Assimilation of the total electron content obtained from GNSS to a model of the ionosphere using a hierarchical Bayesian network | Journal of Space Weather and Space Climate

Open Access

Issue		J. Space Weather Space Clim. Volume 15, 2025


Article Number		23
Number of page(s)		17
DOI		https://doi.org/10.1051/swsc/2025019
Published online		04 June 2025

Aa E, Huang W, Yu S, Liu S, Shi L, et al. 2015. A regional ionospheric TEC mapping technique over China and adjacent areas on the basis of data assimilation. J Geophys Res Space Phys 120(6): 5049–5061. https://doi.org/10.1002/2015JA021140. [CrossRef] [Google Scholar]
Anghel A, Carrano C, Komjathy A, Astilean A, Letia T. 2009. Kalman filter-based algorithms for monitoring the ionosphere and plasmasphere with GPS in near-real time. J Atmos Sol Terr Phys 71(1): 158–174. https://doi.org/10.1016/j.jastp.2008.10.006. [CrossRef] [Google Scholar]
An X, Meng X, Chen H, Jiang W, Xi R, et al. 2020. Modelling global ionosphere based on multi-frequency, multi-constellation GNSS observations and IRI model. Remote Sens 12(3): 439. https://doi.org/10.3390/rs12030439. [CrossRef] [Google Scholar]
Banerjee S, Carlin BP, Gelfand AE. 2003. Hierarchical modeling and analysis for spatial data, Chapman and Hall/CRC, New York, NY. https://doi.org/10.1201/9780203487808. [CrossRef] [Google Scholar]
Blagoveshchensky DV, Sergeeva MA, Corona-Romero P. 2019. Features of the magnetic disturbance on September 7–8, 2017 by geophysical data. Adv Space Res, 64(1): 171–182. https://doi.org/10.1016/j.asr.2019.03.037. [CrossRef] [Google Scholar]
Bruinsma S, Boniface C, Sutton EK, Fedrizzi M. 2021. Thermosphere modeling capabilities assessment: geomagnetic storms. J Space Weather Space Clim 11: 12. https://doi.org/10.1051/swsc/2021002. [CrossRef] [EDP Sciences] [Google Scholar]
Carlin BP, Banerjee S. 2003. Hierarchical multivariate CAR models for spatio-temporally correlated survival data. Bayesian Statistics 7(7): 45–63. [Google Scholar]
Chen M, Liu L, Xu C, Wang Y. 2020. Improved IRI-2016 model based on BeiDou GEO TEC ingestion across China. GPS Solut 24: 20. https://doi.org/10.1007/s10291-019-0938-8. [CrossRef] [Google Scholar]
Forootan E, Farzaneh S, Kosary M, Schmidt M, Schumacher M. 2021. A simultaneous calibration and data assimilation (C/DA) to improve NRLMSISE00 using thermospheric neutral density (TND) from space-borne accelerometer measurements. Geophys J Int 224(2): 1096–1115. https://doi.org/10.1093/gji/ggaa507. [Google Scholar]
Forootan E, Kosary M, Farzaneh S, Schumacher M. 2023. Empirical data assimilation for merging total electron content data with empirical and physical models. Surv Geophys 44(6): 2011–2041. https://doi.org/10.1007/s10712-023-09788-7. [CrossRef] [Google Scholar]
Fuller-Rowell T, Araujo-Pradere E, Minter C, Codrescu M, Spencer P, et al. 2006. US-TEC: A new data assimilation product from the Space Environment Center characterizing the ionospheric total electron content using real-time GPS data. Radio Sci 41(6): RS6003. https://doi.org/10.1029/2005RS003393. [CrossRef] [Google Scholar]
Gardner CL, Schunk RW, Scherliess L, Sojka JJ, Zhu L. 2014. Global Assimilation of Ionospheric Measurements-Gauss Markov model: improved specifications with multiple data types. Space Weather 12(12): 675–688. https://doi.org/10.1002/2014SW001104. [CrossRef] [Google Scholar]
Grynyshyna-Poliuga O. 2024. Simultaneous monitoring of the limited area ionosphere with the use of GPS and ionosonde. Adv Space Res 73(12); 5964–5977. https://doi.org/10.1016/j.asr.2024.03.003. [CrossRef] [Google Scholar]
Gu S, Dai C, Fang W, Zheng F, Wang Y, et al. 2021. Multi-GNSS PPP/INS tightly coupled integration with atmospheric augmentation and its application in urban vehicle navigation. J Geod 95(6): 64. https://doi.org/10.1007/s00190-021-01514-8. [CrossRef] [Google Scholar]
