A New Medium- and Long-Term UT1–UTC Prediction Method Incorporating Effective Earth Fluid Angular Momentum

YU Kehao; WANG Xiaoya; LI Lihua; LI Zhao; JIANG Weiping; YANG Kai; LONG Jingwen

doi:10.13203/j.whugis20240263

您当前的位置：

首页 >

文章列表页 >

A New Medium- and Long-Term UT1–UTC Prediction Method Incorporating Effective Earth Fluid Angular Momentum

更新时间：2026-11-05

- A New Medium- and Long-Term UT1–UTC Prediction Method Incorporating Effective Earth Fluid Angular Momentum
- Geomatics and Information Science of Wuhan University Vol. 51, Issue 1, Pages: 191-198(2026)
- 作者机构：
  
  1.武汉大学卫星导航定位技术研究中心，湖北武汉，430079
  2.湖北珞珈实验室，湖北武汉，430079
  3.武汉大学测绘学院，湖北武汉，430079
  4.中国科学院上海天文台，上海，200030
  5.中国科学院大学天文与空间科学学院，北京，100049
  6.中国地质大学（北京）土地科学技术学院，北京，100083
- 作者简介：
- 基金信息：
- DOI：10.13203/j.whugis20240263
  CLC： P228
- Received：27 November 2024，
  
  Published：05 January 2026
- 稿件说明：
移动端阅览
俞克豪,王小亚,李丽华，等.顾及地球流体有效角动量信息的UT1-UTC中长期预报新方法[J].武汉大学学报（信息科学版）,2026,51(1):191-198.

YU Kehao,WANG Xiaoya,LI Lihua,et al.A New Medium- and Long-Term UT1–UTC Prediction Method Incorporating Effective Earth Fluid Angular Momentum[J].Geomatics and Information Science of Wuhan University,2026,51(1):191-198.
俞克豪,王小亚,李丽华，等.顾及地球流体有效角动量信息的UT1-UTC中长期预报新方法[J].武汉大学学报（信息科学版）,2026,51(1):191-198. DOI：10.13203/j.whugis20240263

YU Kehao,WANG Xiaoya,LI Lihua,et al.A New Medium- and Long-Term UT1–UTC Prediction Method Incorporating Effective Earth Fluid Angular Momentum[J].Geomatics and Information Science of Wuhan University,2026,51(1):191-198. DOI：10.13203/j.whugis20240263

摘要

世界时与协调世界时差值（difference between universal time and coordinated universal time

UT1-UTC）是地球定向参数（Earth orientation parameters

EOP）的重要组成部分，其高精度和快速预测对全球卫星导航系统气象学、人造卫星精密轨道确定等实时应用领域至关重要。传统UT1-UTC预报方法在中长期预测中精度衰减明显，难以满足北斗导航系统及战争环境的精确制导等高精度需求。提出了一种融合地球流体有效角动量（effective angular momentum

EAM）信息的轴向分量

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994267&type=

3.89466691

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994298&type=

2.70933342

数据与EOP14 C04序列的卷积长短期记忆神经网络（convolutional long short-term memory

ConvLSTM）模型预报UT1-UTC的新方法。实测数据分析结果发现，EAM轴向分量

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994299&type=

3.89466691

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994289&type=

2.70933342

和经跳秒与潮汐改正后的UT1-UTC数据具有强相关性，其振幅和相位具有一致的频谱特性，说明EAM轴向分量

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994299&type=

3.89466691

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994289&type=

2.70933342

是UT1-UTC的主要激发源。与参与第二届EOP预报比赛的各家精度进行对比，在90~360 d的中长期预报跨度中，ConvLSTM模型预报精度最优，改善幅度为30.27%~92.44%。对比公报 A，时间跨度为60 d、180 d和360 d的中长期预报精度分别提升41.46%、70.07%和59.43%，证实了Conv

LSTM能够显著改善UT1-UTC的中长期预报精度。

Abstract

Objectives

The difference between universal time and coordinated universal time (UT1-UTC) is a critical component of the Earth orientation parameters (EOP). Accurate and rapid prediction of UT1-UTC is essential for real-time applications such as GNSS meteorology and precise orbit determination of artificial satellites. Traditional methods for predicting UT1-UTC often experience significant accuracy degradation in medium- and long-term forecasts

making them inadequate for high-precision requirements in applications like the BeiDou navigation satellite system and precision guidance in military operations.

