Accurate Earth orientation parameter (EOP) predictions are needed for many applications, e.g., for the tracking and navigation of interplanetary spacecraft missions. One of the most difficult parameters to forecast is the length of day (LOD), which represents the variation in the Earth’s rotation rate since it is primarily affected by the torques associated with changes in atmospheric circulation. In this study, a new-generation time-series prediction algorithm is developed. The one-dimensional convolutional neural network (1D CNN), which is one of the deep learning methods, is introduced to model and predict the LOD using the IERS EOP 14 C04 and axial Z component of the atmospheric angular momentum (AAM), which was taken from the German Research Centre for Geosciences (GFZ) since it is strongly correlated with the LOD changes. The prediction procedure operates as follows: first, we detrend the LOD and Z-component series using the LS method, then, we obtain the residual series of each one to be used in the 1D CNN prediction algorithm. Finally, we analyze the results before and after introducing the AAM function. The results prove the potential of the proposed method as an optimal algorithm to successfully reconstruct and predict the LOD for up to 7 days.

The relationship between the length of day (LOD) and El-Niño Southern Oscillation (ENSO) has been well studied since the 1980s. LOD is the negative time-derivative of UT1-UTC, which is directly proportional to Earth Rotation Angle (ERA), one of the Earth Orientation Parameters (EOP). The EOP can be determined using Very Long Baseline Interferometry (VLBI), which is a space geodetic technique. In addition, satellite techniques such as the Global Navigation Satellite System (GNSS), Satellite Laser Ranging (SLR), Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) can provide Earth Rotation Parameters, i.e., polar motion and LOD. ENSO is a climate phenomenon occurring over the tropical eastern Pacific Ocean that mainly affects the tropics and the subtropics. Extreme ENSO events can cause extreme weather like flooding and droughts in many parts of the world. In this work, we investigated the effect of ENSO on the LOD from January 1979 to April 2022 using the wavelet coherence method. This method computes the coherence between the two non-stationary time-series in the time-frequency domain using the real-valued Morlet wavelet. We used the Multivariate ENSO index version 2 (MEI v.2) which is the most robust series as the climate index for the ENSO, and LOD time-series from IERS (EOP 14 C04 (IAU2000A)). We also used Oceanic Niño and Southern Oscillation index in this study for comparison. The results show strong coherence of 0.7 to 0.9 at major ENSO events for the periods 2–4 years between LOD and MEI.v2.

The Potsdam Open Source Radio Interferometry Tool (PORT) is the very long baseline interferometry (VLBI) analysis software developed and maintained at the GFZ German Research Centre for Geosciences. Chiefly, PORT is tasked with the timely processing of VLBI sessions and post-processing activities supporting the generation of celestial and terrestrial reference frames. In addition, it serves as a framework for research and development within the GFZ’s VLBI working group and is part of the tool set employed in educating young researchers. Starting out from VLBI group delays, PORT estimates station and radio sources positions, as well as Earth orientation parameters, tropospheric parameters, and station clock offsets and drifts. The estimation procedures take into account all the necessary data analysis models that were agreed on for contributing to the ITRF2020 processing activities. The PORT code base is implemented in the MATLAB ^® and Python programming languages. It is licensed under the terms of the GNU General Public License and available for download at GFZ’s Git server https://git.gfz-potsdam.de/vlbi-data-analysis/port.

The next-generation Global Navigation Satellite Systems (NextGNSS) satellites are planned to be equipped with inter-satellite links and ultra-stable clocks as well as a dedicated Very Long Baseline Interferometry (VLBI) transmitter. This will enable the VLBI network to observe the satellites along with extra-galactic radio sources. The study aims to evaluate the potential benefits by placing VLBI transmitters on NextGNSS satellites. This will empower the NextGNSS to determine UT1-UTC, which is otherwise impossible directly. Furthermore, VLBI observations of satellites would allow for independent validation of satellite orbit determination. In this study, we investigate geodetic parameters such as station positions and Earth Rotation Parameters (ERPs) and the impact of different network geometry on these parameters. Based on the initial findings, it appears that using satellites and quasars in VLBI can define a datum with No-Net Rotation (NNR) without the need for No-Net Translation (NNT) conditions. When both NNR and NNT are imposed, the Helmert transformation parameters are smaller compared to when only NNR is imposed. This can be improved by optimizing the network geometry. Furthermore, the study’s findings indicate that VLBI observations can determine the satellite’s orbit with cm-level accuracy. The performance of the ERPs is better in a uniformly distributed network especially when only NNR condition is imposed.

This work focuses on the assessment of UT1-UTC estimates from various types of sessions during the CONT17 campaign. We chose the CONT17 campaign as it provides 15 days of continuous, high-quality VLBI data from two legacy networks (S/X band), i.e., Legacy-1 (IVS) and Legacy-2 (VLBA) (having different network geometry and are non-overlapping), two types of Intensive sessions, i.e., IVS and Russian Intensives, and five days of new-generation, broadband VGOS sessions. This work also investigates different approaches to optimally compare dUT1 from Intensives with respect to the 24 h sessions given the different parameterization adopted for analyzing Intensives and different session lengths. One approach includes the estimation of dUT1 from pseudo Intensives, which are created from the 24 h sessions having their epochs synchronized with respect to the Intensive sessions. Besides, we assessed the quality of the dUT1 estimated from VGOS sessions at daily and sub-daily resolution. The study suggests that a different approach should be adopted when comparing the dUT1 from the Intensives, i.e., comparison of dUT1 value at the mean epoch of an Intensive session. The initial results regarding the VGOS sessions show that the dUT1 estimated from VGOS shows good agreement with the legacy network despite featuring fewer observations and stations. In the case of sub-daily dUT1 from VGOS sessions, we found that estimating dUT1 with 6 h resolution is superior to other sub-daily resolutions. Moreover, we introduced a new concept of sub-daily dUT1-tie to improve the estimation of dUT1 from the Intensive sessions. We observed an improvement of up to 20% with respect to the dUT1 from the 24 h sessions.