High-fidelity geometry models for improving the consistency of CHAMP, GRACE, GOCE and Swarm thermospheric density data sets

March, Günther; Doornbos, Eelco; Visser, Pieter

doi:10.1016/j.asr.2018.07.009

Cited by 64 publications

(68 citation statements)

References 24 publications

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“…10.1029/2019SW002356 geometry models of the satellites and by modeling the gas-surface interactions and physical drag coefficient to compute the BC March et al, 2019) instead of estimating the BC from orbital data. This approach can be applied in future work to further debias the density and BC estimates.…”

Section: Space Weathermentioning

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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Section: Space Weathermentioning

confidence: 99%

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

Gondelach

Linares

2020

Space Weather

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“…The satellite surpassed the expected mission life of 5 years and provided data for a decade until it reentered in 20 September 2010. Several data sets of total mass densities derived using CHAMP accelerometer measurements exist (e.g., March et al, ; Mehta et al, ). The twin GRACE satellites were launched in March 2002 at an initial altitude of 505 km and an inclination of 89.0° to accurately map the Earth's gravity field.…”

Section: Model‐data Fusionmentioning

confidence: 99%

“…The high accuracy mapping was achieved using the highly sensitive onboard accelerometers and satellite ranging system that allows the measurement of very small perturbations. The accelerometer data have also been used in the derivation of total mass density data sets ranging almost a decade (e.g., March et al, ; Mehta et al, ). In this work, we use the recent data set of Mehta et al () derived with the state‐of‐the‐art drag models.…”

Section: Model‐data Fusionmentioning

confidence: 99%

“…In combination with the recent efforts that have gone into improving modeling of the drag coefficient (Mehta, Walker, McLaughlin, et al, ; Mehta, Walker, Lawrence, et al, ; Walker et al, ), accelerometer‐derived densities represent our most accurate estimate of the neutral mass density in orbit. Several data sets of accelerometer‐derived densities for the CHAMP (CHAllenging Minisatellite Payload) and twin‐GRACE (Gravity Recovery And Climate Experiment) satellites exist (Bruinsma et al, ; Doornbos et al, ; Liu et al, ; March et al, ; Mehta et al, ; Sutton et al, ). In this work, we use the data sets of Mehta et al ().…”

Section: Introductionmentioning

confidence: 99%

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Data‐Driven Inference of Thermosphere Composition During Solar Minimum Conditions

2019

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Mass density variations can deviate from the expected behavior caused by temperature due to changes in the composition. Such deviations can be especially significant during solar minimum conditions. Model‐data differences are typically resolved through temperature corrections while overlooking the role of errors in lower boundary composition. In this work, we use a data‐driven methodology to simultaneously estimate thermosphere composition and temperature contributions to model‐data differences. The methodology uses modal decomposition to extract high‐dimensional, reduced order basis functions for the covariance of the neutral thermospheric species and temperature. The extracted basis functions are combined with CHAllenging Minisatellite Payload and Gravity Recovery And Climate Experiment mass density measurements using a nonlinear least squares solver. We demonstrate the methodology using the Naval Research Laboratory's empirical Mass Spectrometer and Incoherent Scatter (MSIS) model to derive the high‐dimensional basis functions. We characterize and quantify the contribution of temperature and lower boundary effects with oxygen and helium since the two species have a direct impact on drag and orbit prediction through gas‐surface interactions and mass density. We analyze the month of December in 2008, based on the work of Thayer et al. (2012), and estimate that lower boundary composition errors contribute approximately 50% of the model‐data differences.

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“…But it is less easy to estimate the error in the cross-track wind in the polar region due to considerably smaller acceleration signals. There are also systematic contributions from other sources such as gas-surface interactions, surface properties, spacecraft shape, spacecraft attitude and radiation pressure accelerations, which make the satellite aerodynamic coefficients difficult to resolve (see Doornbos et al, 2010, and the error budget in Appendix A of that paper; Mehta et al, 2017 andMarch et al, 2018). The pre-processed data of the accelerometer were re-sampled to 10-sec averages for the further use in this study.…”

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confidence: 99%

Comparing high-latitude thermospheric winds from FPI and CHAMP accelerometer measurements

Aruliah

Förster

Hood

et al. 2019

Preprint

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Abstract. It is generally assumed that horizontal wind velocities are independent of height above the F1-region (> 300 km) due to the large viscosity of the upper thermosphere. This assumption is used to compare two completely different methods of thermospheric neutral wind observation, using two distinct locations in the high-latitude Northern Hemisphere. The measurements are from ground-based Fabry-Perot Interferometers (FPI), and from in-situ accelerometer measurements onboard the CHAMP satellite, which was in a near polar orbit. The UCL KEOPS FPI is located in the vicinity of the auroral oval at the ESRANGE site near Kiruna, Sweden (67.8° N, 20.4° E). The UCL Longyearbyen FPI is a polar cap site, located at the Kjell Henriksen Observatory on Svalbard (78.1° N, 16.0° E). The comparison is done in a statistical sense, comparing a longer time series obtained during nighttime hours in the winter months (November to January); with overflights of the CHAMP satellite between 2001 and 2007 over the observational sites, within ±2° (±220 km horizontal range). The FPI is assumed to measure the Doppler shift along the line-of-sight of winds at ~ 240 km height, i.e. the peak emission height of the atomic oxygen 630.0 nm emission. The components of winds at right angles to the CHAMP orbit are derived from state-of-the-art precision accelerometer measurements at altitudes. CHAMP was at altitudes between 450 km (in 2001) to 330 km (in 2007); i.e. 100–200 km above the FPI wind observations. We show that CHAMP winds at high latitudes are systematically 1.5–2 times larger than FPI winds. In addition to testing the consistency of the different measurement approaches, the study aims to clarify the effects of viscosity on the height dependence of thermospheric winds.

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High-fidelity geometry models for improving the consistency of CHAMP, GRACE, GOCE and Swarm thermospheric density data sets

Cited by 64 publications

References 24 publications

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

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

Data‐Driven Inference of Thermosphere Composition During Solar Minimum Conditions

Comparing high-latitude thermospheric winds from FPI and CHAMP accelerometer measurements

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