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

Abstract.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 χ2 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.

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

[2]  Benjamin F. Chao,et al.  Predictability of the Earth’s polar motion , 1984 .

[3]  John Waldron,et al.  The Langevin Equation: With Applications in Physics, Chemistry and Electrical Engineering , 1996 .

[4]  W. Kosek.  Autocovariance prediction of complex-valued polar motion time series , 2002 .

[5]  A. Brzezinski,et al.  A stochastic model for polar motion with application to smoothing, prediction, and combining. , 1996 .

[6]  A. Brzezinski.  Polar motion excitation by variations of the effective angular momentum function: considerations concerning deconvolution problem , 1992, manuscripta geodaetica.

[7]  B. Chao,et al.  Effect of Long-Period Ocean Tides on the Earth's Polar Motion , 1997 .

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

[9]  R. O. Vicente,et al.  An analysis of the homogeneous ILS polar motion series , 1980 .

[10]  Dennis D. McCarthy,et al.  Prediction of Earth orientation , 1991 .

[11]  T. M. Eubanks,et al.  The short-term prediction of universal time and length of day using atmospheric angular momentum , 1994 .

[12]  Michael Ghil,et al.  Adaptive filtering and prediction of the Southern Oscillation index , 1992 .

[13]  Richard S. Gross,et al.  A Kalman-filter-based approach to combining independent Earth-orientation series , 1998 .

[14]  R. Vautard,et al.  Singular-spectrum analysis: a toolkit for short, noisy chaotic signals , 1992 .

[15]  David D. Morabito,et al.  Kalman Filtering of Earth Orientation Changes , 1988 .

[16]  Ingrid Daubechies,et al.  Ten Lectures on Wavelets , 1992 .

[17]  S.M. Kay,et al.  Spectrum analysis—A modern perspective , 1981, Proceedings of the IEEE.