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 大地测量与地球动力学  2020, Vol. 40 Issue (11): 1188-1193  DOI: 10.14075/j.jgg.2020.11.016

引用本文

ZHANG Xinfeng, LIU Genyou. Extraction of Interannual Signals in the Length-of-Day Variation and Correlation Analysis with the Atmosphere[J]. Journal of Geodesy and Geodynamics, 2020, 40(11): 1188-1193.

Foundation support

National Natural Science Foundation of China, No. 41774017, 41621091; National Key Research and Development Program of China, No. 2016YFB0501900.

Corresponding author

LIU Genyou, PhD, professor, majors in geodesy and GNSS data processing, E-mail: liugy@whigg.ac.cn.

第一作者简介

ZHANG Xinfeng, postgraduate, majors in time series analysis of the Earth rotation parameters, E-mail: zhangxinfeng@whigg.ac.cn.

文章历史

1. 中国科学院精密测量科学与技术创新研究院大地测量与地球动力学国家重点实验室，武汉市徐东大街340号，430077;
2. 中国科学院大学，北京市玉泉路19号甲，100049

1 信号提取方法

2 仿真实验分析

 $\begin{array}{l} f\left( t \right) = 0.04t + 0.001{t^2} + 0.8\cos \left( {2{\mathtt{π}}t/4.7 + 1} \right) + \\ \;\;\;\;\;\;\;\;\;\;\;1.5{{\rm{e}}^{ - 0.015t}}\cos \left( {2{\mathtt{π}}t/6 + 0.5} \right) + \varepsilon \left( t \right) \end{array}$

 图 1 复合信号f(t) Fig. 1 The composite signal f(t)

 图 2 目标信号f1(t)和f2(t)的标准Morlet小波振幅谱以及时域提取结果与原始仿真信号的比较 Fig. 2 The normal Morlet wavelet amplitude spectrum of target signal f1(t), f2(t) and comparison of the extracted result with the original simulation signal
3 日长年际信号的提取及其与大气相关性分析 3.1 数据资料及预处理

 图 3 ΔLOD和AAM时间序列 Fig. 3 Time series of ΔLOD and AAM
3.2 日长年际信号提取

 图 4 ΔLOD时间序列在年际频段(1 a＜周期＜10 a)的频谱 Fig. 4 The frequency spectrum of ΔLOD time series in interannual frequency range from 1 a to 10 a

 图 5 日长信号的标准Morlet小波振幅谱和时域提取结果 Fig. 5 The normal Morlet wavelet amplitude spectrum and time-domain extracted result of the ΔLOD signal
3.3 大气与日长年际变化相关性分析

 图 6 原始ΔLOD序列、AAM序列和ΔLOD-AAM序列在年际频段(1 a＜周期＜10 a)的频谱对比 Fig. 6 The frequency spectrum comparison of the original ΔLOD series, AAM series and (ΔLOD-AAM) series in the interannual frequency band range from 1 a to 10 a

 图 7 大气信号的标准Morlet小波振幅谱以及大气和日长对应信号时域提取结果的比较 Fig. 7 The normal Morlet wavelet amplitude spectrum of atmospheric signal and comparison of extracted result of atmosphere and ΔLOD signal