Hajj GA, Wilson BD, Wang C, Pi X, Rosen IG 2004. Data assimilation of ground GPS total electron content into a physics-based ionospheric model by use of the Kalman filter. Radio Sci 39(1): RS1S05. https://doi.org/10.1029/2002RS002859. [Google Scholar]
Jiang C, Wei L, Yang G, Aa E, Lan T, et al. 2020. Large-scale ionospheric irregularities detected by ionosonde and GNSS receiver network. IEEE Geosci Remote Sens Lett 18(6): 940–943. https://doi.org/10.1109/LGRS.2020.2990940. [Google Scholar]
Kosary M, Forootan E, Farzaneh S, Schumacher M. 2022. A sequential calibration approach based on the ensemble Kalman filter (C-EnKF) for forecasting total electron content (TEC). J Geod 96(4): 29. https://doi.org/10.1007/s00190-022-01623-y. [CrossRef] [Google Scholar]
Li M, Yuan Y, Wang N, Li Z, Huo X. 2018. Performance of various predicted GNSS global ionospheric maps relative to GPS and JASON TEC data. GPS Solut 22, 55. https://doi.org/10.1007/s10291-018-0721-2. [CrossRef] [Google Scholar]
Lin CY, Matsuo T, Liu JY, Lin CH, Huba JD, et al. 2017. Data assimilation of ground-based GPS and radio occultation total electron content for global ionospheric specification. J Geophys Res Space Phys 122(10): 10876–10886. https://doi.org/10.1002/2017JA024185. [Google Scholar]
Mao T, Wan W, Yue X, Sun L, Zhao B, et al. 2008. An empirical orthogonal function model of total electron content over China. Radio Sci 43(2): RS2009. https://doi.org/10.1029/2007RS003629. [Google Scholar]
Manin AA, Sokolov SV, Novikov AI, Polyakova MV, Demidov DN, et al. 2021. Kalman filter adaptation to disturbances of the observer’s parameters. Inventions 6(4): 80. https://doi.org/10.3390/inventions6040080. [CrossRef] [Google Scholar]
McMillan NJ, Holland DM, Morara M, Feng J. 2010. Combining numerical model output and particulate data using Bayesian space-time modeling. Environmetrics 21(1): 48–65. https://doi.org/10.1002/env.984. [CrossRef] [Google Scholar]
Mukhtaro P, Pancheva D, Andonov B, Pashova L. 2013. Global TEC maps based on GNSS data: 1. Empirical background TEC model. J Geophys Res Space Phys 118(7): 4594–4608. https://doi.org/10.1002/jgra.50413. [CrossRef] [Google Scholar]
Mungufeni P, Migoya-Orué Y, Matamba TM, Omondi G. 2022. Application of classical Kalman filtering technique in assimilation of multiple data types to NeQuick model. J Space Weather Space Clim 12: 9. https://doi.org/10.1051/swsc/2022006. [CrossRef] [EDP Sciences] [Google Scholar]
Owolabi C, Lei J, Bolaji OS, Ren D, Yoshikawa A. 2020. Ionospheric current variations induced by the solar flares of 6 and 10 September 2017. Space Weather 18(11): e2020SW002608. https://doi.org/10.1029/2020SW002608. [CrossRef] [Google Scholar]
Peng J, Yuan Y, Liu Y, Zhang H, Zhang T, et al. 2024. Evaluation of GNSS-TEC data-drive IRI-2016 model for electron density. Atmosphere 15(8): 958. https://doi.org/10.3390/atmos15080958. [CrossRef] [Google Scholar]
Priyadarshi S. 2015. A review of ionospheric scintillation models. Surv Geophys 36: 295–324. https://doi.org/10.1007/s10712-015-9319-1. [CrossRef] [Google Scholar]
Qiao J, Liu Y, Fan Z, Tang Q, Li X, et al. 2021. Ionospheric TEC data assimilation based on Gauss-Markov Kalman filter. Adv Space Res 68(10): 4189–4204. https://doi.org/10.1016/j.asr.2021.08.004. [CrossRef] [Google Scholar]
Qiao J, Zhou C, Liu Y, Zhao J, Zhao Z. 2022. Ionospheric Kalman filter assimilation based on covariance localization technique. Remote Sens 14(16): 4003. https://doi.org/10.3390/rs14164003. [CrossRef] [Google Scholar]
Qin J, Liang S, Yang K, Kaihotsu I, Liu R, et al. 2009. Simultaneous estimation of both soil moisture and model parameters using particle filtering method through the assimilation of microwave signal. J Geophys Res Atmos 114(D15): D15103. https://doi.org/10.1029/2008JD011358. [Google Scholar]
