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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]

A terrestrial reference frame (TRF) is a basis for precise orbit determination of Earth-orbiting satellites, since it defines positions and velocities of stations, the tracking data of which are used to derive satellite positions. In this paper, we investigate the impact of the International Terrestrial Reference Frame realization ITRF2014, as compared to its predecessor ITRF2008, on the quality of orbits, namely, on root-mean-square (rms) fits of observations and orbital arc overlaps of three altimetry satellites (TOPEX/Poseidon, Jason-1 , and Jason-2) in the time interval from August 1992 to April 2015 and on altimetry products computed using these orbits, such as single-satellite altimeter crossover differences, radial and geographically correlated mean sea surface height (SSH) errors and regional and global mean sea level trends. The satellite orbits are computed using satellite laser ranging (SLR) and Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) observations of a global network of stations. We have found that using ITRF2014 generally improves the orbit quality as compared to using ITRF2008. Thus, the mean values of the rms fits of SLR observations decreased (improved) by 2.4 % and 8.8 % for Jason-1 and Jason-2 , respectively, but are almost not impacted for TOPEX/Poseidon when using ITRF2014 instead of ITRF2008. The internal orbit consistency in the radial direction (as derived from arc overlaps) is reduced (improved) by 6.6 %, 2.3 %, and 5.9 % for TOPEX/Poseidon, Jason-1 , and Jason-2 , respectively. Single-satellite altimetry crossover analyses indicate reduction (improvement) in the absolute mean crossover differences by 0.2 mm (8.1 %) for TOPEX, 0.4 mm (17.7 %) for Jason-1 , and 0.6 mm (30.9 %) for Jason-2 with ITRF2014 instead of ITRF2008. The major improvement of the mean values of the rms of crossover differences (0.13 mm; 0.3 %) has been found for Jason-2. Multi-mission crossover analysis shows slight improvements in the standard deviations of radial errors: 0.1 %, 0.2 %, and 1.6 % for TOPEX, Jason-1 , and Jason-2 , respectively. The standard deviations of geographically correlated mean SSH errors improved by 1.1 % for Jason-1 and 5.4 % for Jason-2 and degraded by 1.3 % for TOPEX. The change from ITRF2008 to ITRF2014 orbits only has minor effects on the estimation of regional and global sea level trends over the 22-year time series from 1993 to 2015. However, on interannual timescales (3–8 years) large-scale coherent trend patterns are observed that seem to be connected to drifts between the origins of the tracking station networks. This leads to the changes in interannual global mean sea level of up to 0.06 mm yr -1 for TOPEX, 0.05 mm yr -1 for Jason-1 , and up to 0.12 mm yr -1 for Jason-2 , i.e., up to 4 % of the corresponding sea level signal based on altimetry for timescales of 3 to 8 years. The respective changes in the regional sea level trend on these timescales are up to 0.4 mm yr -1 in the time span from April 1993 to July 2008 and up to 1.0 mm yr -1 in the time span from July 2008 to April 2015. [ABSTRACT FROM AUTHOR]

A terrestrial reference frame (TRF) is a basis for precise orbit determination of Earth orbiting satellites, since it defines positions and velocities of stations tracking data of which is used to derive satellite positions. In this paper, we investigate the impact of the International Terrestrial Reference Frame realization ITRF2014, as compared to its predecessor ITRF2008, on the quality of orbits, namely, on root-mean-square (RMS) fits of observations and orbital arc overlaps of three altimetry satellites (TOPEX/Poseidon, Jason-1 and Jason-2) at the time interval from August 1992 till April 2015 and on altimetry products computed using these orbits, such as single-satellite altimeter crossover differences, radial and geographically correlated mean sea surface height (SSH) errors, regional and global mean sea level trends. The satellite orbits are computed using Satellite Laser Ranging (SLR) and Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) observations of a global network of stations. We have found that using ITRF2014 generally improves orbit quality, as compared to using ITRF2008. Thus, the mean values of the RMS fits of SLR observations decreased (improved) by 2.4 and 8.8% for Jason-1 and Jason-2, respectively, but are almost not impacted for TOPEX/Poseidon, when using ITRF2014 instead of ITRF2008. The internal orbit consistency in the radial direction (as derived from arc overlaps) is reduced (improved) by 6.6%, 2.3%, and 5.9% for TOPEX/Poseidon, Jason-1, and Jason-2, respectively. Single satellite altimetry crossover analysis indicates reduction (improvement) of the absolute mean crossover differences by 0.2mm (8.1%) for TOPEX/Poseidon, 0.4mm (17.7%) for Jason-1 and 0.6mm (30.9%) for Jason-2 with ITRF2014 instead of ITRF2008. The major improvement of the mean values of the RMS of crossover differences (0.13mm (0.3%)) has been found for Jason-2. Multi-mission crossover analysis shows slight improvements in the standard deviations of radial errors: 0.1%, 0.2% and 1.6% for TOPEX/Poseidon, Jason-1 and Jason-2, respectively. The standard deviations of geographically correlated mean SSH errors improved by 1.1% for Jason-1 and 5.4% for Jason-2 and degraded by 1.3% for TOPEX/Poseidon. The change from ITRF2008 to ITRF2014 orbits has only minor effects on the estimation of regional and global sea level trends over the 22 years time series from 1993 to 2015. However, on interannual time scales (3-8 years) large scale coherent trend patterns are observed that seem to be connected to drifts between the origins of the tracking stations networks. This leads to uncertainties of interannual global mean sea level of up to 0.06mmyr-1 for TOPEX/Poseidon, 0.05mmyr-1 for Jason-1, and up to 0.12mmyr-1 for Jason-2. The respective changes of regional sea level trend on these time scales reach maximum values of 0.4mmyr-1 for TOPEX/Poseidon, of 0.5mmyr-1 for Jason-1 and of 1.0mmyr-1 for Jason-2. [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]

