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Assimilation and Modeling of Thermospheric Neutral Density Using Observations from LEO Satellites
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Ghazaleh Ghazavi   , Saeed Farzaneh * , Mohammad Ali Sharifi |
| University of Tehran |
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Abstract: (399 Views) |
Accurate modeling of thermospheric neutral density (TND) is required to analyze the non-gravitational forces that affect low-Earth orbit (LEO) satellites and improve the performance of orbit determination models. Although empirical models such as NRLMSISE2.0 arewidelyutilized, the structural limitations in these models and their dependency on calibration leads to insufficient accuracy in environmental conditions. In this study, an algorithm is developed to integrate mass density observations from accelerometers mounted on CHAMP and GRACE satellites with the NRLMSISE2.0 model. After transferring the data to a reference height of 400km and performing statistical matching, corrections based on spherical harmonic expansion are applied in two variables of latitude and local solar time to reduce the bias between the observations and the model. The tatistical analysis of the performance of the improved model using the metrics of root mean square error (RMSE), relative deviation (ARD), logarithmic difference (LD), and correlation coefficient (R) shows that the improved model is able to reconstruct the thermospheric density structure more accurately and its performance in determining the satellite orbit is somehow improved. The numerical results indicate that the model accuracy for CHAMP and GRACE satellites is increased by 40.23% and 45.80%, respectively. These findings emphasize the effectiveness of the proposed method in updating thermospheric experimental models and their application in determining the orbit and spatial predictions.
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| Keywords: Thermospheric Neutral Density, Empirical Model, Calibration, Geodetic bservations |
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Full-Text [PDF 2079 kb]
(41 Downloads)
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Type of Study: Research |
Subject:
Geodesy
Received: 2025/04/24 | Accepted: 2025/12/13 | ePublished ahead of print: 2026/01/31 | Published: 2026/04/11
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