New density estimates derived using accelerometers on board the CHAMP and GRACE satellites

Mehta, Piyush M.; Walker, Andrew; Sutton, E. K.; Godinez, H. C.

doi:10.1002/2016sw001562

Cited by 102 publications

(168 citation statements)

References 26 publications

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“…During high solar activity the stability of the calibration parameters increases due to the intensity of the non-gravitational forces measured by the accelerometer as stated by . A validation of densities derived in this study is possible when taking daily mean densities by Sutton (2008) and Mehta et al (2017) in November 2008 into account. Relative to Sutton (2008), the root mean square difference (RMSD) of the approaches used in this study are 3.7 × 10 −14 kg m −3 (MME), 2.4 × 10 −14 kg m −3 (DE) and 2.2 × 10 −13 kg m −3 (EMA), whereas the RMSD relative to Mehta et al (2017) are 5.3 × 10 −13 kg m −3 (MME), 1.8 × 3 http://tinyurl.com/densitysets; last access: 16 May 2018 10 −13 kg m −3 (DE) and 3.8 × 10 −13 kg m −3 (EMA).…”

Section: Thermospheric Neutral Density Estimationmentioning

confidence: 90%

“…A validation of densities derived in this study is possible when taking daily mean densities by Sutton (2008) and Mehta et al (2017) in November 2008 into account. Relative to Sutton (2008), the root mean square difference (RMSD) of the approaches used in this study are 3.7 × 10 −14 kg m −3 (MME), 2.4 × 10 −14 kg m −3 (DE) and 2.2 × 10 −13 kg m −3 (EMA), whereas the RMSD relative to Mehta et al (2017) are 5.3 × 10 −13 kg m −3 (MME), 1.8 × 3 http://tinyurl.com/densitysets; last access: 16 May 2018 10 −13 kg m −3 (DE) and 3.8 × 10 −13 kg m −3 (EMA). During this period, the DE approach is found to be the most suitable method to calibrate accelerometer measurements in order to derive thermospheric neutral densities.…”

Section: Thermospheric Neutral Density Estimationmentioning

confidence: 90%

“…Moreover, it is required to know the satellite's mass m, its areas A projected onto flight direction and the relative velocity v rel modeled by the satellite's initial velocity with respect to the velocity of the atmosphere, which is assumed to rotate with the Earth. Atmospheric winds are neglected as they have only a minor impact on the satellite's velocity and the estimated thermospheric densities (Sutton, 2008;Mehta et al, 2017). Unlike the calibration techniques of Sect.…”

Section: Atmospheric Dragmentioning

confidence: 99%

“…In addition, the derived densities are compared to two different empirical models NRLMSISE-00 (Picone et al, 2002) and Jacchia-Bowman 2008(Bowman et al, 2008. For evaluating the accelerometer-based densities resulting from this study, density sets by Sutton (2008), and more recently estimated densities from Mehta et al (2017), both available between 2002 and 2010, are taken into account 3 . Throughout this study, the densities are not normalized to a constant altitude, which is done for example in Lei et al (2012), since such a normalization needs an assumption about vertical changes in the thermospheric density.…”

Section: Thermospheric Neutral Density Estimationmentioning

confidence: 99%

“…These measurements reflect accelerations due to the atmospheric drag (the dominant component of the acceleration vector at the orbital altitude of these satellites), and thus enable studies of thermospheric neutral density and winds (e.g., Bruinsma et al, 2004;Liu et al, 2005;Sutton et al, 2007;Doornbos, 2012;Lei et al, 2012;Mehta et al, 2017). Non-gravitational forces also contain the effect of the solar and Earth radiation.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of accelerometer data calibration methods used in thermospheric neutral density estimation

