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