Review and comparison of empirical thermospheric mass density models

Hé, Changyong; Yang, Yang; Carter, Brett; Kerr, Emma; Wu, Suqin; Deleflie, F.; Cai, Han; Zhang, Kefei; Sagnières, Luc; Norman, Robert

doi:10.1016/j.paerosci.2018.10.003

Cited by 38 publications

(41 citation statements)

References 74 publications

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“…The JB2008 model computes thermosphere neutral mass density from a single parameter, the local exosphere temperature, here represented by T ∞ . This temperature is represented by

T_{\infty} = T_{ℓ} false(θ, δ_{⊙}, τ false) + Δ T_{LST} false(τ, θ, h false) + T_{UV} false(χ false) + T_{MA} false(D s t false),

where T ℓ corresponds to an empirical formula for the local exospheric temperature as a function of latitude θ , solar declination δ ⊙ , and local time τ ; Δ T LST is an altitude‐dependent ( h ) local solar time correction; T UV is the solar contribution dependent on solar extreme UV and far UV irradiance as a function of solar indices χ including X‐ray and Lyman‐ α wavelengths (see review by He et al., ); and T MA is a global correction resulting from magnetic activity and computed as a function of the Dst index according to an empirical formula provided by Burke et al. ().…”

Section: Data Model and Orbital Drag Computationsmentioning

confidence: 99%

“…( 2) where T ℓ corresponds to an empirical formula for the local exospheric temperature as a function of latitude θ, solar declination δ ⊙ , and local time τ; ΔT LST is an altitude-dependent (h) local solar time correction; T UV is the solar contribution dependent on solar extreme UV and far UV irradiance as a function of solar indices χ including X-ray and Lyman-α wavelengths (see review by He et al, 2018); and T MA is a global correction resulting from magnetic activity and computed as a function of the Dst index according to an empirical formula provided by Burke et al (2009).…”

Section: 1029/2019sw002287mentioning

confidence: 99%

“…This pioneer observation sparked the interest of many scientists in the following decades in producing empirical models to estimate densities in the thermosphere (Bruinsma, 2015;Bruinsma et al, 2018;Emmert, 2015;Jacchia, 1970;King-Hele, 1987;Mayr et al, 1990;Mehta et al, 2018;Picone et al, 2002;Prölss, 2011;Storz et al, 2005;Sutton, 2018;Weng et al, 2017;Yamazaki et al, 2015). He et al (2018) have provided a review on the comparison between empirical thermosphere neutral mass density models commonly used in the past decades.…”

Section: Introductionmentioning

confidence: 99%

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Satellite Orbital Drag During Magnetic Storms

Oliveira

Zesta

2019

Space Weather

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We investigate satellite orbital drag effects at low‐Earth orbit associated with thermosphere heating during magnetic storms caused by coronal mass ejections. CHAllenge Mini‐satellite Payload (CHAMP) and Gravity Recovery And Climate Experiment (GRACE) neutral density data are used to compute orbital drag. Storm‐to‐quiet density comparisons are performed with background densities obtained by the Jacchia‐Bowman 2008 (JB2008) empirical model. Our storms are grouped in different categories regarding their intensities as indicated by minimum values of the SYM‐H index. We then perform superposed epoch analyses with storm main phase onset as zero epoch time. In general, we find that orbital drag effects are larger for CHAMP (lower altitudes) in comparison to GRACE (higher altitudes). Results show that storm time drag effects manifest first at high latitudes, but for extreme storms, particularly observed by GRACE, stronger orbital drag effects occur during early main phase at low/equatorial latitudes, probably due to heating propagation from high latitudes. We find that storm time orbital decay along the satellites' path generally increases with storm intensity, being stronger and faster for the most extreme events. For these events, orbital drag effects decrease faster probably due to elevated cooling effects caused by nitric oxide, which introduce modeled density uncertainties during storm recovery phase. Errors associated with total orbit decay introduced by JB2008 are generally the largest for the strongest storms and increase during storm times, particular during recovery phases. We discuss the implication of these uncertainties for the prediction of collision between space objects at low‐Earth orbit during magnetic storms.

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“…The JB2008 model computes thermosphere neutral mass density from a single parameter, the local exosphere temperature, here represented by T ∞ . This temperature is represented by

T_{\infty} = T_{ℓ} false(θ, δ_{⊙}, τ false) + Δ T_{LST} false(τ, θ, h false) + T_{UV} false(χ false) + T_{MA} false(D s t false),

Section: Data Model and Orbital Drag Computationsmentioning

confidence: 99%

Section: 1029/2019sw002287mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Satellite Orbital Drag During Magnetic Storms

Oliveira

Zesta

2019

Space Weather

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“…During solar maximum, 3-D orbit difference for latitudinal variation is nearly 70 m. Note that the longitudinal variation due to the movement of subsolar point has been removed by using the same local solar time in DTM2013. The degree of the spherical harmonics used by DTM2013 is less than 6 (incomplete terms of the Legendre functions were adopted), and therefore, the impact of the horizontal variations shown here only include the horizontal variations with the wavelength larger than 30 • (He et al, 2018). Table 4 presents the orbit difference in radial, along-track, and cross-track directions for a 1-day OP simulation for different seasons.…”