Methods

We propose a novel method for predicting UT1-UTC by integrating the axial component data

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994315&type=

4.57200003

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994290&type=

3.04800010

of effective angular momentum (EAM) with the EOP14 C04 series using a convolutional long short-term memory (ConvLSTM) model. Initially

leap seconds and solid Earth tide terms are removed from the original UT1-UTC series to obtain the difference between the tidally reduced universal time and international atomic time (UT1R-TAI) data. Spectral analysis is then performed on the axial component data

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994316&type=

4.57200003

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994291&type=

3.04800010

of EAM and UT1R-TAI data using the fast Fourier transform to investigate whether the axial component data

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994317&type=

4.57200003

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994292&type=

3.04800010

of EAM can comprehensively describe the excitation of UT1-UTC. Subsequently

a ConvLSTM model incorporating the axial component data

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994318&type=

4.57200003

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994293&type=

3.04800010

of EAM is constructed to predict the UT1-UTC time series. After prediction

the leap seconds and solid Earth tide terms are reintroduced

while the accuracy of predictions

is evaluated.

Results

Analysis of observations reveals a strong correlation between the axial component

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994317&type=

4.57200003

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994292&type=

3.04800010

of EAM and UT1-UTC data after applying leap-second and tidal corrections

with consistent amplitude and phase characteristics in their frequency spectra. This indicates that the axial component

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994319&type=

4.57200003

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994294&type=

3.04800010

of EAM serves as a primary excitation source for UT1-UTC. Compared to the prediction accuracy of participants in the second Earth orientation parameters prediction comparison campaign

the ConvLSTM model demonstrates superior performance in medium- to long-term predictions spanning 90 d to 360 d

with accuracy improvements ranging from 30.27% to 92.44%. Additionally

compared to Bulletin A

the ConvLSTM model achieves accuracy enhancements of 41.46%

70.07%

and 59.43% for prediction spans of 60 d

0 d

and 360 d

respectively.

Conclusions

The results confirm that the ConvLSTM model significantly improves the medium- to long-term prediction accuracy of UT1-UTC. These findings are crucial for autonomous determination of EOP and real-time applications

as well as for precise satellite orbit determination and other related fields.

关键词

Keywords

references

NILSSON T ， HEINKELMANN R ， KARBON M ， et al . Earth Orientation Parameters Estimated from VLBI During the CONT11 Campaign ［J］. Journal of Geodesy ， 2014 ， 88 （ 5 ）： 491 - 502 .

NIEDZIELSKI T ， KOSEK W . Prediction of UT1-UTC， LOD and AAM <math id="M63"><msub><mrow><mi>χ</mi></mrow><mrow><mn mathvariant="normal">3</mn></mrow></msub></math> https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994459&type= 3.89466691 https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=98994484&type= 2.70933342 by Combination of Least-Squares and Multivariate Stochastic Methods ［J］. Journal of Geodesy ， 2008 ， 82 （ 2 ）： 83 - 92 .

俞克豪，沈彤晖，李丽华，等 . 基于CONT观测资料解算地球自转参数精度分析［J］. 大地测量与地球动力学， 2023 ， 43 （ 9 ）： 986 - 990 .

YU Kehao ， SHEN Tonghui ， LI Lihua ， et al . Accuracy Analysis of Estimating Earth Rotation Parameters Based on CONT Campaigns ［J］. Journal of Geo‑desy and Geodynamics ， 2023 ， 43 （ 9 ）： 986 - 990 .

李斐，邵先远，曲春凯，等 . 利用2006—2015年VLBI数据进行地球定向参数解算与分析［J］. 武汉大学学报（信息科学版）， 2019 ， 44 （ 11 ）： 1581 - 1587 .

