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

Accurate ultra-short-term prediction of the Earth rotation parameters(ERP)holds paramount impor-tance for real-time applications,particularly in reference frame conversion.Among them,diurnal rota-tion(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.Pres-ently,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.