Improved LS+MAR hybrid method to UT1-UTC ultra-short-term prediction by using first-order-difference UT1-UTC

Abstract

Accurate ultra-short-term prediction of the Earth rotation parameters (ERP) holds paramount importance for real-time applications, particularly in reference frame conversion. Among them, diurnal rotation (UT1-UTC) which cannot be directly estimated through Global Navigation Satellite System (GNSS) techniques, significantly affects the rapid and ultra-rapid orbit determination of GNSS satellites. Presently, the traditional LS (least squares) + AR (autoregressive) and LS + MAR (multivariate autoregressive) hybrid methods stand as primary approaches for UT1-UTC ultra-short-term predictions (1–10 days). The LS + MAR hybrid method relies on the UT1-UTC and LOD (length of day) series. However, the correlation between LOD and first-order-difference UT1-UTC is stronger than that between LOD and UT1-UTC. In light of this, and with the aid of the first-order-difference UT1-UTC, we propose an enhanced LS + MAR hybrid method to UT1-UTC ultra-short-term prediction. By using the UT1-UTC and LOD data series of the IERS (International Earth Rotation and Reference Systems Service) EOP 14 C04 product, we conducted a thorough analysis and evaluation of the improved method's prediction performance compared to the traditional LS + AR and LS + MAR hybrid methods. According to the numerical results over more than 210 days, they demonstrate that, when considering the correlation information between the LOD and the first-order-difference UT1-UTC, the mean absolute errors (MAEs) of the improved LS + MAR hybrid method range from 21 to 934 μs in 1–10 days predictions. In comparison to the traditional LS + AR hybrid method, the MAEs show a reduction of 7–53 μs in 1–10 days predictions, with corresponding improvement percentages ranging from 1 to 28%. Similarly, when compared to the traditional LS + MAR hybrid method, the MAEs have a reduction of 5–42 μs in 1–10 days predictions, with corresponding improvement percentages ranging from 4–20%. Additionally, when aided by GNSS-derived LOD data series, the MAEs of improved LS + MAR hybrid method experience further reduction.