LI Fei ， SHAO Xianyuan ， QU Chunkai ， et al . Solution and Analysis of Earth Orientation Parameters with 2006—2015 VLBI Observation ［J］. Geomatics and Information Science of Wuhan University ， 2019 ， 44 （ 11 ）： 1581 - 1587 .

BIZOUARD C ， LAMBERT S ， GATTANO C ， et al . The IERS EOP 14C04 Solution for Earth Orientation Parameters Consistent with ITRF 2014 ［J］. Journal of Geodesy ， 2019 ， 93 （ 5 ）： 621 - 633 .

YU K H ， SHI H W ， SUN M Q ， et al . Combined BiLSTM and ARIMA Models in Middle- and Long-Term Polar Motion Prediction ［J］. Studia Geophysica et Geodaetica ， 2024 ， 68 （ 1 ）： 25 - 40 .

LEI Y ， ZHAO D N ， GUO M . Medium- and Long-Term Prediction of Length-of-Day Changes with the Combined Singular Spectrum Analysis and Neural Networks ［J］. Studia Geophysica et Geodaetica ， 2023 ， 67 （ 3 ）： 107 - 123 .

许雪晴，周永宏，胥灿灿 . 1972—2022年地球自转速率的变化特性及其中的气候事件表征研究［J］. 地球与行星物理论评（中英文）， 2023 ， 54 （ 5 ）： 572 - 580 .

XU Xueqing ， ZHOU Yonghong ， XU Cancan . Variation in the Rotation Rate of Earth and Its Role as a Climate Change Indicator Between 1972 and 2022 ［J］. Reviews of Geophysics and Planetary Physics ， 2023 ， 54 （ 5 ）： 572 - 580 .

王波，鄢建国，高梧桐，等 . EOP预报误差对深空探测器精密定轨结果影响分析［J］. 武汉大学学报（信息科学版）， 2024 ， 49 （ 9 ）： 1538 - 1545 .

WANG Bo ， YAN Jianguo ， GAO Wutong ， et al . Impact Analysis of EOP Prediction Errors on Orbit Determination of Deep-Space Spacecraft ［J］. Geomatics and Information Science of Wuhan University ， 2024 ， 49 （ 9 ）： 1538 - 1545 .

赵丹宁，高蕊，雷雨 . 利用小波分解改进极移预报模型［J］. 武汉大学学报（信息科学版）， 2019 ， 44 （ 12 ）： 1797 - 1801 .

ZHAO Danning ， GAO Rui ， LEI Yu . Improvement of the Polar Motion Prediction Model Using Wavelet Decomposition ［J］. Geomatics and Information Scien‑ce of Wuhan University ， 2019 ， 44 （ 12 ）： 1797 - 1801 .

雷雨，赵丹宁，蔡宏兵 . 利用端部效应改善的最小二乘外推模型进行UT1-UTC预报［J］. 天文研究与技术， 2018 ， 15 （ 3 ）： 302 - 307 .

LEI Yu ， ZHAO Danning ， CAI Hongbing . A Least Squares Extrapolation Model for UT1-UTC Prediction Method with Consideration of the Edge-Effect ［J］. Astronomical Research & Technology ， 2018 ， 15 （ 3 ）： 302 - 307 .

HU C ， WANG Q X ， WANG Z W ， et al . A Method for Improving the Short-Term Prediction Model for ERP Based on Long-Term Observations ［M］// China Satellite Navigation Conference ， Singapore ， 2019 .

SCHUH H ， ULRICH M ， EGGER D ， et al . Prediction of Earth Orientation Parameters by Artificial Neural Networks ［J］. Journal of Geodesy ， 2002 ， 76 （ 5 ）： 247 - 258 .

JIA S ， XU T H ， SUN Z Z ， et al . Middle and Long-Term Prediction of UT1-UTC Based on Combination of Gray Model and Autoregressive Integrated Moving Average ［J］. Advances in Space Research ， 2017 ， 59 （ 3 ）： 888 - 894 .

KOOT L ， DE VIRON O ， DEHANT V . Atmospheric Angular Momentum Time-Series： Characterization of Their Internal Noise and Creation of a Combined Series ［J］. Journal of Geodesy ， 2006 ， 79 （ 12 ）： 663 - 674 .

