The numerical prediction of the Earth’s polar motion based on an advanced multivariate algorithm - Shi, Kunpeng; Ding, Hao; Chen, Tao; Zou, Chuanyi (Frontiers in Astronomy and Space Sciences, 2023)
Since there are complicated changes in the polar motion (PM) from sub-annual to decadal, precisely predicting it is pretty challenging. Here we provide an advanced multivariate algorithm by combining an iterative oblique singular spectrum analysis (SSA) with pseudo data (IOSSA-pd), and considering much more periodic and quasi-periodic signals (especially long-period oscillations (Ding et al., 2019), and multi-frequency Chandler wobble (Pan, 2012)) than previous studies. The IOSSA in oblique coordinates, due to its weak separability conditions, have a better separation performance than general SSA; and the IOSSA-pd approach will further solve the shift problem. Upon using this IOSSA-pd method, the PM data can be separated into deterministic and stochastic components, extrapolated by multiple-harmonic (MH) and Autoregressive Integrated Moving Average (ARIMA) model, respectively. Based on the IERS EOPC04 PM series, we produced multiple sets of PM predictions with 1-year leading times and reported the IERS Bulletin A predictions as comparison. For 90-day leading time predictions, the mean absolute errors (MAEs) of x- and y-component were 7.69 and 5.12 mas, respectively, while the corresponding MAEs obtained by IERS Bulletin A are 9.45 and 5.69 mas, respectively. Up to 360 days, our algorithm obtains the MAEs of PM slowly accumulating to 12.98 mas on average much better than 19.14 mas for the Bulletin A’s predictions (also significant superior to the corresponding results given by the previous studies). The prediction performance in the middle- and long-term prediction is further compared against the general SSA predictor. In virtue of much weak periodic error, our results show that combining the IOSSA-pd + MH + ARIMA models improved the predicted success rate up to 70.8% and 74.0% for the x- and y-component, respectively.