et al. 2018

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Abstract. Ultra-sensitive space-borne accelerometers on board of low Earth orbit (LEO) satellites are used to measure non-gravitational forces acting on the surface of these satellites. These forces consist of the Earth radiation pressure, the solar radiation pressure and the atmospheric drag, where the first two are caused by the radiation emitted from the Earth and the Sun, respectively, and the latter is related to the thermospheric density. On-board accelerometer measurements contain systematic errors, which need to be mitigated by applying a calibration before their use in gravity recovery or thermospheric neutral density estimations. Therefore, we improve, apply and compare three calibration procedures: (1) a multi-step numerical estimation approach, which is based on the numerical differentiation of the kinematic orbits of LEO satellites; (2) a calibration of accelerometer observations within the dynamic precise orbit determination procedure and (3) a comparison of observed to modeled forces acting on the surface of LEO satellites. Here, accelerometer measurements obtained by the Gravity Recovery And Climate Experiment (GRACE) are used. Time series of bias and scale factor derived from the three calibration procedures are found to be different in timescales of a few days to months. Results are more similar (statistically significant) when considering longer timescales, from which the results of approach (1) and (2) show better agreement to those of approach (3) during medium and high solar activity. Calibrated accelerometer observations are then applied to estimate thermospheric neutral densities. Differences between accelerometer-based density estimations and those from empirical neutral density models, e.g., NRLMSISE-00, are observed to be significant during quiet periods, on average 22 % of the simulated densities (during low solar activity), and up to 28 % during high solar activity. Therefore, daily corrections are estimated for neutral densities derived from NRLMSISE-00. Our results indicate that these corrections improve model-based density simulations in order to provide density estimates at locations outside the vicinity of the GRACE satellites, in particular during the period of high solar/magnetic activity, e.g., during the St. Patrick's Day storm on 17 March 2015.

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Section: Thermospheric Neutral Density Estimationmentioning

confidence: 90%

Section: Thermospheric Neutral Density Estimationmentioning

confidence: 90%

Section: Atmospheric Dragmentioning

confidence: 99%

Section: Thermospheric Neutral Density Estimationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of accelerometer data calibration methods used in thermospheric neutral density estimation

et al. 2018

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High‐Latitude Neutral Mass Density Maxima

Huang

et al. 2017

JGR Space Physics

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Recent studies have reported that thermospheric effects due to solar wind driving can be observed poleward of auroral latitudes. In these papers, the measured neutral mass density perturbations appear as narrow, localized maxima in the cusp and polar cap. They conclude that Joule heating below the spacecraft is the cause of the mass density increases, which are sometimes associated with local field‐aligned current structures, but not always. In this paper we investigate neutral mass densities measured by accelerometers on the CHAllenging Minisatellite Payload (CHAMP) and Gravity Recovery and Climate Experiment (GRACE) spacecraft from launch until years 2010 (CHAMP) and 2012 (GRACE), approximately 10 years of observations from each satellite. We extract local maxima in neutral mass densities over the background using a smoothing window with size of one quarter of the orbit. The maxima have been analyzed for each year and also for the duration of each set of satellite observations. We show where they occur, under what solar wind conditions, and their relation to magnetic activity. The region with the highest frequency of occurrence coincides approximately with the cusp and mantle, with little direct evidence of an auroral zone source. Our conclusions agree with the “hot polar cap” observations that have been reported and studied in the past.

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A methodology for reduced order modeling and calibration of the upper atmosphere

Mehta

Linares

2017

Space Weather

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Atmospheric drag is the largest source of uncertainty in accurately predicting the orbit of satellites in low Earth orbit (LEO). Accurately predicting drag for objects that traverse LEO is critical to space situational awareness. Atmospheric models used for orbital drag calculations can be characterized either as empirical or physics‐based (first principles based). Empirical models are fast to evaluate but offer limited real‐time predictive/forecasting ability, while physics based models offer greater predictive/forecasting ability but require dedicated parallel computational resources. Also, calibration with accurate data is required for either type of models. This paper presents a new methodology based on proper orthogonal decomposition toward development of a quasi‐physical, predictive, reduced order model that combines the speed of empirical and the predictive/forecasting capabilities of physics‐based models. The methodology is developed to reduce the high dimensionality of physics‐based models while maintaining its capabilities. We develop the methodology using the Naval Research Lab's Mass Spectrometer Incoherent Scatter model and show that the diurnal and seasonal variations can be captured using a small number of modes and parameters. We also present calibration of the reduced order model using the CHAMP and GRACE accelerometer‐derived densities. Results show that the method performs well for modeling and calibration of the upper atmosphere.

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New density estimates derived using accelerometers on board the CHAMP and GRACE satellites

Cited by 102 publications

References 26 publications

Comparison of accelerometer data calibration methods used in thermospheric neutral density estimation

Comparison of accelerometer data calibration methods used in thermospheric neutral density estimation

High‐Latitude Neutral Mass Density Maxima

A methodology for reduced order modeling and calibration of the upper atmosphere

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