In 2016, three new realizations of the International Terrestrial Reference System (ITRS), namely, DTRF2014, ITRF2014, and JTRF2014, have been released. In this paper, we evaluate these ITRS realizations for precise orbit determination of ten high and low Earth orbiting geodetic satellites using satellite laser ranging (SLR) observations. We show the reduction of observation residuals and estimated range biases, when using these new ITRS realizations, as compared with the previous ITRS realization for SLR stations—SLRF2008. Thus, the mean SLR root-mean-square (RMS) fits reduce (improve), on average over all satellites tested, by 3%, 3.6%, 8.1%, and 7.7% at 1993.0–2015.0, when using ITRF2014, DTRF2014, and DTRF2014 with non-tidal loading (NTL), and JTRF2014 realizations, respectively. The improvement of the RMS fits is even larger at 2015.0–2017.0: 14% and 15.5% using ITRF2014 and DTRF2014, respectively. For the altimetry satellite Jason-2, we found improvements in the RMS and mean of the sea surface height crossover differences with the new ITRS realizations, as compared with SLRF2008. We show that JTRF2014, after an editing done for SLR stations Conception and Zimmerwald, and DTRF2014 with NTL corrections result in the smallest RMS and absolute mean fits of SLR observations indicating the best performance among the ITRS realizations tested, while using SLRF2008 and ITRF2014 causes a 0.2–0.3 mm/y trend in the mean of SLR fits at 2001.0–2017.0. [ABSTRACT FROM AUTHOR]

Interannual to decadal sea level trends are indicators of climate variability and change. A major source of global and regional sea level data is satellite radar altimetry, which relies on precise knowledge of the satellite's orbit. Here, we assess the error budget of the radial orbit component for the TOPEX/Poseidon mission for the period 1993 to 2004 from a set of different orbit solutions. The errors for seasonal, interannual (5-year), and decadal periods are estimated on global and regional scales based on radial orbit differences from three state-of-the-art orbit solutions provided by different research teams: the German Research Centre for Geosciences (GFZ), the Groupe de Recherche de Géodésie Spatiale (GRGS), and the Goddard Space Flight Center (GSFC). The global mean sea level error related to orbit uncertainties is of the order of 1mm (8% of the global mean sea level variability) with negligible contributions on the annual and decadal timescales. In contrast, the orbitrelated error of the interannual trend is 0.1mmyr-1 (27% of the corresponding sea level variability) and might hamper the estimation of an acceleration of the global mean sea level rise. For regional scales, the gridded orbit-related error is up to 11 mm, and for about half the ocean the orbit error accounts for at least 10% of the observed sea level variability. The seasonal orbit error amounts to 10% of the observed seasonal sea level signal in the Southern Ocean. At interannual and decadal timescales, the orbit-related trend uncertainties reach regionally more than 1mmyr-1. The interannual trend errors account for 10% of the observed sea level signal in the tropical Atlantic and the south-eastern Pacific. For decadal scales, the orbit-related trend errors are prominent in a several regions including the South Atlantic, western North Atlantic, central Pacific, South Australian Basin, and the Mediterranean Sea. Based on a set of test orbits calculated at GFZ, the sources of the observed orbit-related errors are further investigated. The main contributors on all timescales are uncertainties in Earth's time-variable gravity field models and on annual to interannual timescales discrepancies of the tracking station subnetworks, i.e. satellite laser ranging (SLR) and Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS). [ABSTRACT FROM AUTHOR]