ZHAO X ， WU Y W ， YANG X Y ， et al . Multi-Parameter Polar Motion Prediction Based on Effective Angular Momentum Function ［J］. Chinese Astronomy and Astrophysics ， 2022 ， 46 （ 4 ）： 486 - 499 .

DILL R ， DOBSLAW H ， THOMAS M . Improved 90-Day Earth Orientation Predictions from Angular Momentum Forecasts of Atmosphere， Ocean， and Terrestrial Hydrosphere ［J］. Journal of Geodesy ， 2019 ， 93 （ 3 ）： 287 - 295 .

杨新宇，吴元伟，赵鑫，等 . 基于地球流体有效角动量函数的UT1分段预报［J］. 时间频率学报， 2023 ， 46 （ 2 ）： 94 - 104 .

YANG Xinyu ， WU Yuanwei ， ZHAO Xin ， et al . Piecewise UT1 Prediction Based on the Earth’s Fluid Effective Angular Momentum Function ［J］. Journal of Time and Frequency ， 2023 ， 46 （ 2 ）： 94 - 104 .

YU K H ， YANG K ， SHEN T H ， et al . Estimation of Earth Rotation Parameters and Prediction of Polar Motion Using Hybrid CNN–LSTM Model ［J］. Remote Sensing ， 2023 ， 15 （ 2 ）： 427 .

PETIT G ， LUZUM B . The 2010 Reference Edition of the IERS Conventions ［M］// Altamimi Z， Collilieux X. Reference Frames for Applications in Geosciences . Berlin， Heidelberg ： Springer ， 2013 .

ŚLIWIŃSKA-BRONOWICZ J ， KUR T ， WIŃSKA M ， et al . Assessment of Length-of-Day and Universal Time Predictions Based on the Results of the Second Earth Orientation Parameters Prediction Comparison Campaign ［J］. Journal of Geodesy ， 2024 ， 98 （ 3 ）： 22 .

GOU J Y ， KIANI SHAHVANDI M ， HOHENSINN R ， et al . Ultra-Short-Term Prediction of LOD Using LSTM Neural Networks ［J］. Journal of Geodesy ， 2023 ， 97 （ 5 ）： 52 .

XU X Q ， ZHOU Y H ， LIAO X H . Short-Term Earth Orientation Parameters Predictions by Combination of the Least-Squares， AR Model and Kalman Filter ［J］. Journal of Geodynamics ， 2012 ， 62 ： 83 - 86 .

MODIRI S ， BELDA S ， HEINKELMANN R ， et al . Polar Motion Prediction Using the Combination of SSA and Copula-Based Analysis ［J］. Earth， Planets and Space ， 2018 ， 70 （ 1 ）： 115 .

SU X Q ， LIU L T ， HOUTSE H ， et al . Long-Term Polar Motion Prediction Using Normal Time-Frequency Transform ［J］. Journal of Geodesy ， 2014 ， 88 （ 2 ）： 145 - 155 .

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Prediction of Length of Day Using Singular Spectrum Analysis and Autoregressive Integrated Moving Average Model

A New Data Preprocessing Method for BeiDou Satellite Clock Bias

Development and Prospects of Spaceborne Greenhouse Gas LiDAR Technology

Spatiotemporal Evolution and Explainable Analysis of Grassland Vulnerability in the Hohhot-Baotou-Ordos-Yulin Urban Agglomeration

Geometric and Frequency-Domain Constrained Single-Step Diffusion for Seabed Terrain Super-Resolution Reconstruction

Related Author

LEI Yu

ZHAO Danning

CAI Hongbing

XU Jinsong

WANG Xu

CHAI Hongzhou

CHONG Yang

LI Jinsheng

Related Institution

School of Computer Science and Technology, Xi'an University of Posts and Telecommunications

Shaanxi Key Laboratory Network Data Intelligent Processing, Xi'an University of Posts and Telecommunications

School of Electrical and Electronic Engineering, Baoji University of Arts and Sciences

JSNU SPBPU Institute of Engineering, Jiangsu Normal University

School of Resources and Civil Engineering, Liaoning Institute of Science and Technology

AI问答

⁰