Reentry Time Prediction Using Atmospheric Density Corrections

Yurasov, Vasiliy S.; Назаренко, А. И.; Alfriend, Kyle T.; Cefola, P. J.

doi:10.2514/1.26593

Cited by 22 publications

(8 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The atmospheric experience model is an important means of obtaining neutral density. Atmospheric experience models are indispensable in science and engineering and are widely used in data analysis and engineering design [6][7][8][9][10]. The MSIS series, CIRA series, and Jacchia series are commonly used empirical models of the atmosphere.…”

Section: Introductionmentioning

confidence: 99%

Density Correction of NRLMSISE-00 in the Middle Atmosphere (20–100 km) Based on TIMED/SABER Density Data

et al. 2020

View full text Add to dashboard Cite

This paper describes the density correction of the NRLMSISE-00 using more than 15 years (2002–2016) of TIMED/SABER satellite atmospheric density data from the middle atmosphere (20–100 km). A bias correction factor dataset is established based on the density differences between the TIMED/SABER data and NRLMSISE-00. Seven height nodes are set in the range between 20 and 100 km. The different scale oscillations of the correction factor are separated at each height node, and the spherical harmonic function is used to fit the coefficients of the different timescale oscillations to obtain a spatiotemporal function at each height node. Cubic spline interpolation is used to obtain the correction factor at other non-node heights. The spatiotemporal correction function depends on six key parameters, including height, latitude, longitude, local time, day, and year. The evaluation results show that the spatiotemporal correction function proposed in this paper achieves a good correction effect on the atmospheric density of NRLMSISE-00. The correction effect becomes more pronounced as the height increases. After correction, the relative error of the model decreased by 40%–50% in July, especially at ±40° N in the 80–100 km region. The correction effect of the spatiotemporal correction function under different geomagnetic activity may have some potential relationships with geomagnetic activities. During geomagnetic storms, the relative errors in atmospheric density at 100, 70, and 32 km decrease from 41.21%, 22.09%, and 3.03% to −9.65%, 2.60%, and 1.44%, respectively, after correction. The relative errors in atmospheric density at 100, 70, and 32 km decrease from 68.95%, 21.02%, and 3.56% to 3.49%, 2.20%, and 1.77%, respectively, during the geomagnetic quiet period. The correction effect during the geomagnetic quiet period is better than that during geomagnetic storms at a height of 100 km. The subsequent effects of geomagnetic activity will be considered, and the atmospheric density during magnetic storms and quiet periods will be corrected separately near 100 km. The ability of the model to characterize the mid-atmosphere (20–100 km) is significantly improved compared with the pre-correction performance. As a result, the corrected NRLMSISE-00 can provide more reliable atmospheric density data for scientific researches and engineering fields, such as data analysis, instrument design, and aerospace vehicles.

show abstract

Section: Introductionmentioning

confidence: 99%

Density Correction of NRLMSISE-00 in the Middle Atmosphere (20–100 km) Based on TIMED/SABER Density Data

et al. 2020

View full text Add to dashboard Cite

show abstract

“…The first category is dynamic calibration of the atmosphere (DCA) and the second is using accelerometers onboard satellites to measure non-conservative accelerations, which includes drag. DCA utilizes the observed motions of a large number of satellites in order to estimate large-scale density corrections to an existing atmospheric density model (Bowman et al, 2007;Bowman, 2004;Cefola et al, 2003;Storz et al, 2005;Wilkins et al, 2007aWilkins et al, , 2007bYurasov et al, 2004;Yurasov et al, 2008). Dynamic calibration of the atmosphere provides a significant improvement to empirical density models but with several disadvantages.…”

Section: Introductionmentioning

confidence: 99%

Thermosphere Density Variability, Drag Coefficients, and Precision Satellite Orbits

McLaughlin¹,

Krishna²,

Mehta³

et al. 2013

View full text Add to dashboard Cite

The public reporting burden for this collection of information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this collection of information, including suggestions for reducing the burden, to the Department of Defense, Executive Services and Communications Directorate (0704-0188). Respondents should be aware that notwithstanding any other provision of law, no person shall be subject to any penalty for failing to comply with a collection of information if it does not display a currently valid OMB control number.

show abstract

“…[], Yurasov et al . [, ], and Wilkins et al . [, ] are examples of the DCA approach of making large‐scale corrections to existing atmospheric density models.…”

Section: Introductionmentioning

confidence: 99%

Thermospheric density variations: Observability using precision satellite orbits and effects on orbit propagation

et al. 2013

View full text Add to dashboard Cite

[1] This paper examines atmospheric density estimated using precision orbit ephemerides (POE) from the CHAMP and GRACE satellites during short periods of greater atmospheric density variability. The results of the calibration of CHAMP densities derived using POEs with those derived using accelerometers are examined for three different types of density perturbations, [traveling atmospheric disturbances (TADs), geomagnetic cusp phenomena, and midnight density maxima] in order to determine the temporal resolution of POE solutions. In addition, the densities are compared to High-Accuracy Satellite Drag Model (HASDM) densities to compare temporal resolution for both types of corrections. The resolution for these models of thermospheric density was found to be inadequate to sufficiently characterize the short-term density variations examined here. Also examined in this paper is the effect of differing density estimation schemes by propagating an initial orbit state forward in time and examining induced errors. The propagated POE-derived densities incurred errors of a smaller magnitude than the empirical models and errors on the same scale or better than those incurred using the HASDM model.

show abstract

Reentry Time Prediction Using Atmospheric Density Corrections

Cited by 22 publications

References 3 publications

Density Correction of NRLMSISE-00 in the Middle Atmosphere (20–100 km) Based on TIMED/SABER Density Data

Density Correction of NRLMSISE-00 in the Middle Atmosphere (20–100 km) Based on TIMED/SABER Density Data

Thermosphere Density Variability, Drag Coefficients, and Precision Satellite Orbits

Thermospheric density variations: Observability using precision satellite orbits and effects on orbit propagation

Contact Info

Product

Resources

About