Eigen‐Swarm: Swarm's Thermospheric Mass Density Modeling via Eigen‐Decomposition
Abstract Precise thermospheric mass density (TMD) prediction is essential for satellite orbital tracking, reentry calculations, and upper atmospheric processes under varying solar and magnetospheric conditions. In this paper, we construct an empirical model of TMD at 450 km altitude with acceleromet...
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| Format: | Article |
| Language: | English |
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Wiley
2025-07-01
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| Series: | Space Weather |
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| Online Access: | https://doi.org/10.1029/2025SW004351 |
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| author | Charles Owolabi Hyunju Connor Don Hampton Denny M. Oliveira Andres Calabia V. Sai Gowtam Eftyhia Zesta |
| author_facet | Charles Owolabi Hyunju Connor Don Hampton Denny M. Oliveira Andres Calabia V. Sai Gowtam Eftyhia Zesta |
| author_sort | Charles Owolabi |
| collection | DOAJ |
| description | Abstract Precise thermospheric mass density (TMD) prediction is essential for satellite orbital tracking, reentry calculations, and upper atmospheric processes under varying solar and magnetospheric conditions. In this paper, we construct an empirical model of TMD at 450 km altitude with accelerometer‐derived (ACC) TMD observations from the Swarm‐C satellite during 2014–2020. We employ the Eigen‐Decomposition technique to extract dominant spatio‐temporal modes, with the first three capturing 99.12% of the variance, forming the basis of the Swarm‐based Eigen‐Decomposition model. We study the factors controlling the observed TMD variability and investigate their relation to longitude, latitude, local solar time, seasonal effects, solar and geomagnetic indices. The Eigen‐Decomposition model performance is validated by comparison with the Jacchia‐Bowman 2008 (JB2008), Naval Research Laboratory Mass Spectrometer Incoherent Scatter 2.0 (NRLMSIS2.0), and Calabia and Jin (CAJIN) models, as well as TMD data from the Gravity Recovery and Climate Experiment Follow‐On mission during 2018–2020, using root mean square error (RMSE) as the evaluation metric. The Eigen‐Decomposition model achieves an RMSE of 19.45%, outperforming JB2008 (29.83%), NRLMSIS2.0 (65.16%), and CAJIN (45.25%). Additional metrics, including correlation coefficient (R), mean (μ), and variance (σ2), further confirm the improved accuracy and fidelity of our approach across different solar activity conditions. This work demonstrates the effectiveness of data‐driven techniques in capturing TMD dynamics and deepening our understanding of the thermospheric response to space weather conditions. |
| format | Article |
| id | doaj-art-56ad5d279c4e45b7a1d221dd696ddc68 |
| institution | Kabale University |
| issn | 1542-7390 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Space Weather |
| spelling | doaj-art-56ad5d279c4e45b7a1d221dd696ddc682025-08-20T03:34:53ZengWileySpace Weather1542-73902025-07-01237n/an/a10.1029/2025SW004351Eigen‐Swarm: Swarm's Thermospheric Mass Density Modeling via Eigen‐DecompositionCharles Owolabi0Hyunju Connor1Don Hampton2Denny M. Oliveira3Andres Calabia4V. Sai Gowtam5Eftyhia Zesta6Geophysical Institute University of Alaska Fairbanks Fairbanks AK USANASA Goddard Space Flight Center Greenbelt MD USAGeophysical Institute University of Alaska Fairbanks Fairbanks AK USANASA Goddard Space Flight Center Greenbelt MD USADepartment of Physics and Mathematics University of Alcala Madrid SpainNASA Goddard Space Flight Center Greenbelt MD USANASA Goddard Space Flight Center Greenbelt MD USAAbstract Precise thermospheric mass density (TMD) prediction is essential for satellite orbital tracking, reentry calculations, and upper atmospheric processes under varying solar and magnetospheric conditions. In this paper, we construct an empirical model of TMD at 450 km altitude with accelerometer‐derived (ACC) TMD observations from the Swarm‐C satellite during 2014–2020. We employ the Eigen‐Decomposition technique to extract dominant spatio‐temporal modes, with the first three capturing 99.12% of the variance, forming the basis of the Swarm‐based Eigen‐Decomposition model. We study the factors controlling the observed TMD variability and investigate their relation to longitude, latitude, local solar time, seasonal effects, solar and geomagnetic indices. The Eigen‐Decomposition model performance is validated by comparison with the Jacchia‐Bowman 2008 (JB2008), Naval Research Laboratory Mass Spectrometer Incoherent Scatter 2.0 (NRLMSIS2.0), and Calabia and Jin (CAJIN) models, as well as TMD data from the Gravity Recovery and Climate Experiment Follow‐On mission during 2018–2020, using root mean square error (RMSE) as the evaluation metric. The Eigen‐Decomposition model achieves an RMSE of 19.45%, outperforming JB2008 (29.83%), NRLMSIS2.0 (65.16%), and CAJIN (45.25%). Additional metrics, including correlation coefficient (R), mean (μ), and variance (σ2), further confirm the improved accuracy and fidelity of our approach across different solar activity conditions. This work demonstrates the effectiveness of data‐driven techniques in capturing TMD dynamics and deepening our understanding of the thermospheric response to space weather conditions.https://doi.org/10.1029/2025SW004351thermospheric mass densityeigen‐decompositionSwarm satelliteJB2008/NRLMSIS2.0 modelsempirical modelingsolar activity |
| spellingShingle | Charles Owolabi Hyunju Connor Don Hampton Denny M. Oliveira Andres Calabia V. Sai Gowtam Eftyhia Zesta Eigen‐Swarm: Swarm's Thermospheric Mass Density Modeling via Eigen‐Decomposition Space Weather thermospheric mass density eigen‐decomposition Swarm satellite JB2008/NRLMSIS2.0 models empirical modeling solar activity |
| title | Eigen‐Swarm: Swarm's Thermospheric Mass Density Modeling via Eigen‐Decomposition |
| title_full | Eigen‐Swarm: Swarm's Thermospheric Mass Density Modeling via Eigen‐Decomposition |
| title_fullStr | Eigen‐Swarm: Swarm's Thermospheric Mass Density Modeling via Eigen‐Decomposition |
| title_full_unstemmed | Eigen‐Swarm: Swarm's Thermospheric Mass Density Modeling via Eigen‐Decomposition |
| title_short | Eigen‐Swarm: Swarm's Thermospheric Mass Density Modeling via Eigen‐Decomposition |
| title_sort | eigen swarm swarm s thermospheric mass density modeling via eigen decomposition |
| topic | thermospheric mass density eigen‐decomposition Swarm satellite JB2008/NRLMSIS2.0 models empirical modeling solar activity |
| url | https://doi.org/10.1029/2025SW004351 |
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