiting the application scope of windsensing lidar.Deep learning methods based on historical data modeling have been widely used in wind field prediction.The long shortterm memory(LSTM)network shows good performance in wind field prediction.However,most studies mainly focus on onedimensional temporal or spatial wind fields,while atmospheric wind fields exhibit both temporal and vertical spatial characteristics.Doppler lidar,as a high spatiotemporal resolution atmospheric wind field detection tool,obtains spatiotemporal twodimensional wind field information.Therefore,we propose a method using a bidirectional long shortterm memory(BiLSTM)model applied to wind field detection with lidar for wind profile prediction.The aim is to fully utilize the spatiotemporal twodimensional wind field data observed by the lidar,train a temporal BiLSTM model to capture the temporal variation characteristics of wind profiles,predict future wind profiles,interpolate missing wind profiles,and acquire more continuous wind field information.Methods Our study focuses on Doppler lidar atmospheric wind field detection experiments in Juehua Island,Liaoning Province,China.We utilize complete wind profile data for modeling and validation to predict and interpolate deficient wind profiles detected by the lidar.Previous complete wind profile data segments serve as the training and validation sets to establish wind profile prediction models based on a timeseries BiLSTM model and a nontime series convolutional neural network(C

关 键 词：大气光学 多普勒激光雷达 深度学习 双向长短期记忆网络 风廓线预测 

分 类 号：P412.25[天文地球—大气科学及气象学] P425.1