Twelve years (1991–2003) of ERS-1 and ERS-2 altimetry data have been reprocessed within the European Space Agency (ESA) reprocessing altimeter products for ERS (REAPER) project using an updated, modern set of algorithms and auxiliary models. The reprocessed data set (identified as RP01) has been cross-calibrated against the reprocessed ENVISAT V2.1 data. The format of this reprocessed data set is network common data form (version 3). The new data set shows a clear improvement in data quality beyond that of previous releases. The product validation shows reduction of the mean standard deviation of the sea-surface height differences from 8.1 (previously available product) to 6.7 cm (RP01). This paper presents the details of how the reprocessing was conducted and shows selected results from the validation and quality-assurance processes. The major improvements of the REAPER RP01 data set with respect to the previous ESA ERS radar altimetry (RA) products are due to the use of four ENVISAT RA-2 retrackers, RA calibration improvements, new reprocessed precise orbit solutions, ECMWF ERA-interim model for meteorological corrections, new ionospheric corrections, and new sea state. The intent of this paper is to aid the reader in understanding the benefits of the new data set for their particular use-case. [ABSTRACT FROM PUBLISHER]

Sea Level is a very sensitive index of climate change since it integrates the impacts of ocean warming and ice mass loss from glaciers and the ice sheets. Sea Level has been listed as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS). During the past 25 years, the sea level ECV has been measured from space by different altimetry missions that have provided global and regional observations of sea level variations. As part of the Climate Change Initiative (CCI) program of the European Space Agency (ESA) (established in 2010), the Sea Level project (SL_cci) aimed at providing an accurate and homogeneous long-term satellite-based sea level record. At the end of the first phase of the project (2010-2013), an initial version (v1.1) of the sea level ECV has been made available to users (Ablain et al., 2015). During the second phase (2014-2017), improved altimeter standards have been selected to produce new sea level products (called SL_cci v2.0) based on 9 altimeter missions for the period 1993-2015 (https://doi.org/10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612). Corresponding orbit solutions, geophysical corrections and altimeter standards used in this v2.0 dataset are described in details in Quartly et al. (2017). The present paper focuses on the description of the SL_cci v2.0 ECV and associated uncertainty and discusses how it has been validated. Various approaches have been used for the quality assessment such as internal validation, comparisons with sea level records from other groups and with in-situ measurements, sea level budget closure analyses and comparisons with model outputs. Compared to the previous version of the sea level ECV, we show that use of improved geophysical corrections, careful bias reduction between missions and inclusion of new altimeter missions lead to improved sea level products with reduced uncertainties at different spatial and temporal scales. However, there is still room for improvement since the uncertainties remain larger than the GCOS requirements. Perspectives for subsequent evolutions are also discussed. [ABSTRACT FROM AUTHOR]

Abstract. Sea level is an essential climate variable (ECV) that has a direct effect on many people through inundations of coastal areas, and it is also a clear indicator of climate changes due to external forcing factors and internal climate variability. Regional patterns of sea level change inform us on ocean circulation variations in response to natural climate modes such as El Niño and the Pacific Decadal Oscillation, and anthropogenic forcing. Comparing numerical climate models to a consistent set of observations enables us to assess the performance of these models and help us to understand and predict these phenomena, and thereby alleviate some of the environmental conditions associated with them. All such studies rely on the existence of long-term consistent high-accuracy datasets of sea level. The Climate Change Initiative (CCI) of the European Space Agency was established in 2010 to provide improved time series of some ECVs, including sea level, with the purpose of providing such data openly to all to enable the widest possible utilisation of such data. Now in its second phase, the Sea Level CCI project (SL_cci) merges data from nine different altimeter missions in a clear, consistent and well-documented manner, selecting the most appropriate satellite orbits and geophysical corrections in order to further reduce the error budget. This paper summarises the corrections required, the provenance of corrections and the evaluation of options that have been adopted for the recently released v2.0 dataset (https://doi.org/10.5270/esa-sea_level_cci-1993_2015-v_2.0-201612). This information enables scientists and other users to clearly understand which corrections have been applied and their effects on the sea level dataset. The overall result of these changes is that the rate of rise of global mean sea level (GMSL) still equates to ∼ 3.2 mm yr−1 during 1992–2015, but there is now greater confidence in this result as the errors associated with several of the corrections have been reduced. Compared with v1.1 of the SL_cci dataset, the new rate of change is 0.2 mm yr−1 less during 1993 to 2001 and 0.2 mm yr−1 higher during 2002 to 2014. Application of new correction models brought a reduction of altimeter crossover variances for most corrections. [ABSTRACT FROM AUTHOR]

