利用小波分解改进极移预报模型

为进一步提高极移预报精度，将小波分解引入极移预报中。首先利用小波分解对极移序列进行分解，分离低频分量与高频分量，然后对低频分量建立最小二乘外推模型，获得极移序列的趋势项外推值与残差序列，最后采用自回归（autoregressive，AR）模型对高频分量与残差序列之和进行预报，最终极移的预报值为最小二乘外推值与AR模型预报值之和。结果表明，小波分解可以明显改善最小二乘外推与AR组合模型的极移预报精度，尤其对于中长期预报改善更为明显。     

基于1962至2022年极移观测值的时序分析及长期预报研究

极移参数（polar motion,PM）是地球定向参数（earth orientation parameter,EOP）的重要组成部分,在天文大地测量、卫星的导航定位以及卫星的自主定轨等领域有重要的应用。本文首先利用国际地球自转服务（international earth rotation and reference systems service,IERS）提供的极移观测值进行极移时间序列频谱分析及周期项的提取。结果表明,快速傅里叶变换与离散傅里叶变换周期项提取到的周期项基本无差异,但极移X、Y分量时变分析结果存在差异。此外,本文将正弦逼近应用到极移参数的预报,对于长时间尺度的极移参数预报最小二乘外推模型效果最佳,但预报模型精度易受到周期项提取结果的影响;最小二乘外推模型在极移正向预报和反推拟合效果相近,借助正弦逼近方法可以一定程度上增强模型拟合效果。上述研究结果可为地球自转参数的预报研究提供参考。     

基于LS+AR模型的极移预报及其周期项影响分析

采用LS+AR预报模型的对极移进行预报,深入分析极移数据中钱德勒项和周年项对预报精度的影响。计算结果显示,极移数据中各周期项具有时变性特点,将已知观测数据确定出的周期项应用到极移预报中会明显提高预报精度。因此,建议在进行极移预报之前首先应进行时间序列分析,确定已有数据的各周期项,在极移预报中加以考虑。     

对分量进行联合解算的极移预报方法研究

为了提高极移预报的精度,该文提出结合极移的两个分量进行统一求解的极移联合预报方法:将极移的两个分量组成一个观测方程,并采用LS+AR模型对联合分量的确定项与随机项进行拟合。实验结果表明,文中采用的联合预报方法可以提高极移的预报精度。     

极移时间序列建模及预测

基于自回归滑动平均(auto-regressive and moving average,ARMA)模型以及一阶差分ARMA模型分别对极移的X分量和Y分量进行了模型拟合,并利用所拟合的模型对极移进行了预测.通过与国际地球自转服务发布的实测极移数据以及其他方法对比,证明了所建立的拟合模型在短期预报上的有效性.且X分量的ARMA模型在39 d预报跨度内的整体精度优于一阶差分ARMA模型,而Y分量45 d内的一阶差分ARMA模型预报精度比ARMA模型预报精度更高.     

一种导航卫星中长期轨道预报方法

基于神经网络混合建模的思想提出一种针对导航卫星的中长期轨道预报方法,在原动力学模型的基础上引入神经网络模型作为补偿,从而获得新的预报模型。在训练过程中神经网络通过学习动力学模型轨道预报误差来掌握其变化规律,并在预报过程中为动力学模型预报提供补偿,以提高预报精度。对GPS卫星动力学模型中长期预报误差的特点进行分析,然后根据所得结论提出混合模型的中长期(15 d以上)预报方案,最后通过对GPS卫星的仿真试验证明混合模型的改进效果,结果表明新方法在15~40 d的预报上表现出很好的改进效果。     

一种新的钟差预处理方法及在WNN钟差中长期预报中的应用

为了提高导航卫星钟差中长期预报的精度,在提出一种针对钟差一次差分序列的数据预处理方法的基础上,建立了一种钟差中长期预报的小波神经网络模型。该模型首先对建模钟差数据进行一次差分,然后对一次差分序列进行预处理;用预处理后的一次差分序列对小波神经网络建模并进行中长期预报,最后将预报结果还原得到相应的钟差预报值。使用全球定位系统(GPS)卫星的铷钟数据进行中长期预报,并与常用的二次多项式模型、灰色模型、Kalman滤波模型进行对比,结果表明,本文方法能有效减小导航卫星星载铷钟钟差的中长期预报误差。     

对钱德勒参数进行时变修正的CLS+AR模型在极移预测中的应用

根据LS模型参数中钱德勒项的时变性质,提出了基于钱德勒参数时变修正的CLS模型,并用CLS+AR对极移进行预测。实验结果表明,CLS+AR模型在极移预测精度上较LS+AR模型有较大改善。     