Section: Large-scale Spatial Impactmentioning

confidence: 99%

Impact of Thermospheric Mass Density on the Orbit Prediction of LEO Satellites

et al. 2020

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Many thermospheric mass density (TMD) variations have been recognized in observationsand physical simulations; however, their impact on the low-Earth-orbit satellites has not been fully evaluated. The present study investigates the quantitative impact of periodic spatiotemporal TMD variations modulated by the empirical DTM2013 model. Also considered are two small-scale variations, that is, the equatorial mass anomaly and the midnight density maximum, which are reproduced by the Thermosphere-Ionosphere-Electrodynamics General Circulation Model. This investigation is performed through a 1-day orbit prediction (OP) simulation for a 400-km circular orbit. The results show that the impact of TMD variations during solar maximum is 1 order of magnitude larger than that during solar minimum. The dominant impact has been found in the along-track direction. Semiannual and semidiurnal variations in TMD exert the most significant impact on OP among the intra-annual and intradiurnal variations, respectively. The zero mean periodic variations in TMD may not significantly affect the predicted orbit but increase the orbital uncertainty if their periods are shorter than the time span of OP. Additionally, the equatorial mass anomaly creates a mean orbit difference of 50 m (5 m) with a standard deviation of 30 m (3 m) in 1-day OP during high (low) solar activity. The midnight density maximum exhibits a stronger impact in the order of 150 ± 30 and 15 ± 6 m during solar maximum and solar minimum, respectively. This study makes clear that careful selection of TMD variations is of great importance to balance the trade-off between efficiency and accuracy in OP problems.where ⃗ v is the velocity of the satellite and ⃗ v r is the relative velocity of the satellite with respect to the wind ⃗ v w . In the orbit prediction (OP) simulation performed in this study, these equations are evaluated in the Earth-centered inertial frame.Empirical thermospheric mass density (TMD) models can be used for atmospheric drag calculations, for example, NRLMSISE-00 (Naval Research Laboratory Mass Spectrometer Incoherent Scatter Radar Series) (Picone et al., 2002), JB2008 (Jacchia-Bowman) (Bowman et al., 2008), and DTM2013 (Drag Temperature Model) (Bruinsma, 2015). Accurate TMD prediction is critical in the tracking and collision avoidance of LEO satellites. The error of empirical TMD models is one of the largest sources of uncertainty in OP for LEO

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“…A major advantage of empirical models is that they are fast to evaluate, making them ideal for drag and orbit computations. The accuracy of these empirical models is however limited (He et al, 2018), especially during space weather events. Improved densities Recently, a new methodology for modeling and estimating the thermosphere using reduced-order modeling was developed by Mehta and Linares (2017) to overcome the high-dimensionality problem of physics-based models.…”

Section: Introductionmentioning

confidence: 99%

Real‐Time Thermospheric Density Estimation via Two‐Line Element Data Assimilation

Gondelach

Linares

2020

Space Weather

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Inaccurate estimates of the thermospheric density are a major source of error in low Earth orbit prediction. Therefore, real‐time density estimation is required to improve orbit prediction. In this work, we develop a dynamic reduced‐order model for the thermospheric density that enables real‐time density estimation using two‐line element (TLE) data. For this, the global thermospheric density is represented by the main spatial modes of the atmosphere and a time‐varying low‐dimensional state and a linear model is derived for the dynamics. Three different models are developed based on density data from the TIE‐GCM, NRLMSISE‐00, and JB2008 thermosphere models and are valid from 100 to maximum 800 km altitude. Using the models and TLE data, the global density is estimated by simultaneously estimating the density and the orbits and ballistic coefficients of several objects using a Kalman filter. The sequential estimation provides both estimates of the density and corresponding uncertainty. Accurate density estimation using the TLEs of 17 objects is demonstrated and validated against CHAMP and GRACE accelerometer‐derived densities. The estimated densities are shown to be significantly more accurate and less biased than NRLMSISE‐00 and JB2008 modeled densities. The uncertainty in the density estimates is quantified and shown to be dependent on the geographical location, solar activity, and objects used for estimation. In addition, the data assimilation capability of the model is highlighted by assimilating CHAMP accelerometer‐derived density data together with TLE data to obtain more accurate global density estimates. Finally, the dynamic thermosphere model is used to forecast the density.

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Review and comparison of empirical thermospheric mass density models

Cited by 38 publications

References 74 publications

Satellite Orbital Drag During Magnetic Storms

Satellite Orbital Drag During Magnetic Storms

Impact of Thermospheric Mass Density on the Orbit Prediction of LEO Satellites

Real‐Time Thermospheric Density Estimation via Two‐Line Element Data Assimilation

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