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Rapid provision of Earth orientation parameters (EOPs, here polar motion and dUT1) is indispensable in many geodetic applications and also for spacecraft navigation. There are, however, discrepancies between the rapid EOPs and the final EOPs that have a higher latency but the highest accuracy. To reduce these discrepancies, we focus on a data‐driven approach, present a novel method named ResLearner, and use it in the context of deep ensemble learning. Furthermore, we introduce a geophysically constrained approach for ResLearner. We show that the most important geophysical information to improve the rapid EOPs is the effective angular momentum functions of atmosphere, ocean, land hydrology, and sea level. In addition, semidiurnal, diurnal, and long‐period tides coupled with prograde and retrograde tidal excitations are important features. The influence of some climatic indices on the prediction accuracy of dUT1 is discussed, and El Niño Southern Oscillation is found to be influential. We developed an operational framework, providing the improved EOPs on a daily basis with a prediction window of 63 days to fully cover the latency of final EOPs. We show that under the operational conditions and using the rapid EOPs of the International Earth Rotation and Reference Systems Service (IERS), we achieve improvements as high as 60%, thus significantly reducing the differences between rapid and final EOPs. Furthermore, we discuss how the new final series IERS 20 C04 is preferred over 14 C04. Finally, we compare against EOP hindcast experiments of the European Space Agency, on which ResLearner presents comparable improvements. Plain Language Summary: The International Earth Rotation and Reference Systems Service (IERS) provides rapid Earth orientation parameters (EOPs) using different space‐geodetic techniques to bridge the latency of the final, most accurate EOPs solution. However, these rapid EOPs are not in full agreement with the final EOPs. In order to reduce the differences between the rapid and final EOPs, we focus on the application of machine learning and present a novel method named ResLearner, which is based on geodetic data and geophysical constraints. We present the method in the context of deep ensemble learning, focusing on a prediction window of 63 days. We also attempt to link informative geophysical effects to these discrepancies. We show that they are linked to a mixture of atmospheric, oceanic, hydrological, and sea level effective angular momentum functions, dominance of the Global Navigation Satellite Systems‐derived polar motion, and various short‐ and long‐term tidal excitations. El Niño Southern Oscillation is also relevant for dUT1 prediction. The methodology can provide significant improvements of up to 60% in operational settings with respect to rapid EOPs provided by IERS. Additional validation is done by using the data of Jet Propulsion Laboratory final EOP series and also EOP series provided by the European Space Agency. Key Points: We introduce a novel machine learning algorithm named ResLearner to improve the accuracy of rapid and predicted Earth orientation parameters (EOPs)We also present geophysically constrained ResLearner, using Earth's effective angular momentum functions, tides, and climatic indicesBesides prediction, ResLearner is also able to effectively correct deficits in rapidly processed EOPs with respect to final EOPs [ABSTRACT FROM AUTHOR]

A major problem in the precise orbit determination (POD) of satellites at altitudes below 1,000 km is the modeling of the aerodynamic drag which mainly depends on the thermospheric density and causes the largest non‐gravitational acceleration. Typically, empirical thermosphere models are used to calculate density values at satellite positions but current thermosphere models cannot provide the required accuracy. Thus, unaccounted variations in the thermospheric density may lead to significantly incorrect satellite positions. For the first time, we bring together thermospheric density corrections for the NRLMSISE‐00 model in terms of scale factors with a temporal resolution of 12 hr derived from satellite laser ranging (SLR) and accelerometer measurements. Whereas, the latter are in situ information given along the satellite orbit, SLR results have to be interpreted as mean values along the orbit within the underlying time interval. From their comparison, we notice a rather similar behavior with correlations of up to 80% and more depending on altitude. During high solar activity, scale factors vary around 30% at low solar activity and up to 70% at high solar activity from the value one. In addition, we found the scaled thermospheric density decreasing stronger as the modeled density of NRLMSISE‐00. To check the reliability of the SLR‐derived scale factors, we compare the POD result of two different software packages, namely DOGS‐OC from DGFI‐TUM and GROOPS from IGG Bonn. Furthermore, a validation of our estimated scale factors with respect to an external data set proofs the high quality of the obtained results. Plain Language Summary: Variations in the density of the thermosphere must be taken into account when modeling and predicting the motion of satellites in the near‐Earth environment. Typically, thermospheric densities at the position of satellites are provided by models, but their accuracy is limited. Due to the sensitivity of satellites orbiting the Earth in the altitude range of the thermosphere, they can be used to derive information about the thermospheric density. In this study, we compare for the first time thermospheric density corrections in terms of scale factors for the NRLMSISE‐00 model with a temporal resolution of 12 hr derived from two geodetic measurement techniques, namely satellite laser ranging (SLR) and accelerometry. Our results demonstrate that both measurement techniques can be used to derive comparable scale factors of the thermospheric density, which vary around the desired value one. This indicates to which extent the NRLMSISE‐00 model differs from the observed thermospheric density. On average, during high solar activity, the model underestimates the thermospheric density and can be scaled up using the estimated scale factors. We additionally discuss our estimated scale factors with respect to an external data set. Furthermore, we validate the approach of deriving scale factors from SLR measurements by using two independent software packages. Key Points: For the first time, we compare scale factors of the thermospheric density derived from satellite laser ranging (SLR) and accelerometer measurementsThe estimated scale factors vary by up to 30% at low solar activity and up to 70% at high solar activity from the desired value 1Correlations of 0.7–0.8 are obtained between the estimated scale factors from SLR and accelerometer measurements depending on the height [ABSTRACT FROM AUTHOR]