基于差分序列分析极移不规则变化及对模型拟合的影响

通过对近52年的极移数据进行二阶差分,发现极移不规则变化的活跃程度存在明显的分段特征。其中活跃部分大约持续6 100d,与地震的近6 500d的长活动周期以及章动的18.6a周期较接近;然后以ARMA模型为例,选取状况不同的4个不规则变化时段的极移数据进行模型的拟合与预报,并进行对比。结果表明:1若拟合数据处于活跃期,建模及预报的精度相对于处于平静期时较差;2拟合数据同时包含活跃期与平静期,拟合的残差出现病态分布,模型建立不成功;3拟合精度相当的不同模型,预测数据处于平静期要比处于活跃期时精度要好。     

灰色系统理论的优化方法及其在卫星钟差短期预报中的应用

通过利用灰色系统理论对卫星钟差进行预报研究,论述了灰色GM(1,1)模型的建立、计算及原点精度检验方法,并结合实例给出一种模型优化的途径,即参数优化法。计算结果表明,参数优化法可以提高卫星钟差短期预报的精度。     

Influence of subdaily polar motion model on nutation offsets estimated by very long baseline interferometry

This paper studies the connection between the subdaily model for polar motion used in the processing of very long baseline interferometry (VLBI) observations and the estimated nutation offsets. By convention accepted by the International Earth Rotation Service, the subdaily model for polar motion recommended for routine processing of geodetic observations does not contain any daily retrograde terms due to their one-to-one correlation with the nutation. Nevertheless, for a 24-h VLBI solution a part of the signal contained in the polar motion given by the used subdaily model is numerically mistaken for a retrograde daily sidereal signal. This fictitious retrograde daily signal contributes to the estimated nutation, leading to systematic differences between the nutation offsets from VLBI solutions computed with different subdaily polar motion models. We demonstrate this effect using solutions for all suitable 24-h VLBI sessions over a time span of 11 years (2000–2011). By changing the amplitudes of one tidal term in the underlying subdaily model for polar motion and comparing the estimated parameters to the solutions computed with the unchanged subdaily model, the paper shows and explains theoretically the effects produced by the individual subdaily terms on the VLBI nutation estimates.     

Possible improvement of Earth orientation forecast using autocovariance prediction procedures

 Autocovariance prediction has been applied to attempt to improve polar motion and UT1-UTC predictions. The predicted polar motion is the sum of the least-squares extrapolation model based on the Chandler circle, annual and semiannual ellipses, and a bias fit to the past 3 years of observations and the autocovariance prediction of these extrapolation residuals computed after subtraction of this model from pole coordinate data. This prediction method has been applied also to the UT1-UTC data, from which all known predictable effects were removed, but the prediction error has not been reduced with respect to the error of the current prediction model. However, the results show the possibility of decreasing polar motion prediction errors by about 50 for different prediction lengths from 50 to 200 days with respect to the errors of the current prediction model. Because of irregular variations in polar motion and UT1-UTC, the accuracy of the autocovariance prediction does depend on the epoch of the prediction. To explain irregular variations in x, y pole coordinate data, time-variable spectra of the equatorial components of the effective atmospheric angular momentum, determined by the National Center for Environmental Prediction, were computed. These time-variable spectra maxima for oscillations with periods of 100–140 days, which occurred in 1985, 1988, and 1990 could be responsible for excitation of the irregular short-period variations in pole coordinate data. Additionally, time-variable coherence between geodetic and atmospheric excitation function was computed, and the coherence maxima coincide also with the greatest irregular variations in polar motion extrapolation residuals. Received: 22 October 1996 / Accepted: 16 September 1997     

Modeling and forecast of the polar motion excitation functions for short-term polar motion prediction

Short-term forecast of the polar motion is considered by introducing a prediction model for the excitation function that drives the polar motion dynamics. The excitation function model consists of a slowly varying trend, periodic modes with annual and several sub-annual frequencies (down to the 13.6-day fortnightly tidal period), and a transient decay function with a time constant of 1.5 days. Each periodic mode is stochastically specified using a second-order auto-regression process, allowing its frequency, phase, and amplitude to vary in time within a statistical tolerance. The model is used to time-extrapolate the excitation function series, which is then used to generate a polar motion forecast dynamically. The skills of this forecast method are evaluated by comparison to the C-04 polar motion series. Over the lead-time horizon of four months, the proposed method has performed equally well to some of the state-of-art polar motion prediction methods, none of which specifically features forecasting of the excitation function. The annual mode in the ₂ component is energetically the most dominant periodicity. The modes with longer periods, annual and semi-annual in particular, are found to contribute more significantly to forecast accuracy than those with shorter periods.     