New, precise, consistent orbits (VER11) of altimetry satellites ERS-1, ERS-2, TOPEX/Poseidon, Envisat, Jason-1, and Jason-2 have been recently derived at the GFZ German Research Centre for Geosciences in the extended ITRF2008 terrestrial reference frame using improved models and covering the time span 1991–2015. These orbits show improved quality, as compared with GFZ previous (VER6) orbits derived in 2013. Improved macromodels reduce root mean square (RMS) fits of satellite laser ranging (SLR) observations by 2.6%, 6.9%, and 7% for TOPEX/Poseidon, Jason-1, and Jason-2, respectively. Applying Vienna Mapping Functions 1 instead of Hopfield model for tropospheric correction of Doppler Orbitography and Radiopositioning Integrated by Satellite (DORIS) observations reduces RMS fits of SLR observations by 2%–2.4% and those of DORIS observations by 2.6% for Envisat and Jason satellites. Using satellite true attitude instead of models improves Jason-1 SLR RMS fits by 41% from July 2012 until July 2013. The VER11 orbits indicate the mean values of the SLR RMS fits between 1.2 and 2.1 cm for the different missions. The internal orbit consistency in the radial direction is between 0.5 and 1.9 cm. The global mean sea level trend for the period 1993–2014 from TOPEX, Jason-1, and Jason-2 is 2.8 and 3.0 mm/year using GFZ VER6 and VER11 orbits, respectively. Regionally, the decadal trends from GFZ VER11 and external orbits vary in the order of 1 mm/year. [ABSTRACT FROM PUBLISHER]

Interannual to decadal sea level trends are indicators of climate variability and change. A major source of global and regional sea level data is satellite radar altimetry, which relies on precise knowledge of the satellite's orbit. Here, we assess the error budget of the radial orbit component for the TOPEX/Poseidon mission for the period 1993 to 2004 from a set of different orbit solutions. Upper bound errors for seasonal, interannual (5 years), and decadal periods are estimated on global and regional scales based on radial orbit differences from three state-of-the-art orbit solutions provided by different research teams (GFZ, GSFC, and GRGS). The global mean sea level error related to the orbit is of the order of 7 mm (more than 10 % of the sea level variability) with negligible contributions on the annual and decadal time scale. In contrast, the orbit related error of the interannual trend is 0.1 mm/year (18 % of the corresponding sea level variability) and might hamper the estimation of an acceleration of the global mean sea level rise. For regional scales, the gridded orbit related error is up to 11 mm and for about half the ocean the orbit error accounts for at least 10 % of the observed sea level variability. The seasonal orbit error amounts to 10 % of the observed seasonal sea level signal in the Southern Ocean. At interannual and decadal time scales, the orbit related trend uncertainties reach regionally more than 1 mm/year. The interannual trend errors account for 10 % of the observed sea level signal in the Tropical Atlantic and the south-eastern Pacific. For decadal scales, the orbit related trend errors are prominent in a couple of regions including: South Atlantic, western North Atlantic, central Pacific, South Australian Basin, and Mediterranean Sea. Based on a set of test orbits calculated at GFZ, the sources of the observed orbit related errors are further investigated. Main contributors on all time scales are uncertainties in Earth's time variable gravity field models and on annual to interannual time scales discrepancies of the tracking station sub-networks, i.e., SLR and DORIS. [ABSTRACT FROM AUTHOR]

We have extended backwards from 2001 to 1979 the current release 05 (RL05) of the Gravity Recovery and Climate Experiment (GRACE) Atmospheric and Oceanic De-aliasing Level- 1B (AOD1B) product and studied the impact of this and a previous release 04 (RL04) of the AOD1B product on precise orbits of five altimetry satellites (ERS-1, ERS-2, TOPEX/Poseidon, Envisat and Jason-1) for the time span 1991-2012, as compared to the case when no AOD1B product is used. We have found that using AOD1B RL05 product reduces root mean square (RMS) fits of satellite laser ranging (SLR) observations by about 1.0-6.4 per cent, 2-d arc overlaps in radial, cross-track and along-track directions by about 1.3-12.0, 0.3-10.0 and 2.0-10.0 per cent, respectively, for various satellites tested, as compared to the case without AOD1B product. Using AOD1B RL05 product instead of RL04 one reduces SLR RMS fits by 0.1-0.7 per cent, 2-d arc overlaps in radial, cross-track and along-track directions by 0.1-0.6, 0.1-1.3 and 0.2-1.2 per cent, respectively, for the satellite orbits tested. The multi-mission crossover analysis shows that the application of an AOD1B product reduces the scatter of radial errors by 0.4-2.8 per cent for the satellite missions studied. At the regions with the most pronounced changes the use of the AOD1B products improves the consistency between the sea level as measured by the TOPEX and ERS-2 missions and by the Jason-1 and Envisat missions by 5 to 10 per cent (globally by about 2 per cent). The results of our study show that extended AOD1B RL05 product performs better than the AOD1B RL04 and improves orbits of altimetry satellites and consistency of sea level products.q [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]