摘要:
世界时与协调世界时差值(difference between universal time and coordinated universal time,UT1-UTC)是地球定向参数(Earth Orientation Parameters,EOP)的重要组成部分,其高精度和快速预测对GNSS气象学、人造卫星精密轨道确定等实时应用领域至关重要。传统UT1-UTC预报方法在中长期预测中精度衰减明显,难以满足北斗导航系统及战争环境的精确制导等的高精度需求。提出一种融合地球流体有效角动量(effective angular momentum,EAM)信息的轴向分量χ3数据与EOP14 C04序列的卷积长短期记忆神经网络(convolutional long short-term memory,ConvLSTM)模型预报UT1-UTC的新方法。实测数据分析结果发现,EAM轴向分量χ3和UT1R-TAI数据具有强相关性,其振幅和相位具有一致的频谱特性,说明EAM轴向分量χ3是UT1-UTC的主要激发源。和参与第二届EOP预报比赛(second Earth orientation parameters prediction comparison campaign,2nd EOP PCC)的各家精度进行对比,在90~360天的中长期预报跨度中,ConvLSTM模型预报精度最优,改善幅度为30.27%~92.44%。对比Bulletin A,时间跨度为60天、180天和360天的中长期预报精度分别提升41.46%、70.07%和59.43%,证实了ConvLSTM新方法能够显著改善UT1-UTC的中长期预报精度。研究成果对EOP的自主确定与实时应用、人造卫星精密轨道确定等领域至关重要。
Abstract:
Objectives: The difference between Universal Time and Coordinated Universal Time (UT1-UTC) is a critical component of the Earth Orientation Parameters (EOP). Accurate and rapid prediction of UT1-UTC is essential for real-time applications such as GNSS meteorology and precise orbit determination of artificial satellites. Traditional methods for predicting UT1-UTC often experience significant accuracy degradation in medium- and long-term forecasts, making them inadequate for high-precision requirements in applications like the BeiDou Navigation Satellite System and precision guidance in military operations. Methods: We propose a novel method for predicting UT1-UTC by integrating the axial component data χ3 of Effective Angular Momentum (EAM) with the EOP14 C04 series using a Convolutional Long Short-Term Memory (ConvLSTM) model. Initially, leap seconds and solid Earth tide terms are removed from the original UT1-UTC series to obtain UT1R-TAI data. Spectral analysis is then performed on the axial component data χ3 of EAM and UT1R-TAI data using the fast Fourier transform (FFT) to investigate whether the axial component data χ3 of EAM can comprehensively describe the excitation of UT1-UTC. Subsequently, a ConvLSTM model incorporating the axial component data χ3 of EAM is constructed to predict the UT1-UTC time series. After prediction, the leap seconds and solid Earth tide terms are reintroduced, while the accuracy of predictions is evaluated. Results: Analysis of observations reveals a strong correlation between the axial component χ3 of EAM and UT1R-TAI data, with consistent amplitude and phase characteristics in their frequency spectra. This indicates that the axial component χ3 of EAM serves as a primary excitation source for UT1-UTC. Compared to the prediction accuracy of participants in the Second Earth Orientation Parameters Prediction Comparison Campaign (2nd EOP PCC), the ConvLSTM model demonstrates superior performance in medium- to long-term predictions spanning 90 to 360 days, with accuracy improvements ranging from 30.27% to 92.44%. Additionally, compared to Bulletin A, the ConvLSTM model achieves accuracy enhancements of 41.46%, 70.07%, and 59.43% for prediction spans of 60, 180, and 360 days, respectively. Conclusions: The results confirm that the ConvLSTM model significantly improves the medium- to long-term prediction accuracy of UT1-UTC. These findings are crucial for autonomous determination of EOP and real-time applications, as well as for precise satellite orbit determination and other related fields.