- 1Henan University, College of Geographical Sciences, Faculty of Geographical Science and Engineering, China (wangchenxiang@whu.edu.cn)
- 2Henan Industrial Technology Academy of Spatiotemporal Big Data (Henan University), Zhengzhou, 450046, China
- 3School of Geodesy and Geomatics, Wuhan University, 129 Luoyu Road, Wuhan 430079, China
Abstract: The Earth is subject to complex internal and external forces in space. External forces include the gravitational attraction from the sun, moon, and other planets, and the internal forces include mass loads and frictional forces from the atmosphere, oceans, ice, snow, water, as well as interactions between the crust and mantle. Earth rotation parameters (ERPs) are essential for transforming between the celestial and terrestrial reference frames, and for high-precision space navigation and positioning. Amongst the ERPs, Polar Motion (PM) is a critical parameter for analyzing and understanding the dynamic interaction between the solid Earth, atmosphere, ocean, and other geophysical fluids. To investigate the impact of effective angular momentum (EAM) on long-term ERPs prediction, this thesis conducts research on long-term ERPs prediction considering EAM. Taking into account the influence of EAM, a discrete Liouville equation related to polar motion and UT1-UTC was first established, and the corresponding geodetic angular momentum was obtained. Finally, the residual geodetic angular momentum was obtained and modeled. Taking into account the residual of geodetic angular momentum and the experimental results of EAM, it is shown that, compared with Bulletin A, the LS+LSTM model has improved the accuracy of PMX, PMY, and UT1-UTC in the mid-and long term.
Keywords: Earth rotation parameters, Polar Motion, UT1-UTC, Least squares, Long Short-Term Memory model, effective angular momentum
Funding: Part of this work is supported by the National Natural Science Foundation of China (NSFC) (Grant No.42174037, No. 42030105, No. 42204006, No. 42274011, No. 42304095) and the State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping Science and Geospatial Information Technology of MNR, CASM (Grant No. 2024-01-01), the China Postdoctoral Science Foundation under Grant No.2024M752480.
How to cite: Wang, C., Wang, J., Zhang, C., Zhang, P., and Sang, J.: High-precision Earth Rotation Parameters Prediction with Physical Excitation Factors and Deep Learning Methods, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4895, https://doi.org/10.5194/egusphere-egu25-4895, 2025.
