The application of a combination of weighted least-squares and autoregressive methods in predictions of polar motion parameters

Wu, Fei; Deng, Kazhong; Chang, Guobin; Wang, Qianxin

doi:10.1007/s40328-018-0214-3

Cited by 9 publications

(4 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Most AEs for the HW-VCW forecasts are smaller than MAEs of other researches in relative timespans. On day-1, MAEs of HW-VCW Op are 0.214 mas and 0.154 mas for PMX and PMY, while the best published results are 0.223 mas and 0.183 mas derived by weighted LS + AR (WLS + AR) (Wu et al 2018). In forecasts up to day-30, our MAEs are still smaller than others, indicating that forecasts made by HW-VCW are reliable and stable within 30-day PM forecasts.…”

Section: Secular Stability Testmentioning

confidence: 46%

“…When PMX and PMY are both considered, 59.42% ΔPM are smaller than 0.3 mas, and the root mean square errors (RMSE) is 0.34 mas for 1-day forecast, comparable to those of Dobslaw and Dill (2018) Specifically, in Fig. 4, we compare absolute errors (AEs) and mean absolute errors (MAEs) of 1-, 5-, 10-, 20-and 30-day PM forecasts derived by HW-VCW Op with recent researches (Xu et al 2012;Wu et al 2018Wu et al , 2021Yao et al 2013;Wang et al 2018;Jin et al 2021). Most AEs for the HW-VCW forecasts are smaller than MAEs of other researches in relative timespans.…”

Section: Secular Stability Testmentioning

confidence: 52%

“…By combining LS and AR, Kosek et al (2008) put forward the LS + AR method. Xu et al (2012) applied a Kalman filter prior to LS + AR, while Wu et al (2018) used weighted LS + AR, both of which were proved to be effective, reducing mean absolute errors (MAE) of 1-day X-and Y-pole polar motion (PMX and PMY) forecasts to less than 0.3 mas (milli-arcsecond). Dobslaw and Dill (2018) and Dill et al (2019) forecasted PM using effective angular momentum (EAM) and then adopted LS + AR to model unexplained residuals (named as EAM + LS + AR).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Short-Term Polar Motion Forecast Based on the Holt-Winters Algorithm and Angular Momenta of Global Surficial Geophysical Fluids

et al. 2022

View full text Add to dashboard Cite

By taking into account the variable free polar motion (PM) known as the Chandler wobble (CW) and irregular forced PM excited by quasi-periodic changes in atmosphere, oceans and land water (described by the data of effective angular momenta EAM), we propose a short-term PM forecast method based on the Holt-Winters (HW) additive algorithm (termed as the HW-VCW method, with VCW denoting variable CW). In this method, the variable CW period is determined by minimizing the differences between PM observations and EAM-derived PM for every 8-year sliding timespan. Compared to the X- and Y-pole forecast errors (ΔPMX and ΔPMY) of the International Earth Rotation and Reference Systems Service (IERS) Bulletin A, our results derived from operational EAM can reduce ΔPMX by up to 38.4% and ΔPMY by up to 34.3% for forecasts ranging from 1 to 30 days. Further, we prove that using EAM forecast instead of operational EAM in the HW-VCW method can achieve similar accuracies.

show abstract

Section: Secular Stability Testmentioning

confidence: 46%

Section: Secular Stability Testmentioning

confidence: 52%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Short-Term Polar Motion Forecast Based on the Holt-Winters Algorithm and Angular Momenta of Global Surficial Geophysical Fluids

et al. 2022

View full text Add to dashboard Cite

show abstract

“…For choice of periodic terms in LS extrapolation, Sun et al (2019) introduced the retrograde annual and semiannual wobbles into a harmonic model for LS extrapolation, effectively enhancing PM predictions. Taking into account of variable annual and Chandler wobbles (AW and CW), Sun et al (2012) and Wu et al (2018) utilized the weighted least squares (WLS) for extrapolating the deterministic terms including the CW and AW. In addition, Some studies recently combined singular spectrum analysis (SSA) with LS extrapolation to model the time-variant periodic oscillations (Modiri et al, 2018;Shen et al, 2017;Yang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Medium- and Long-Term Prediction of Polar Motion Using Weighted Least Squares Extrapolation and Vector Autoregressive Modeling

Zhao

Guo

2023

Artificial Satellites

View full text Add to dashboard Cite

This article presents the application of weighted least squares (WLS) extrapolation and vector autoregressive (VAR) modeling in polar motion prediction. A piecewise weighting function is developed for the least squares (LS) adjustment in consideration of the effect of intervals between observation and prediction epochs on WLS extrapolation. Furthermore, the VAR technique is used to simultaneously model and predict the residuals of x p, y p pole coordinates for WLS misfit. The simultaneous predictions of x p, y p pole coordinates are subsequently computed by the combination of WLS extrapolation of harmonic models for the linear trend, Chandler and annual wobbles, and VAR stochastic prediction of the residuals (WLS+VAR). The 365-day-ahead x p, y p predictions are compared with those generated by LS extrapolation+univariate AR prediction and LS extrapolation+VAR modeling. It is shown that the x p, y p predictions based on WLS+VAR taking into consideration both the interval effect and correlation between x p and y p outperform those generated by two others. The accuracies of the x p predictions are 13.97 mas, 18.47 mas, and 20.52 mas, respectively for the 150-, 270-, and 365-day horizon in terms of the mean absolute error statistics, 36%, 24.8%, and 33.5% higher than LS+AR, respectively. For the y p predictions, the 150-, 270-, and 365-day accuracies are 15.41 mas, 21.17 mas, and 21.82 mas respectively, 27.4%, 11.9%, and 21.8% higher than LS+AR respectively. Moreover, the absolute differences of the WLS+VAR predictions and observations are smaller than the differences from LS+VAR and LS+AR, which is practically important to practical and scientific users, although the improvement in accuracies is no more than 10% relative to LS+VAR. The further comparison with the predictions submitted to the 1st Earth Orientation Parameters Prediction Comparison Campaign (1st EOP PCC) shows that while the accuracy of the predictions within 30 days is comparable with that by the most accurate prediction techniques including neural networks and LS+AR participating in the campaign for x p, y p pole coordinates, the accuracy of the predictions up to 365 days into the future are better than accuracies by the other techniques except best LS+AR used in the EOP PCC. It is therefore concluded that the medium- and long-term prediction accuracy of polar motion can be improved by modeling x p, y p pole coordinates together.

show abstract

Selecting data for autoregressive modeling in polar motion prediction

Chang

Deng

et al. 2019

Acta Geod Geophys

View full text Add to dashboard Cite

The application of a combination of weighted least-squares and autoregressive methods in predictions of polar motion parameters

Cited by 9 publications

References 13 publications

Short-Term Polar Motion Forecast Based on the Holt-Winters Algorithm and Angular Momenta of Global Surficial Geophysical Fluids

Short-Term Polar Motion Forecast Based on the Holt-Winters Algorithm and Angular Momenta of Global Surficial Geophysical Fluids

Medium- and Long-Term Prediction of Polar Motion Using Weighted Least Squares Extrapolation and Vector Autoregressive Modeling

Selecting data for autoregressive modeling in polar motion prediction

Contact Info

Product

Resources

About