Impact of SLR long-term mean range biases on SLRF2020-based orbits of altimetry satellites

Impact of SLR long-term mean range biases on SLRF2020-based orbits of altimetry satellites

Blog Article

Abstract In 2022 and 2023, the new (2020) realizations of the International Terrestrial Reference System (ITRS), namely ITRF2020, JTRF2020, and DTRF2020, were published.One of the differences of these realizations as compared to the previous (2014) ones is the application of Satellite Laser Ranging (SLR) station- and satellite-specific long-term mean range biases (RBs) for the four geodetic Dryer Heating Element satellites LAGEOS-1/-2 and Etalon-1/-2.These RBs were subtracted from the SLR observations used to compute the official ILRS (International Laser Ranging Service) contribution to the ITRS 2020 realizations.This strategy leads to the research question, whether these RBs should and can be applied in SLR-observation-based precise orbit determination (POD) for any satellite to obtain the highest orbit quality possible while using an ITRS 2020 realization as a priori reference frame.

In this paper, based on the POD results for four altimetry satellites (TOPEX/Poseidon, Jason-1, Jason-2, and Jason-3) over the total time interval 1992-2021 and single- as well as multi-satellite altimetry crossover analyses for Jason-2 (2008-2019), we show that the application of LAGEOS-1 long-term mean RBs and the estimation of RBs for certain stations and time spans according to the ILRS Data Handling File (DHF) for SLRF2020 (DHF2020) reduces the root-mean-square fits of SLR observations, scatter of estimated empirical accelerations in along- and cross-track directions, as well as standard deviations of single-satellite crossover differences, and thus improves the orbit quality of Toys all tested altimetry satellites.Consequently, we conclude that the DHF2020 can be used also for any other satellite than LAGEOS-1.However, since the RBs are not only station- but also satellite-dependent, optimal results might be achieved by determining SLR long-term mean RBs for all SLR-tracked satellites and by using the RBs in the POD of these satellites.The smallest residuals of SLR observations and the best orbit quality (smallest values of the standard deviations of single-satellite crossover differences) are obtained, when arc-wise RBs are estimated for each station.

However, one should be careful with applying the latter approach, since frequently (i.e.with a high temporal resolution) estimated biases in the radial direction absorb, beside SLR measurement errors and system delays, also non-modeled or not-perfectly modeled geophysical signals (e.g.

, non-tidal station loading) which might be of particular importance for some analysis.Graphical Abstract.