In this study, we present an empirical model, named CH-Therm-2018, of the thermospheric mass density derived from 9-year (from August 2000 to July 2009) accelerometer measurements from the CHAllenging Minisatellite Payload (CHAMP) satellite at altitudes from 460 to 310 km. The CHAMP dataset is divided into two 5-year periods with 1-year overlap (from August 2000 to July 2005 and from August 2004 to July 2009) to represent the high-to-moderate and moderate-to-low solar activity conditions, respectively. The CH-Therm-2018 model describes the thermospheric density as a function of seven key parameters, namely the height, solar flux index, season (day of year), magnetic local time, geographic latitude and longitude, as well as magnetic activity represented by the solar wind merging electric field. Predictions of the CH-Therm-2018 model agree well with CHAMP observations (within 20 %) and show different features of thermospheric mass density during the two solar activity levels, e.g., the March-September equinox asymmetry and the longitudinal wave pattern. From the analysis of satellite laser ranging (SLR) observations of the ANDE-Pollux satellite during August-September 2009, we estimate 6 h scaling factors of the thermospheric mass density provided by our model and obtain the median value equal to 1:267±0:60. Subsequently, we scale up our CHTherm-2018 mass density predictions by a scale factor of 1.267. We further compare the CH-Therm-2018 predictions with the Naval Research Laboratory Mass Spectrometer Incoherent Scatter Radar Extended (NRLMSISE-00) model. The result shows that our model better predicts the density evolution during the last solar minimum (2008-2009) than the NRLMSISE-00 model. [ABSTRACT FROM AUTHOR]

For low Earth orbiting satellites, non-gravitational forces cause one of the largest perturbing accelerations. During a precise orbit determination (POD), the accurate modeling of the satellite-body attitude and solar panel orientation is important since the satellite's effective cross-sectional area is directly related to the perturbing acceleration. Moreover, the position of tracking instruments that are mounted on the satellite body are affected by the satellite attitude. For satellites like Jason-1/-2/-3, attitude information is available in two forms—as a so-called nominal yaw steering model and as observation-based (measured by star tracking cameras) quaternions of the spacecraft body orientation and rotation angles of the solar arrays. In this study, we have developed a preprocessing procedure for publicly available satellite attitude information. We computed orbits based on Satellite Laser Ranging (SLR) observations to the Jason satellites at an overall time interval of approximately 25 years, using each of the two satellite attitude representations. Based on the analysis of the orbits, we investigate the influence of using preprocessed observation-based attitude in contrast to using a nominal yaw steering model for the POD. About 75% of all orbital arcs calculated with the observation-based satellite attitude data result in a smaller root mean square (RMS) of residuals. More precisely, the resulting orbits show an improvement in the overall mission RMS of SLR observation residuals of 5.93% (Jason-1), 8.27% (Jason-2) and 4.51% (Jason-3) compared to the nominal attitude realization. Besides the satellite orbits, also the estimated station coordinates benefit from the refined attitude handling, that is, the station repeatability is clearly improved at the draconitic period. Moreover, altimetry analysis indicates a clear improvement of the single-satellite crossover differences (6%, 15%, and 16% reduction of the mean of absolute differences and 1.2%, 2.7%, and 1.3% of their standard deviations for Jason-1/-2/-3, respectively). On request, the preprocessed attitude data are available. [ABSTRACT FROM AUTHOR]

The location of the Earth's principal axes of inertia is a foundation for all the theories and solutions of its rotation, and thus has a broad effect on many fields, including astronomy, geodesy, and satellite-based positioning and navigation systems. That location is determined by the second-degree Stokes coefficients of the geopotential. Accurate solutions for those coefficients were limited to the stationary case for many years, but the situation improved with the accomplishment of Gravity Recovery and Climate Experiment (GRACE), and nowadays several solutions for the time-varying geopotential have been derived based on gravity and satellite laser ranging data, with time resolutions reaching one month or one week. Although those solutions are already accurate enough to compute the evolution of the Earth's axes of inertia along more than a decade, such an analysis has never been performed. In this paper, we present the first analysis of this problem, taking advantage of previous analytical derivations to simplify the computations and the estimation of the uncertainty of solutions. The results are rather striking, since the axes of inertia do not move around some mean position fixed to a given terrestrial reference frame in this period, but drift away from their initial location in a slow but clear and not negligible manner. [ABSTRACT FROM AUTHOR]

Continuous monitoring of the perpetual changing Earth is one of the key topics of present Earth System research. In the past, the satellite mission Gravity Recovery And Climate Experiment (GRACE) was measuring the time-variable gravity field of the Earth with outstanding performance in accuracy and reliability. On October 27 in 2017, GRACE de-orbited and this very successful geodetic satellite mission ended. On May 22 in 2018, the successor mission GRACE-FO (Follow On) was launched and scientific data will be provided soon to the users. Between the two satellite missions, there is a gap of more than six months. Beside the GRACE and other satellite missions, several analysis centers (ACs) worldwide process laser ranging (SLR) data to spherical satellites orbiting the Earth which allow a very accurate estimation of the low-degree spherical harmonics of the Earth's gravity field. Up to now, the available time series are stand-alone solutions which co-exist to each other.In this investigation, we will combine the SLR-derived time series of low-degree spherical harmonics from different SLR analysis centers and, moreover, combine them with the GRACE data in order to extend the continuous time series of the monthly gravity field in the GRACE/GRACE-FO gap and into the past (before the launch of GRACE in 2002). We will compare the combined SLR time series to GRACE monthly solutions and evaluate the impact of different solution setups like the SLR-orbit length, a priori models, etc. on the results. Additionally, we study the benefit of a combined SLR time series compared to the AC stand-alone solutions and motivate the advantages of such a combination in the future! [ABSTRACT FROM AUTHOR]