Long-term prediction of polar motion using a combined SSA and ARMA model

To meet the need for real-time and high-accuracy predictions of polar motion (PM), the singular spectrum analysis (SSA) and the autoregressive moving average (ARMA) model are combined for short- and long-term PM prediction. According to the SSA results for PM and the SSA prediction algorithm, the principal components of PM were predicted by SSA, and the remaining components were predicted by the ARMA model. In applying this proposed method, multiple sets of PM predictions were made with lead times of two years, based on an IERS 08 C04 series. The observations and predictions of the principal components correlated well, and the SSA $$+$$+ ARMA model effectively predicted the PM. For 360-day lead time predictions, the root-mean-square errors (RMSEs) of PMx and PMy were 20.67 and 20.42 mas, respectively, which were less than the 24.46 and 24.78 mas predicted by IERS Bulletin A. The RMSEs of PMx and PMy in the 720-day lead time predictions were 28.61 and 27.95 mas, respectively.

[1]  C. Bizouard,et al.  The Combined Solution C04 for Earth Orientation Parameters Consistent with International Terrestrial Reference Frame 2005 , 2009 .

[2]  Yibin Yao,et al.  A new LS+AR model with additional error correction for polar motion forecast , 2013, Science China Earth Sciences.

[3]  Nico Sneeuw,et al.  Singular spectrum analysis for modeling seasonal signals from GPS time series , 2013 .

[4]  M. S. Carter,et al.  Recent improvements in IERS Rapid Service/Prediction Center products , 2009 .

[5]  Lintao Liu,et al.  Long-term polar motion prediction using normal time–frequency transform , 2014, Journal of Geodesy.

[6]  L. Zotov.  Dynamical Modeling and Excitation Reconstruction as Fundamental of Earth Rotation Prediction , 2010 .

[7]  Dmitri Kondrashov,et al.  Stochastic modeling of decadal variability in ocean gyres , 2015 .

[8]  David H. Schoellhamer,et al.  Singular spectrum analysis for time series with missing data , 2001 .

[9]  Michael Ghil,et al.  Gap filling of solar wind data by singular spectrum analysis , 2010 .

[10]  M. Kalarus,et al.  Achievements of the Earth orientation parameters prediction comparison campaign , 2010 .

[11]  Hossein Hassani,et al.  Singular Spectrum Analysis: Methodology and Comparison , 2021, Journal of Data Science.

[12]  Sergey Kravtsov,et al.  Atlantic Multidecadal Oscillation and Northern Hemisphere’s climate variability , 2010, Climate Dynamics.

[13]  H. Schuh,et al.  Linear drift and periodic variations observed in long time series of polar motion , 2001 .

[14]  Rodrigo Cauduro Dias de Paiva,et al.  Assessment of terrestrial water balance using remote sensing data in South America , 2019, Journal of Hydrology.

[15]  J. Rial,et al.  Synchronization of the climate system to eccentricity forcing and the 100,000-year problem , 2013 .

[16]  R. Vautard,et al.  Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series , 1989 .

[17]  O. Akyilmaz,et al.  Prediction of Earth rotation parameters by fuzzy inference systems , 2004 .

[18]  Ayman A. Hassan,et al.  Assessment of terrestrial water contributions to polar motion from GRACE and hydrological models , 2012 .

[19]  G. P. King,et al.  Extracting qualitative dynamics from experimental data , 1986 .

[20]  Kung-Sik Chan,et al.  Time Series Regression Models , 2008 .

[21]  C. K. Shum,et al.  Fuzzy-wavelet based prediction of Earth rotation parameters , 2011, Appl. Soft Comput..

[22]  M. Kalarus,et al.  Forecasting of the Earth orientation parameters - comparison of different algorithms , 2008 .

[23]  Jianli Chen,et al.  Hydrological excitations of polar motion, 1993–2002 , 2005 .

[24]  Yingyan Cheng,et al.  An enhanced singular spectrum analysis method for constructing nonsecular model of GPS site movement , 2016 .

[25]  Jim R. Ray,et al.  Evaluation of GPS orbit prediction strategies for the IGS Ultra-rapid products , 2011, GPS Solutions.

[26]  Forecasting the polar motions of the deformable Earth , 2002 .

[27]  W. Kosek,et al.  Possible improvement of Earth orientation forecast using autocovariance prediction procedures , 1998 .

[28]  J. Beckers,et al.  EOF Calculations and Data Filling from Incomplete Oceanographic Datasets , 2003 .

[29]  Xinhao Liao,et al.  Long-term prediction of the Earth Orientation Parameters by the artificial neural network technique , 2012 .

[30]  Michael Ghil,et al.  Spatio-temporal filling of missing points in geophysical data sets , 2006 .

[31]  Xueqing Xu,et al.  Short-term earth orientation parameters predictions by combination of the least-squares, AR model and Kalman filter , 2012 .

[32]  H. Schuha,et al.  VLBI: A fascinating technique for geodesy and astrometry , 2013 .

[33]  Jinyun Guo,et al.  Seasonal and inter-annual variations of length of day and polar motion observed by SLR in 1993–2006 , 2009 .

[34]  Xueqing Xu,et al.  EOP prediction using least square fitting and autoregressive filter over optimized data intervals , 2015 .

[35]  Kung-Sik Chan,et al.  Time Series Analysis: With Applications in R , 2010 .

[36]  Jinyun Guo,et al.  Earth rotation parameter and variation during 2005–2010 solved with LAGEOS SLR data☆ , 2015 .

[37]  Harald Schuh,et al.  Prediction of Earth orientation parameters by artificial neural networks , 2002 .

[38]  M. Valipour,et al.  Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir , 2013 .