A new method to improve the prediction of the celestial pole offsets

Abstract

Knowledge of the Earth’s changing rotation is fundamental to positioning objects in space and on the planet. Nowadays, the Earth’s orientation in space is expressed by five Earth Orientation Parameters (EOP). Many applications in astronomy, geosciences, and space missions require accurate EOP predictions. Operational predictions are released daily by the Rapid Service/Prediction Centre of the International Earth Rotation and Reference Systems Service (IERS). The prediction procedures and performances differ for the three EOP classes: polar motion, rotation angle (UT1-UTC), and the two celestial pole offsets (CPO), dX and dY. The IERS Annual Report 2016 shows Rapid Service CPO predictions errors with respect to IERS 08 C04 observations in 2016 ranging from 120 to 140 μas in 40 days for dX, and 100–160 μas for dY. We test a new method for the CPO prediction based on the recent availability of sophisticated empirical models for the Free Core Nutation, a main component of the CPO variations. We found it allows predicting both CPO with error estimates for the period 2000–2016 lower than the 2016 Rapid Service products, reaching about 85 μas after 40 days and near 90 μas after a year. These results would represent a 35–40% improvement.

Smoothing and predicting celestial pole offsets using a Kalman filter and smoother

“…Therefore, the increase in RMS for JPL predictions is less steady. However, when going from ultra-short to short-term and mid-term predictions, the increase in JPL prediction errors is less rapid than for the case of Belda et al (2018) predictions. The average RMS reduction compared to the IERS Rapid Service forecasts is lower than for the CPO predictions developed by Belda et al (2018) (19% and 22% for dX and dY, respectively).…”

“…The CPO predictions developed by Belda et al (2018) revealed visibly lower RMS prediction errors than official IERS products, reaching about 0.090 mas after 25 days, 0.100 mas after 50 days and about 0.105 mas after 90 days ( Fig. 5 in Belda et al 2018). The average RMS reduction compared to the IERS Rapid Service forecasts was equal to 39.5% for dX and 33.8% for dY.…”

“…Daily CPO series were forecasted by extrapolating the FCN model with the amplitude taken from the 08 C04 CPO series of the 400 days preceding the prediction epoch. The CPO predictions developed by Belda et al (2018) revealed visibly lower RMS prediction errors than official IERS products, reaching about 0.090 mas after 25 days, 0.100 mas after 50 days and about 0.105 mas after 90 days ( Fig. 5 in Belda et al 2018).…”

“…In his work, Malkin (2010b) proved that the smoothing of the input CPO series can provide decreased prediction errors. Similarly to the method developed by Belda et al (2018), for ZM2 forecasts the error growth is almost uniform along the 100-day period considered.…”

“…The use of a Kalman filter and smoother provides CPO predictions that are as accurate as those achieved by other authors. For example, in the newest work of Belda et al (2018), the authors proved that the use of an accurate FCN prediction can reduce significantly the errors of CPO predictions. They developed a simple method of CPO prediction that used the conventional IERS EOP 08 C04 CPO series (Bizouard and Gambis 2009) and an empirical FCN model fitted to VLBI data (Belda et al 2016), as input data sets.…”