Jia Liu

Given that effective angular momentum (EAM) data demonstrate a strong correlation with length of day (LOD) data and are extensively utilized in the prediction of the universal time (UT1), this research integrated the EAM into the design of a Kalman filter. At the solution combination level, the UT1, LOD, and EAM were merged to derive a UT1/LOD sequence featuring higher accuracy and enhanced continuity. To begin with, a comprehensive evaluation of the three datasets was conducted to identify the systematic biases and periodic components of the LOD. Subsequently, geodetic angular momentum (GAM) data were employed to rectify the EAM data spanning from 2019 to 2022. Finally, the corrected EAM was combined with the UT1 and LOD through Kalman modeling. To evaluate the capability of this EAM-aided Kalman filter, Jet Propulsion Laboratory (JPL) and Wuhan University (WHU) LOD data, International Very Long Baseline Interferometry (VLBI) Service for Geodesy and Astrometry (IVS) intensive and National Time Service Center (NTSC) UT1 data, and German Research Centre for Geosciences (GFZ) EAM data were used for combination experiments. The final estimations of the UT1 and LOD were compared with the International Earth Rotation Service (IERS) Earth-orientation parameter (EOP) 20 C04 series. From July to September 2021, the root mean square (RMS) of the combined UT1 series was reduced from 38 µs to 26 µs for the IVS intensive UT1, with an improvement of 30%. The RMS of the combined UT1 series was reduced from 102 µs to 47 µs for the NTSC UT1 measurement, with an improvement of 54%. The bias of the LOD was effectively corrected and the RMS of the LOD improved by 60–70% and the standard deviation of the LOD improved by 11–30%. Further, the final estimated uncertainties of the UT1 and LOD are, in general, consistent with the estimated RMS, indicating a reasonable estimation of uncertainties. Comparative experiments with and without the EAM show that using EAM data can effectively reduce the extreme values, especially for the NTSC UT1 series with large uncertainties. In summary, this EAM-aided Kalman filter can produce UT1 and LOD series with improved accuracy, and with reasonable uncertainties.

The components of EAM are strongly correlated with LOD and play an important role in UT1-UTC and LOD prediction. However, the EAM dataset is prone to be noisy. In this study, we propose a hybrid method to reduce the noise of the EAM data and improve the accuracy of UT1-UTC and LOD predictions. We use the EOP data to denoise the EAM data, and use Kalman filtering to denoise the 1–6 days forecast of EAM. Then, we use the denoised EAM dataset to improve the UT1-UTC and LOD prediction. The denoised EAM dataset improved the prediction of UT1-UTC within 10 days by 20%. In addition, we found that by introducing two additional periodic (23.9 days and 91.3 days) components for the least-squares fitting, the accuracy of UT1-UTC and LOD prediction in the range of 30–80 days is significantly improved. In more than 430 UT1-UTC and LOD prediction experiments conducted during 2021–2022, the improvements in the 1–6 days forecast were significant. For the 6th day, 30th day, and 60th day, the MAE of UT1-UTC was 0.1592, 2.9169, and 6.7857 ms, respectively, corresponding to improvements of 31.35, 12.60, and 12.93%, respectively, when compared to predictions of Bulletin A. The MAE of LOD predictions on the 1st day, 6th day, 30th day, and 90th day was 0.0255, 0.0432, 0.1694, and 0.2505 ms, respectively, which improved by 26.09, 14.29, 6.36, and 3.76% when compared with our second EOPPCC method.