Thermosphere Density Variability, Drag Coefficients, and Precision Satellite Orbits

McLaughlin, Craig; Krishna, Dhaval Mysore; Mehta, Piyush M.; Lechtenberg, Travis; Hiatt, Andrew; Fattig, E.; Locke, Travis

doi:10.21236/ada582025

Cited by 2 publications

(2 citation statements)

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“…The second example, given in Figure , compares CHAMP and GRACE‐A accelerometer‐derived densities during 26–27 September 2007 with the above mentioned two different geomagnetic activity input methods. The comparison presented in Figure is similar to the CHAMP and GRACE‐A comparison presented in Figure 5 in McLaughlin et al (). The model‐data comparison is performed at the original data resolution.…”

Section: Discussionsupporting

confidence: 83%

“…Therefore, the different methods of driving NRLMSISE-00 may especially impact the results at high latitudes during high geomagnetic activity. To further elucidate the effects of driving NRLMSISE-00 with the daily average of 3-hr Ap or the full required history of Ap, results from two previous studies are consulted, Elvidge et al (2016) and McLaughlin et al (2013), and reported in Appendix B.…”

Section: Discussionmentioning

confidence: 99%

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The First Comparison Between Swarm‐C Accelerometer‐Derived Thermospheric Densities and Physical and Empirical Model Estimates

2018

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The first systematic comparison between Swarm-C accelerometer-derived thermospheric density and both empirical and physics-based model results using multiple model performance metrics is presented. This comparison is performed at the satellite's high temporal 10-s resolution, which provides a meaningful evaluation of the models' fidelity for orbit prediction and other space weather forecasting applications. The comparison against the physical model is influenced by the specification of the lower atmospheric forcing, the high-latitude ionospheric plasma convection, and solar activity. Some insights into the model response to thermosphere-driving mechanisms are obtained through a machine learning exercise. The results of this analysis show that the short-timescale variations observed by Swarm-C during periods of high solar and geomagnetic activity were better captured by the physics-based model than the empirical models. It is concluded that Swarm-C data agree well with the climatologies inherent within the models and are, therefore, a useful data set for further model validation and scientific research. Plain Language SummaryThe first systematic comparison of the thermospheric densities between Swarm-C accelerometer-derived densities is presented. The data are compared to two latest versions of a physics-based general circulation model and arguably the most widely used empirical models using multiple model performance metrics. The comparison at the satellite's temporal resolution provides a useful evaluation of the models' fidelity for orbit prediction and pertinent space weather forecasting applications. The results of this study show that the short-timescale variations observed by Swarm-C during periods of high solar and geomagnetic activities were better captured by the physics-based model than the empirical model. The results show the complex interconnectedness of solar activity and geomagnetic activity on model performance in terms of estimating density. The study also uses a machine learning exercise to demonstrate characteristics inherent to each model run. Swarm-C data are consistent with the models. It is concluded that Swarm-C data are suitable for further model validation and scientific research.

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Section: Discussionsupporting

confidence: 83%

Section: Discussionmentioning

confidence: 99%

The First Comparison Between Swarm‐C Accelerometer‐Derived Thermospheric Densities and Physical and Empirical Model Estimates

2018

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A simultaneous calibration and data assimilation (C/DA) to improve NRLMSISE00 using thermospheric neutral density (TND) from space-borne accelerometer measurements

Forootan

Farzaneh

Kosary

et al. 2020

Geophysical Journal International

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Summary Improving Thermospheric Neutral Density (TND) estimates is important for computing drag forces acting on Low-Earth-Orbit (LEO) satellites and debris. Empirical thermospheric models are often used to compute TNDs for the precise orbit determination experiments. However, it is known that simulating TNDs are of limited accuracy due to simplification of model structure, coarse sampling of model inputs, and dependencies to the calibration period. Here, we apply TND estimates from accelerometer measurements of the Challenging Minisatellite Payload (CHAMP) and the Gravity Recovery and Climate Experiment (GRACE) missions as observations to improve the NRLMSISE-00 model, which belongs to the Mass Spectrometer and Incoherent Scatter (MSIS) family of models. For this, a novel simultaneous Calibration and Data Assimilation (C/DA) technique is implemented that uses the Ensemble Kalman Filter (EnKF) and the Ensemble Square-Root Kalman Filter (EnSRF) as merger. The application of C/DA is unique because it modifies both model-derived TNDs, as well as the selected model parameters. The calibrated parameters derived from C/DA are then used to predict TNDs in locations that are not covered by CHAMP and GRACE orbits, and forecasting TNDs of the next day. The C/DA is implemented using daily CHAMP- and/or GRACE-TNDs, for which compared to the original model, we find 27% and 62% reduction of misfit between model and observations in terms of Root Mean Square Error (RMSE) and Nash Coefficient, respectively. These validations are performed using the observations along the orbital track of the other satellite that is not used in the C/DA during 2003 with various solar activity. Comparisons with another empirical model, i.e., Jacchia-Bowman 2008, indicate that the C/DA results improve these quality measurements on an average range of 50% and 60%, respectively.

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Thermosphere Density Variability, Drag Coefficients, and Precision Satellite Orbits

Cited by 2 publications

References 31 publications

The First Comparison Between Swarm‐C Accelerometer‐Derived Thermospheric Densities and Physical and Empirical Model Estimates

The First Comparison Between Swarm‐C Accelerometer‐Derived Thermospheric Densities and Physical and Empirical Model Estimates

A simultaneous calibration and data assimilation (C/DA) to improve NRLMSISE00 using thermospheric neutral density (TND) from space-borne accelerometer measurements

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