Towards thermospheric density estimation from SLR observations of LEO satellites: a case study with ANDE-Pollux satellite

Panzetta, F; Bloßfeld, M; Erdogan, Eren; Rudenko, Sergei; Schmidt, Michaël; Müller, Hendrik

doi:10.1007/s00190-018-1165-8

Cited by 13 publications

(20 citation statements)

References 38 publications

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“…This is not an easy task since, on the one hand, it requires precise modeling of all other gravitational and nongravitational perturbations acting on the satellites, and on the other hand, the amount of SLR observations contributing globally to LEO satellites observations is low. However, the derived density values can either be used to validate empirical models locally or provide scaling factors for these models (Panzetta et al, 2018).…”

Section: Density Validations By Slr Measurements To Calibration Satelmentioning

confidence: 99%

“…In addition we also compared the SLR-derived densities with four different empirical models: CIRA86 (Hedin et al, 1988), NRLMSISE-00 (Picone et al, 2002), DTM2013 (Bruinsma, 2015) and JB2008 (Bowman et al, 2008b). The corresponding mean values of the estimated scaling factors are 0.65 ± 0.26 for CIRA86, 0.65 ± 0.25 for NRLMSISE-00, 0.79 ± 0.24 for DTM2013 and 0.89 ± 0.27 for JB2008, respectively.…”

Section: Density Validations By Slr Measurements To Calibration Satelmentioning

confidence: 99%

“…Various empirical models have also been developed to describe the thermospheric mass density variability. The most widely used are the Mass Spectrometer Incoherent Scatter Radar Extended (MSISE) model family (Hedin, 1991;Picone et al, 2002), the Drag Temperature Model (Bruinsma et al, 2003(Bruinsma et al, , 2012 and the Jacchia-Bowman 2008 (JB2008) model series (Bowman et al, 2008a, b). Liu et al (2013) and Yamazaki et al (2015) reported two empirical models derived from recent LEO missions, such as the CHAllenging Minisatellite Payload (CHAMP, Reigber et al, 2002) and the Gravity Recovery and Climate Experiment (GRACE, Tapley et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

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An empirical model of the thermospheric mass density derived from CHAMP satellite

et al. 2018

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Abstract. 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 Mini-satellite 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 CH-Therm-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.

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Section: Density Validations By Slr Measurements To Calibration Satelmentioning

confidence: 99%

Section: Density Validations By Slr Measurements To Calibration Satelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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An empirical model of the thermospheric mass density derived from CHAMP satellite

et al. 2018

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“…Accurate modeling of these forces is required for precise orbit determination of GNSS satellites (Fliegel et al 1992;Rodríguez-Solano et al 2012;Arnold et al 2015;Steigenberger et al 2015;Darugna et al 2018;Bury et al 2019 (Sośnica et al 2014;Panzetta et al 2018), and several Earth observation satellites such as Swarm ), radar altimeter and radar imaging satellites Peter et al 2017;Hackel et al 2017). LEO gravity field recovery missions, e.g., Gravity Recovery and Climate Experiment (GRACE; Tapley et al 2004), CHAllenging Minisatellite Payload (CHAMP; Reigber et al 2002) or ESA's Gravity field and steady-state Ocean Circulation Explorer (GOCE; Floberghagen et al 2011), carry space-borne accelerometers to remove non-gravitational forces from measured orbit perturbations.…”

Section: Introductionmentioning

confidence: 99%

“…In this context, also the anisotropic reflection at the Earth's surface had been studied (Rubincam et al 1987). Since the material properties of these spherical satellites represent the largest uncertainty in analytical force modeling for SLR satellites, a radiation pressure coefficient or a scaling factor is commonly estimated within a precise orbit determination (POD;Bloßfeld et al 2018;Panzetta et al 2018;Hattori and Otsubo 2019).…”

Section: Introductionmentioning

confidence: 99%

Extended forward and inverse modeling of radiation pressure accelerations for LEO satellites

Vielberg

Kusche

2020

J Geod

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For low Earth orbit (LEO) satellites, activities such as precise orbit determination, gravity field retrieval, and thermospheric density estimation from accelerometry require modeled accelerations due to radiation pressure. To overcome inconsistencies and better understand the propagation of modeling errors into estimates, we here suggest to extend the standard analytical LEO radiation pressure model with emphasis on removing systematic errors in time-dependent radiation data products for the Sun and the Earth. Our extended unified model of Earth radiation pressure accelerations is based on hourly CERES SYN1deg data of the Earth's outgoing radiation combined with angular distribution models. We apply this approach to the GRACE (Gravity Recovery and Climate Experiment) data. Validations with 1 year of calibrated accelerometer measurements suggest that the proposed model extension reduces RMS fits between 5 and 27%, depending on how measurements were calibrated. In contrast, we find little changes when implementing, e.g., thermal reradiation or anisotropic reflection at the satellite's surface. The refined model can be adopted to any satellite, but insufficient knowledge of geometry and in particular surface properties remains a limitation. In an inverse approach, we therefore parametrize various combinations of possible systematic errors to investigate estimability and understand correlations of remaining inconsistencies. Using GRACE-A accelerometry data, we solve for corrections of material coefficients and CERES fluxes separately over ocean and land. These results are encouraging and suggest that certain physical radiation pressure model parameters could indeed be determined from satellite accelerometry data.

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Upper-atmosphere mass density variations from CASSIOPE precise orbits

Calabia

Jin

2020

Preprint

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Low Earth Orbit (LEO) satellites are significantly affected by variable air drag forces, which are mainly altered by atmospheric expansion/contraction driven by solar and geomagnetic activity. Air drag reduces the orbital velocity of a satellite, its nominal altitude, and shortens its lifespan. The effect of air drag pressure on the position of a satellite orbiting at an altitude of around 450 km may drag around 3 m per revolution in the along-track axis, limiting the satellite's lifespan to approximately 5-10 years. In applications, such as remote sensing or satellite altimetry and gravity, the orbital trajectory and velocity (ephemeris) of satellites must be known to an accuracy of a few millimeters. Moreover, the exponential increase in presence of space debris (consider the recent destructive events of Fengyun-1C, Iridium, and Mission Shakti) has highlighted the importance of orbital tracking and prediction of potential collisions. The dynamic Precise Orbit Determination (POD) method tracks and predicts the orbital ephemeris by calculating an orbital trajectory through a double integration and linearization of Newton-Euler's equation of motion (Montenbruck & Gill, 2013). In the POD method, by combining force models with empirical observations, used for example in laser-ranging, Doppler, accelerometer, or Global Navigation Satellite System (GNSS) measurements, the position, and velocity of a satellite can be stochastically estimated with significant accuracy (Jin & Su, 2020;Tapley et al., 2004).Due to variable air drag force being so important, in the last decade, thermospheric mass density (TMD) variations driven by solar and geomagnetic activity have been investigated using satellite technology to a great extent (e.g.

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Towards thermospheric density estimation from SLR observations of LEO satellites: a case study with ANDE-Pollux satellite

Cited by 13 publications

References 38 publications

An empirical model of the thermospheric mass density derived from CHAMP satellite

An empirical model of the thermospheric mass density derived from CHAMP satellite

Extended forward and inverse modeling of radiation pressure accelerations for LEO satellites

Upper-atmosphere mass density variations from CASSIOPE precise orbits

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