Powiadomienia systemowe
- Sesja wygasła!
Identyfikatory
Warianty tytułu
Języki publikacji
Abstrakty
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 xp, yp pole coordinates for WLS misfit. The simultaneous predictions of xp, yp 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 xp, yp predictions are compared with those generated by LS extrapolation+univariate AR prediction and LS extrapolation+VAR modeling. It is shown that the xp, yp predictions based on WLS+VAR taking into consideration both the interval effect and correlation between xp and yp outperform those generated by two others. The accuracies of the xp 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 yp 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 xp, yp 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 xp, yp pole coordinates together.
Słowa kluczowe
Rocznik
Tom
Strony
42--55
Opis fizyczny
Bibliogr. 34 poz., rys., tab.
Twórcy
autor
- School of Computer Science and Technology, Xi’an University of Posts and Telecommunications, Xi’an, China
autor
- School of Electronic and Electrical Engineering, Baoji University of Arts and Sciences, Baoji, China
autor
- Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, China
Bibliografia
- Akaike H. (1971) Autoregressive model fitting for control, Annals of the Institute of Statistical Mathematics, Vol. 23, No. 1, 163-180.
- Akyilmaz O, Kutterer H. (2004) Prediction of Earth rotation parameters by fuzzy inference systems, Journal of Geodesy, Vol. 78, No. 1, 82-93.
- Bizouard C, Lambert S, Gattano C, et al. (2019) The IERS EOP 14C04 solution for Earth orientation parameters consistent with ITRF 2014, Journal of Geodesy, Vol. 93, No. 5, 621-633.
- Chin T, Gross R, Dickey J. (2004) Modeling and forecast of the polar motion excitation functions for short-term polar motion prediction, Journal of Geodesy, Vol. 78, No. 6, 343-353.
- Dick W, Thaller D. (2020) IERS annual report 2018, International Earth Rotation and Reference Systems Service, Central Bureau, Frankfurt am Main.
- Dill R, Dobslaw H. (2010) Short-term polar motion forecasts from Earth system modeling data, Journal of Geodesy, Vol. 84, No. 9, 529-536.
- Dill R, Dobslaw H, Thomas M. (2019) Improved 90-day Earth orientation predictions from angular momentum forecasts of atmosphere, ocean, and terrestrial hydrosphere, Journal of Geodesy, Vol. 93, No. 3, 287-295.
- Dobslaw H, Dill R. (2017) Predicting Earth rotation variations from global forecasts of atmosphere-hydrosphere dynamics, Advances in Space Research, Vol. 61, No. 4, 1047-1054.
- Gambis D, Luzum B. (2011) Earth rotation monitoring, UT1determination and prediction, Metrologia, Vol. 48, No. 4, S165.
- Guo J, Li Y, Dai C, et al. (2013) A technique to improve the accuracy of Earth orientation prediction algorithms based on least squares extrapolation, Journal of Geodynamics, Vol. 70, No. 10, 36-48.
- IERS Annual Report 2018. Edited by Wolfgang R. Dick and Daniela Thaller. International Earth Rotation and Reference Systems Service, Central Bureau. Frankfurt am Main: Verlag des Bundesamts für Kartographie und Geodäsie, 2020. 207 p., ISBN 978-3-86482-136-3 (print version).
- Jin X, Liu X, Guo J. et al. (2021) Analysis and prediction of polar motion using MSSA method, Earth, Planets and Space, Vol. 73, No. 1, 1-13.
- Kalarus M, Schuh H, Kosek W, et al. (2010) Achievements of the Earth orientation parameters prediction comparison campaign, Journal of Geodesy, Vol. 84, No. 10, 587-596.
- Kosek W, McCarthy D, Luzum B. (1998) Possible improvement of Earth orientation forecast using autocovariance prediction procedures, Journal of Geodesy, Vol. 72, No. 4, 189-199.
- Kosek W, McCarthy D, Luzum B. (2001). El Nino impact on polar motion prediction errors, ˜ Studia Geophysica et Geodaetica, Vol. 45, No. 4, 347-361.
