Methods and software for estimation of total electron content in ionosphere using GNSS observations
Main Article Content
Abstract
Methods and algorithms for determining the total electron content in the ionosphere by signals of global navigation satellite systems are investigated. An algorithm for calculating and visualizing vertical total electron content over the territory of the Republic of Belarus and neighboring states is developed, taking into account correction of phase ambiguity due to «cycle slip» and estimation of differential code biases. Software for processing radio-tomographic data for high-orbit ionosphere control is created. It includes tools for calculating the total electron content from the signals of the GPS satellites, tools for eliminating cycle slip, tools for calculating differential code biases, tools for calculating vertical total electron content over the territory of the Republic of Belarus and neighboring states. The software is written in the Python programming language version 3.10, using third-party cross-platform free libraries. The performance of the presented methods and algorithms is demonstrated by examples.
Article Details
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
Artemiev, V. M., Naumov, A. O., Stepanov, V. L., & Murashko, N. I. (2008). Method and results of real time modeling of ionosphere radiotomography on the basis of the Kalman filter theory. Journal of Automation and Information Sciences, 40(2), 52-62. https://doi.org/10.1615/JAutomatInfScien.v40.i2.50
Belokonov, I. V., Krot, A. М., Kozlov, S. V., Kapliarchuk, Y. А., Savinykh, I. E., & Shapkin, А. S. (2023). A method for estimating the total electron content in the ionosphere based on the retransmission of signals from the global navigation satellite system GPS. Informatics, 20(2), 7-27. https://doi.org/10.37661/1816-0301-2023-20-2-7-27
Milanowska, B., Wielgosz, P., Krypiak-Gregorczyk, A., & Jarmołowski, W. (2021). Accuracy of global ionosphere maps in relation to their time interval. Remote Sensing, 13(18), 3552. https://doi.org/10.3390/rs13183552
Sickle, J. V. (2015). GPS for Land Surveyors. 4th ed. CRC Press, 368. ISBN 978-14-6658-310-8
Themens, D. R., Jayachandran, P. T., Langley, R. B., MacDougall, J. W., & Nicolls, M. J. (2013). Determining receiver biases in GPS-derived total electron content in the auroral oval and polar cap region using ionosonde measurements. GPS solutions, 17, 357-369. https://doi.org/10.1007/s10291-012-0284-6
Galkin, I., Froń, A., Reinisch, B., Hernández-Pajares, M., Krankowski, A., Nava, B., ... & Batista, I. (2022). Global monitoring of ionospheric weather by GIRO and GNSS data fusion. Atmosphere, 13(3), 371. https://doi.org/10.3390/atmos13030371
Zakharenkova, I., Cherniak, I., Braun, J. J., & Wu, Q. (2023). Global Maps of Equatorial Plasma Bubbles Depletions Based on FORMOSAT‐7/COSMIC‐2 Ion Velocity Meter Plasma Density Observations. Space Weather, 21(5), e2023SW003438. https://doi.org/10.1029/2023SW003438
Yasyukevich, Y., Mylnikova, A., & Vesnin, A. (2020). GNSS-based non-negative absolute ionosphere total electron content, its spatial gradients, time derivatives and differential code biases: bounded-variable least-squares and taylor series. Sensors, 20(19), 5702. https://doi.org/10.3390/s20195702
Juan, J. M., Sanz, J., Rovira-Garcia, A., González-Casado, G., Ibáñez, D., & Perez, R. O. (2018). AATR an ionospheric activity indicator specifically based on GNSS measurements. Journal of Space Weather and Space Climate, 8, A14. https://doi.org/10.1051/swsc/2017044
Rideout, W., & Coster, A. (2006). Automated GPS processing for global total electron content data. GPS solutions, 10, 219-228. https://doi.org/10.1007/s10291-006-0029-5
Roma-Dollase, D., Hernández-Pajares, M., Krankowski, A., Kotulak, K., Ghoddousi-Fard, R., Yuan, Y., ... & Gómez-Cama, J. M. (2018). Consistency of seven different GNSS global ionospheric mapping techniques during one solar cycle. Journal of Geodesy, 92, 691-706. https://doi.org/10.1007/s00190-017-1088-9
Li, Z., Wang, N., Hernández-Pajares, M., Yuan, Y., Krankowski, A., Liu, A., ... & Blot, A. (2020). IGS real-time service for global ionospheric total electron content modeling. Journal of Geodesy, 94, 32. https://doi.org/10.1007/s00190-020-01360-0
Lean, J. L., Meier, R. R., Picone, J. M., Sassi, F., Emmert, J. T., & Richards, P. G. (2016). Ionospheric total electron content: Spatial patterns of variability. Journal of Geophysical Research: Space Physics, 121(10), 367-402. https://doi.org/10.1002/2016JA023210
Huang, C., Lu, G., Zhang, Y., Paxton, L. J. (2021). Ionosphere Dynamics and Applications. American Geophysical Union and Wiley. ISBN 9781119507550.