4 结语

 [1] Gross R S, Fukumori I, Menemenlis D, et al. Atmospheric and Oceanic Excitation of Length-of-Day Variations during 1980-2000[J]. Journal of Geophysical Research:Solid Earth, 2004, 109(B1) (0) [2] 师思, 周永宏, 许雪晴. 1979 － 2016年间日长变化在年际、季节性、亚季节性及高频尺度上的大气激发[J]. 天文学进展, 2017, 35(4): 448-461 (Shi Si, Zhou Yonghong, Xu Xueqing. Atmospheric Excitation of the Variation of Length of Day on Interannual, Seasonal, Sub-Seasonal and High-Frequency Timescales, 1979 － 2016[J]. Progress in Astronomy, 2017, 35(4): 448-461) (0) [3] Mound J E, Buffett B A. Interannual Oscillations in Length of Day: Implications for the Structure of the Mantle and Core[J]. Journal of Geophysical Research:Solid Earth, 2003, 108(B7) (0) [4] Ding H. Attenuation and Excitation of the ~6 Year Oscillation in the Length-of-Day Variation[J]. Earth and Planetary Science Letters, 2019, 507: 131-139 DOI:10.1016/j.epsl.2018.12.003 (0) [5] 杨丽娟, 张白桦, 叶旭桢. 快速傅里叶变换FFT及其应用[J]. 光电工程, 2004, 31(增): 1-3 (Yang Lijuan, Zhang Baihua, Ye Xuzhen. Fast Fourier Transform and Its Applications[J]. Opto-Electronic Engineering, 2004, 31(S): 1-3) (0) [6] 魏海涛, 郑南宁, 张志华. 具有紧支撑的正交小波变换滤波器的设计原理和一种图像精确分解与重构算法[J]. 电子学报, 2000, 28(10): 17-19 (Wei Haitao, Zheng Nanning, Zhang Zhihua. Orthogonal Wavelet Transform with Compact Support Filter Design Method and an Algorithm of Image Perfect Decomposition and Reconstruction[J]. Acta Electronica Sinica, 2000, 28(10): 17-19) (0) [7] Liu L T, Hsu H, Grafarend E W. Normal Morlet Wavelet Transform and Its Application to the Earth's Polar Motion[J]. Journal of Geophysical Research:Solid Earth, 2007, 112(B8) (0) [8] Cai S, Liu L T, Wang G C. Short-Term Tidal Level Prediction Using Normal Time-Frequency Transform[J]. Ocean Engineering, 2018, 156: 489-499 DOI:10.1016/j.oceaneng.2018.03.021 (0) [9] Su X Q, Liu L T, Hsu H, et al. Long-Term Polar Motion Prediction Using Normal Time-Frequency Transform[J]. Journal of Geodesy, 2014, 88(2): 145-155 (0) [10] Duan P S, Liu G Y, Liu L T, et al. Recovery of the 6-Year Signal in Length of Day and Its Long-Term Decreasing Trend[J]. Earth, Planets and Space, 2015, 67: 161 DOI:10.1186/s40623-015-0328-6 (0) [11] Li S D, Liu L T, Cai S, et al. Tidal Harmonic Analysis and Prediction with Least-Squares Estimation and Inaction Method[J]. Estuarine, Coastal and Shelf Science, 2019, 220: 196-208 DOI:10.1016/j.ecss.2019.02.047 (0) [12] Chao B F, Yan H M. Relation between Length-of-Day Variation and Angular Momentum of Geophysical Fluids[J]. Journal of Geophysical Research:Solid Earth, 2010, 115(B10) (0) [13] 虞南华, 郑大伟. 大气角动量资料源的变化对研究日长变化激发的贡献[J]. 天文学报, 1998, 39(2): 122-130 (Yu Nanhua, Zheng Dawei. Contribution of AAM Data Source Change to the Research on Excitation of ΔLOD[J]. Acta Astronomica Sinica, 1998, 39(2): 122-130) (0)
Extraction of Interannual Signals in the Length-of-Day Variation and Correlation Analysis with the Atmosphere
ZHANG Xinfeng1,2     LIU Genyou1
1. State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, CAS, 340 Xudong Street, Wuhan 430077, China;
2. University of Chinese Academy of Sciences, A19 Yuquan Road, Beijing 100049, China
Abstract: In order to conduct a more detailed analysis of the length-of-day (LOD) interannual variation, we use the normal Morlet wavelet transform method to identify and extract six main interannual signals from the LOD time series, which are 2.3 a (2.4 a), 3.3 a, 3.7 a, 4.8 a, 6.1 a and 8.1 a signals, respectively. Then, based on the time-domain extracted result, we acquire corresponding average amplitudes, which are 0.08 ms, 0.05 ms, 0.05 ms, 0.07 ms, 0.10 ms and 0.07 ms, respectively. Refer to the method of extracting interannual signals of the LOD, we extract the corresponding signals in the atmospheric angular momentum series, and perform correlation analysis between them. The results show that the atmosphere is closely related to four high frequency interannual signals (2.3 a (2.4 a), 3.3 a, 3.7 a and 4.8 a) corresponding to the LOD, and the correlation coefficients are 0.99, 0.93, 0.99, 0.91, respectively. The contribution rates of the atmosphere to the 2.3 a (2.4 a), 3.3 a, 3.7 a and 4.8 a signals of LOD are about 99.7%, 63.1%, 94.7% and 69.3%, which indicates that 2.3 a (2.4 a) and 3.7 a signals of the LOD can be almost completely explained by atmosphere, and the other two signals are also mainly affected by atmosphere. The 6.0 a and 8.5 a atmospheric signals are irrelevant or weakly related with the 6.1 a and 8.1 a signals corresponding to the LOD, and the correlation coefficients are -0.11 and -0.56, respectively.
Key words: length-of-day variation; normal Morlet wavelet transform; interannual signals; average amplitude; characteristic analysis