- Love I, Zicchino L. (2006) Financial development and dynamic investment behavior: evidence from panel VAR, The Quarterly Review of Economics and Finance, Vol. 46, No. 2, 190-210.
- Modiri S, Belda S, Heinkelmann R, et al. (2018) Polar motion prediction using the combination of SSA and copula-based analysis, Earth, Planets and Space, Vol. 70, No. 1, 1-18.
- Schuh H, Nagel S, Seitz T. (2001) Linear drift and periodic variations observed in long time series of polar motion, Journal of Geodesy, Vol. 74, No. 10, 701-710.
- Schuh H, Ulrich M, Egger D, et al. (2002) Prediction of Earth orientation parameters by artificial neural networks, Journal of Geodesy, Vol. 76, No. 5, 247-258.
- Shen Y, Guo J, Liu X, et al (2017) One hybrid model combining singular spectrum analysis and LS + ARMA for polar motion prediction, Advances in Space Research, Vol. 59, No. 2, 513-523.
- Su X, Liu L, Houtse H, et al. (2014) Long-term polar motion prediction using normal time–frequency transform, Journal of Geodesy, Vol. 88, No. 2, 145-155.
- Sun Z, Xu T (2012) Prediction of Earth rotation parameters based on improved weighted least squares and autoregressive model Geodesy and Geodynamics, Vol. 3, No. 3, 57-64.
- Sun Z, Xu T, Jiang C, et al. (2019) An improved prediction algorithm for Earth’ s polar motion with considering the retrograde annual and semi-annual wobbles based on least squares and autoregressive model, Acta Geodaetica et Geophysica, Vol. 54, No. 4, 499-511.
- Wang L, Miao W, Wu F. (2022) A new polar motion prediction method combined with the difference between polar motion series Geodesy and Geodynamics, Vol. 13, No. 6, 564-572.
- Wooden W, van Dam T, Kosek W. (2006) IERS Working Group on prediction plans and activities, EOS Trans. AGU, 87(52), In AGU Fall Meeting Abstracts , pp. G43A-0988.
- Wu F, Chang G, Deng K, et al. (2019) Selecting data for autoregressive modeling in polar motion prediction, Acta Geodaetica et Geophysica, Vol. 54, No. 4, 557-566.
- Wu F, Deng K, Chang G, et al. (2018) The application of a combination of weighted least-squares and autoregressive methods in predictions of polar motion parameters, Acta Geodaetica et Geophysica, Vol. 53, No. 2, 247-257.
- Wu F, Liu Z, Deng K, et al. (2021) A polar motion prediction method considering the polar coordinates, Advances in Space Research, Vol. 68, No. 3, 1318-1328.
- Xu X, Zhou Y. (2015) EOP prediction using least square fitting and autoregressive filter over optimized data intervals, Advances in Space Research, Vol. 56, No. 10, 2248-2253.
- Xu X, Zhou Y, Liao X. (2012) Short-term Earth orientation parameters predictions by combination of the least-squares, AR model and Kalman filter, Journal of Geodynamics, Vol. 62, No. 12, 83-86.
- Yang Y, Nie W, Xu T, et al. (2022) Earth orientation parameters prediction based on the hybrid SSA+LS+SVM model, Measurement Science and Technology, Vol. 33, No. 12, 125011.
- Zhang H, Wang Q, Zhu J, Zhang X. Application of CLS+AR model polar motion to prediction based on time-varying parameters correction of Chandler wobble, Geomatics and Information Science of Wuhan University, Vol. 37, No. 3, 286-289.
- Zhao D, Lei Y. (2019) Possible enhancement of Earth’ s polar motion predictions using a wavelet-based preprocessing procedure, Studia Geophysica et Geodaetica, Vol. 63, No. 1, 83-94.
- Zhao D, Lei Y. (2020) A technique to reduce the edge effect in least squares extrapolation for enhanced Earth orientation prediction., Studia Geophysica et Geodaetica, Vol. 64, No. 3, 293-305.
Uwagi
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-41dd41d3-6321-4bad-bfee-6e705bc1b06b