SUMMARY: High-precision prediction of polar motion (PM) plays an important role in fields such as astronomy, geodesy, navigation and aerospace. Incorporating information on the effective angular momentum (EAM) of the geophysical fluid is an effective way to improve the precision of PM prediction. Based on the EOP_20_C04 data set and the EAM function, this study applies complex segmented least-squares (CSLS) + the long short term memory (LSTM) neural network and CSLS + autoregression (AR) models to predict PM. For the 6-d PM prediction, the mean absolute errors (MAEs) achieved by CSLS+AR are 1.03 and 0.8 mas in the X- and Y-directions, respectively, corresponding to reductions of 45.80 and 31.97 per cent when compared to predictions reported routinely in Bulletin A of the International Earth Rotation and Reference Systems Service (IERS). For the 365-d PM prediction, the MAEs gained by CSLS+LSTM model are 14.58 and 10.59 mas in the X- and Y-directions, respectively, corresponding to reductions of 28.17 and 51.09 per cent compared to predictions of the Bulletin A, and the prediction accuracy attained by CSLS+LSTM is higher than other prediction schemes. The experimental results show that, when considering EAM information, the CSLS+AR model can achieve better prediction accuracy for short-term PM prediction, however, the CSLS+LSTM model is more effective for medium- and long-term PM prediction owning to the excellent nonlinear fitting capability of the LSTM deep learning algorithm.

Polar motion (PM) has a close relation to the Earth’s structure and composition, seasonal changes of the atmosphere and oceans, storage of waters, etc. As one of the four major space geodetic techniques, doppler orbitography and radiopositioning integrated by satellite (DORIS) is a mature technique that can monitor PM through precise ground station positioning. There are few articles that have analyzed the PM series derived by the DORIS solution in detail. The aim of this research was to assess the PM time-series based on the DORIS solution, to better capture the time-series. In this paper, Fourier fast transform (FFT) and singular spectrum analysis (SSA) were applied to analyze the 25 years of PM time-series solved by DORIS observation from January 1993 to January 2018, then accurately separate the trend terms and periodic signals, and finally precisely reconstruct the main components. To evaluate the PM time-series derived from DORIS, they were compared with those obtained from EOP 14 C04 (IAU2000). The results showed that the RMSs of the differences in PM between them were 1.594 mas and 1.465 mas in the X and Y directions, respectively. Spectrum analysis using FFT showed that the period of annual wobble was 0.998 years and that of the Chandler wobble was 1.181 years. During the SSA process, after singular value decomposition (SVD), the time-series was reconstructed using the eigenvalues and corresponding eigenvectors, and the results indicated that the trend term, annual wobble, and Chandler wobble components were accurately decomposed and reconstructed, and the component reconstruction results had a precision of 3.858 and 2.387 mas in the X and Y directions, respectively. In addition, the tests also gave reasonable explanations of the phenomena of peaks of differences between the PM parameters derived from DORIS and EOP 14 C04, trend terms, the Chandler wobble, and other signals detected by the SSA and FFT. This research will help the assessment and explanation of PM time-series and will offer a good method for the prediction of pole shifts.

Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) has become a matured and accurate space geodetic technique after more than two decades of development. DORIS’s main objective is precise orbit determination (POD) for the low earth orbit (LEO) satellite. For the POD of Haiyang-2A (HY-2A) satellite, a DORIS receiver was equipped on this mission. There are several nonconservative forces acting on a Haiyang-2A (HY-2A) satellite with an altitude of about 970 km. Out of the factors, the atmospheric drag is the most dominant and uncertain one during the process of POD with the dynamic method. The comparison between the computed orbits of HY-2A using DORIS range-rate data and the precise orbits computed by the Centre National d’Etudes Spatiales (CNES) has been performed, and the DORIS range-rate residuals have been analyzed in detail in normal period and frequent solar activity period. Tests demonstrated that the relative optimal atmospheric density model was the empirical MSIS-86 model for the HY-2A satellite. The root mean squares (RMSs) of orbit difference between the derived orbits using the MSIS-86 model and the CNES orbits were 0.0173 m in the radial direction and 0.0619 m in three dimensions. The RMS of the range-rate residual improved 0.024 mm/s during the frequent solar activity period with the atmospheric drag coefficient solved every three hours and then every six hours using the MSIS-86 model. This study will provide valuable references for POD and orbit prediction for the LEO satellites, especially for the other HY-2 series satellites of China.

The Inner Mongolia Autonomous Region is an ecological protective screen in the northern part of China. The precipitable water vapor (PWV) products with high precision and spatiotemporal resolution are critical for climate research in this region. This study aims to comprehensively evaluate and analyze the PWV in Inner Mongolia using the global navigation satellite system (GNSS), radiosonde (RS), the fifth-generation European Center for Medium-Range Weather Forecasts Reanalysis (ERA5), and the Second Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) data. The comparison between GNSS PWV and RS PWV reveals an average bias of −0.68 mm and a root mean square error (RMSE) of 2.17 mm, indicating the high accuracy of GNSS PWV and its potential as an assessment tool of other PWV products. In comparison to PWV of GNSS, PWV of ERA5 and MERRA-2 exhibit an annual average bias of 0.17 and 0.39 mm, respectively, and an annual average RMSE of 1.63 and 2.99 mm, respectively. The monthly average bias and RMSE of ERA5 are 0.41 and 1.79 mm, respectively, while those of MERRA-2 are 0.43 and 3.05 mm, respectively. In the Inner Mongolia region, the diurnal anomaly variation range of PWV for all three datasets is stable within −0.6–0.6 mm. The evaluation results demonstrate that PWV retrieved by ERA5 and MERRA-2 are applicable in the Inner Mongolia region, with ERA5 showing better consistency with GNSS PWV. This study provides valuable insights for understanding the formation, patterns, monitoring, and warning of meteorological natural disasters in the Inner Mongolia region. Graphical Abstract