A new medium-long term polar motion prediction method based on sliding average within difference series

Abstract

Users such as satellite navigation orbiting and deep space exploration are demanding more and more accuracy in polar motion (PM) forecasting, while the existing PM forecasting methods are limited in the single input data and forecasting strategy. To address the problem, this study proposes a new idea of PM forecasts by combining the differences between PM series and the sliding average within the PM series. We use the LS+AR models driven by IERS-14C04 series, to perform experiments with the traditional method and the sliding average method within the PMX, PMY and PM(Y-X) series, respectively. Among them, the prediction of PMX is obtained by subtracting the forecast of PMY of traditional method and the prediction of PM(Y-X) of the sliding average method, the forecast of PMY is obtained by adding the forecast of PMX of the sliding average method and the forecast of PM(Y-X) of the traditional method. The results of the 418-week hindcast experiment from 2012 to 2021 show that the proposed method has a greater improvement than the traditional method, and the corresponding 1-365-day forecasting accuracies are improved by 31.46% and 21.11% over the traditional method, respectively, on average. It has certain advantages over the IERS Bulletin-A in the medium-long term. This not only verifies that the stability and ability of the method have some competitive, but also provides a new idea for the research of PM prediction.