Rayner P. 2020. Data assimilation using an ensemble of models: a hierarchical approach. Atmos Chem Phys 20(6): 3725–3737. https://doi.org/10.5194/acp-20-3725-2020. [CrossRef] [Google Scholar]
Ren X, Zhang X, Schmidt M, Zhao Z, Chen J, et al. 2020. Performance of GNSS global ionospheric modeling augmented by LEO constellation. Earth Space Sci 7(1): e2019EA000898. https://doi.org/10.1029/2019EA000898. [CrossRef] [Google Scholar]
Schillings A, Nilsson H, Slapak R, Wintoft P, Yamauchi M, et al. 2018. O⁺ escape during the extreme space weather event of 4–10 September 2017. Space Weather 16(9): 1363–1376. https://doi.org/10.1029/2018SW001881. [CrossRef] [Google Scholar]
Schunk RW, Scherliess L, Sojka JJ, Thompson DC, Anderson DN, et al. 2004. Global assimilation of ionospheric measurements (GAIM). Radio Sci 39(1): RS1S02. https://doi.org/10.1029/2002RS002794. [Google Scholar]
Song JJ, Mallick B. 2019. Hierarchical Bayesian models for predicting spatially correlated curves. Statistics 53(1): 196–209. https://doi.org/10.1080/02331888.2018.1547905. [CrossRef] [Google Scholar]
Ssessanga N, Kim YH, Habarulema JB, Kwak YS. 2019. On imaging South African regional ionosphere using 4D-var technique. Space Weather 17(11): 1584–1604. https://doi.org/10.1029/2019SW002321. [CrossRef] [Google Scholar]
Suneetha E, Ratnam DV, Leong TE. 2024. Regional ionospheric TEC modeling during geomagnetic storm in August 2021 – data fusion using multi-instrument observations. Adv Space Res 73(7): 3818–3832. https://doi.org/10.1016/j.asr.2023.06.054. [CrossRef] [Google Scholar]
Sætrøm J, Omre H. 2011. Ensemble Kalman filtering with shrinkage regression techniques. Comput Geosci 15: 271–292. https://doi.org/10.1007/s10596-010-9196-0. [CrossRef] [Google Scholar]
Tang J, Zhang S, Huo X, Wu X. 2022. Ionospheric assimilation of GNSS TEC into IRI model using a local ensemble Kalman filter. Remote Sens 14(14): 3267. https://doi.org/10.3390/rs14143267. [CrossRef] [Google Scholar]
Uwamahoro JC, Habarulema JB. 2015. Modelling total electron content during geomagnetic storm conditions using empirical orthogonal functions and neural networks. J Geophys Res Space Phys 120(12): 11. https://doi.org/10.1002/2015JA021961. [CrossRef] [Google Scholar]
Werner AL, Yordanova E, Dimmock AP, Temmer M. 2019. Modeling the multiple CME interaction event on 6–9 September 2017 with WSA-ENLIL+ Cone. Space Weather 17(2): 357–369. https://doi.org/10.1029/2018SW001993. [CrossRef] [Google Scholar]
Wikle CK, Berliner LM. 2007. A Bayesian tutorial for data assimilation. Physica D 230(1–2): 1–16. https://doi.org/10.1016/j.physd.2006.09.017. [CrossRef] [Google Scholar]
Xiong B, Wang Y, Li X, Li Y, Yu Y. 2022. Constructing a global ionospheric TEC map with a high spatial and temporal resolution by spherical harmonic functions. Astrophys Space Sci 367(9): 85. https://doi.org/10.1007/s10509-022-04120-y. [CrossRef] [Google Scholar]
Yuan Y, Wang N, Li Z, Huo X. 2019. The BeiDou global broadcast ionospheric delay correction model (BDGIM) and its preliminary performance evaluation results. Navigation 66(1): 55–69. https://doi.org/10.1002/navi.292. [CrossRef] [Google Scholar]
Yuan Y, Li Z, Wang N, Zhang B, Li H, et al. 2015. Monitoring the ionosphere based on the crustal movement observation network of China. Geod Geodyn 6(2): 73–80. https://doi.org/10.1016/j.geog.2015.01.004. [CrossRef] [Google Scholar]
Zhou F, Dong D, Li W, Jiang X, Wickert J, et al. 2018. GAMP: an open-source software of multi-GNSS precise point positioning using undifferenced and uncombined observations. GPS Solut 22: 33. https://doi.org/10.1007/s10291-018-0699-9. [CrossRef] [Google Scholar]

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