Long-term polar motion prediction using normal time–frequency transform

This paper presents normal time–frequency transform (NTFT) application in harmonic/quasi-harmonic signal prediction. Particularly, we use the normal wavelet transform (a special NTFT) to make long-term polar motion prediction. Instantaneous frequency, phase and amplitude of Chandler wobble, prograde and retrograde annual wobbles of Earth’s polar motion are analyzed via the NTFT. Results show that the three main wobbles can be treated as quasi-harmonic processes. Current instantaneous harmonic information of the three wobbles can be acquired by the NTFT that has a kernel function constructed with a normal half-window function. Based on this information, we make the polar motion predictions with lead times of 1 year and 5 years. Results show that our prediction skills are very good with long lead time. An abnormality in the predictions occurs during the second half of 2005 and first half of 2006. Finally, we provide the future (starting from 2013) polar motion predictions with 1- and 5-year leads. These predictions will be used to verify the effectiveness of the method proposed in this paper.     

Polar motion modeling,analysis, and prediction with time dependent harmonic coefficients

A time dependent amplitude model was proposed for the analysis and prediction of polar motion time series. The formulation was implemented to analyze part of the new combined solution, EOP (IERS) C 04, daily polar motion time series of 14 years length using a statistical model with first order autoregressive disturbances. A new solution approach, where the serial correlations of the disturbances are eliminated by sequentially differencing the measurements, was used to estimate the model parameters using weighted least squares. The new model parsimoniously represents the 14-year time series with 0.5 mas rms fit, close to the reported 0.1 mas observed pole position precisions for the x and y components. The model can also predict 6 months into the future with less than 4 mas rms prediction error for both polar motion components, and down to sub mas for one-step ahead prediction as validated using a set of daily time series data that are not used in the estimation. This study is dedicated to the memory of Prof. Urho Uotila (1923–2006) whose teaching of “Adjustment Computations” over the years influenced so much, so many of us who had the privilege of being his students.     

Short-term polar motion forecasts from earth system modeling data

Polar motion predictions for up to 10 days into the future are obtained from predicted states of the atmosphere, ocean and continental hydrosphere in a hind-cast experiment covering 2003–2008. High-frequency mass variations within the geophysical fluids are the main cause of wide-band stochastic signals not considered in the presently used statistical prediction approach of IERS bulletin A for polar motion. Taking EAM functions based on forecasted model states, derived from ECMWF medium-range forecasts and corresponding LSDM and OMCT simulations, into account the prediction errors are reduced by 26%. The effective forecast length of the model combination is found to be 7 days, primarily limited by the accuracy of the forecasted atmospheric wind fields. Highest improvements are found for forecast days 4–5 with prediction skill scores of the polar motion excitation functions improved by a factor up to 5. Whereas bulletin A forecasts can explain the observed variance within the first 10 days only by up to 40%, half of the model forecasts reach relative explained variances between 40 and 80%.     

Predictability of the Earth’s polar motion

The numerical prediction of the Earth’s polar motion is of both theoretical and practical interest. The present paper is aimed at a comprehensive, experimental study of the predictability of polar motion using a homogeneous BIH (Bureau International de l’Heure) data set for the period 1967–1983. Based on our knowledge of the physics of the annual and the Chandler wobbles, we build the numerical model for the polar motion by allowing the wobble period to vary. Using an optimum base length of six years for prediction, this “floating-period” model, equipped with a nonlinear least-squares estimator, is found to yield polar motion predictions accurate to within 0″.012 to 0″.024 depending on the prediction length up to one year, corresponding to a predictability of 89–82%. This represents a considerable improvement over the conventional fixed-period predictor, which, by its nature, does not respond to variations in the apparent wobble periods (in particular, a dramatic decrease in the periods of both the annual and the Chandler wobbles after the year 1980). The superiority of the floating-period predictor to other predictors based on critically different numerical models is also demonstrated.     

北斗卫星轨道预报精度分析及改进

针对北斗GEO、IGSO、MEO的3种卫星类型和动态偏航、零偏航两种姿态控制模式,进行了以ECOM光压模型为基础的轨道预报精度分析。确定了北斗3类卫星的短期、中期、长期预报光压参数选择策略。采用光压参数修正法,通过对北斗卫星光压参数长期变化规律建模,有效提升了地影段轨道长期预报精度。研究结果可同时服务于北斗卫星轨道确定及历书参数生成。