在大气动力学研究中,大气风场数据是分析大气动力特性、动力过程以及大气相互作用的重要参数。然而,受多种因素影响,MST（中层—平流层—对流层雷达,Mesosphere-Stratosphere-Troposphere Radar）在对中层高空区域进行探测时,往往无法获得完整的中间层风场数据。为解决这一问题,本研究引入了深度长短期记忆（Long Short-Term Memory, LSTM）时序预测模型,用于补全缺失的风场数据。在传统LSTM时序预测模型的基础上,通过增加网络层数来捕捉数据之间更深层次的关系,从而构建了基于深度LSTM循环神经网络的预测模型,并对模型进行了参数调优。选用武汉MST雷达（崇阳站）大气中间层水平风场数据作为输入,进行模型训练与测试。实验结果表明,深度LSTM循环神经网络在风速预测方面展现出良好的预测性能,所预测的风速与实测数据高度吻合。此外,深度LSTM模型在数据拟合方面具有较强的适应性,能够有效地对缺失数据进行补充,并展现出较好的预测能力。

In the study of atmospheric dynamics, atmospheric wind field data is an important parameter for analyzing the dynamic characteristics, dynamic processes and atmospheric interactions of the atmosphere. However, affected by various factors, when MST (Mesosphere-Stratosphere-Troposphere Radar) detects the upper middle layer area, it often fails to obtain complete wind field data of the middle layer. To solve this problem, this study introduces the Deep Long Short-Term Memory (LSTM) time series prediction model to complete the missing wind field data. Based on the traditional LSTM time series prediction model, by increasing the number of network layers to capture the deeper relationships between data, a prediction model based on the deep LSTM recurrent neural network is constructed, and the parameters of the model are optimized. The horizontal wind field data of the atmospheric middle layer of the Wuhan MST radar (Chongyang Station) is selected as the input for model training and testing. The experimental results show that the deep LSTM recurrent neural network exhibits excellent prediction performance in wind speed prediction, and the predicted wind speed is highly consistent with the measured data. Furthermore, the deep LSTM model has strong adaptability in data fitting, can effectively supplement the missing data, and shows good predictive ability.