Future Improvements in EOP Prediction

Abstract

The Earth orientation parameters (EOP) are determined by space geodetic techniques with very high accuracy corresponding to a few millimeters on the Earth’s surface. However, the accuracy of their prediction, even for a few days in the future, is several times lower and still unsatisfactory for certain users. Wavelet decomposition of the EOP data and prediction of their different frequency components reveals that the increase of x, y pole coordinate and UT1-UTC data prediction errors up to about 100 days in the future are mostly caused by irregular short period oscillations with periods less than half a year. These irregular short period variations in x, y pole coordinates data are mostly excited by the equatorial components of atmospheric and ocean excitation functions while in UT1-UTC data are excited mostly by axial component of atmospheric excitation function. The main problem of each prediction technique is to predict simultaneously long and short period oscillations of the EOP data. The nature of short period oscillations in EOP data is mostly stochastic and longer period seasonal oscillations can be modeled using deterministic method. It has been shown that the combination of the prediction methods which are different for deterministic and stochastic part of the EOP can provide the best accuracy of prediction. Several prediction techniques, involving the least-squares extrapolation for prediction of the deterministic part and autoregressive method to predict short period stochastic part are good candidates for the prediction algorithms of the EOP data. The main problem of each prediction technique is to predict simultaneously long and short period oscillations of the EOP data and this problem can be solved by the combination of wavelet transform decomposition with the autocovariance prediction method.