Hofmann-Wellenhof, B. (2008). GNSS – Global Navigation Satellite Systems. GPS, GLONASS, Galileo, and more. Springer, Berlin, Germany. ISBN 978-3-211-73017-1
Materassi, M., Forte, B., Coster, A., Skone, S. (2020). The Dynamical Ionosphere A Systems Approach to Ionospheric Irregularity. Elsevier. ISBN 978-0-12-814782-5.
Hieu, L. V., Ferreira, V. G., He, X., & Tang, X. (2014). Study on cycle-slip detection and repair methods for a single dual-frequency global positioning system (GPS) receiver. Boletim de Ciências Geodésicas, 20(4), 984-1004. https://doi.org/10.1590/S1982-21702014000400054
Wang, Y., Zhao, L., & Gao, Y. (2021). Estimation and analysis of GNSS differential code biases (DCBs) using a multi-spacing software receiver. Sensors, 21(2), 443. https://doi.org/10.3390/s21020443
Wang, N., Yuan, Y., Li, Z., Montenbruck, O., & Tan, B. (2016). Determination of differential code biases with multi-GNSS observations. Journal of Geodesy, 90, 209-228. https://doi.org/10.1007/s00190-015-0867-4
Montenbruck, O., Hauschild, A., & Steigenberger, P. (2014). Differential code bias estimation using multi‐GNSS observations and global ionosphere maps. Navigation: Journal of the Institute of Navigation, 61(3), 191-201. https://doi.org/10.1002/navi.64
Subirana, J. S., Zornoza, J.M., Hernández, M. (2013). GNSS Data Processing, Vol. I: Fundamentals and Algorithms. Pajares, Contactivity bv, Leiden, the Netherlands, ISBN 978-92-9221-886-7
Ignacio, R. (2021). RINEX. The Receiver Independent Exchange Format Version 4.00. IGS/RTCM RINEX WG, Darmstadt, Germany, 120.
Astafyeva, E. (2019). Ionospheric detection of natural hazards. Reviews of Geophysics, 57(4), 1265-1288. https://doi.org/10.1029/2019RG000668
Komjathy, A., Yang, Y. M., Meng, X., Verkhoglyadova, O., Mannucci, A. J., & Langley, R. B. (2016). Review and perspectives: Understanding natural-hazards-generated ionospheric perturbations using GPS measurements and coupled modeling. Radio Science, 51(7), 951-961. https://doi.org/10.1002/2015RS005910
Laštovička, J. (2022). Long-term changes in ionospheric climate in terms of foF2. Atmosphere, 13(1), 110. https://doi.org/10.3390/atmos13010110
Naumov, A. O., Khmarskiy, P. A., Byshnev, N. I., & Piatrouski, N. A. (2023). Methods and software for calculating total electronic content based on GNSS data. Advanced Engineering Days (AED), 7, 158-160.