Articles | Volume 13, issue 9
https://doi.org/10.5194/essd-13-4567-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-13-4567-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
23 Sep 2021
Data description paper |
| 23 Sep 2021
| 23 Sep 2021
The cooperative IGS RT-GIMs: a reliable estimation of the global ionospheric electron content distribution in real time
Department of Mathematics, Universitat Politècnica de Catalunya (UPC-IonSAT), Barcelona,
Spain
Manuel Hernández-Pajares
CORRESPONDING AUTHOR
Department of Mathematics, Universitat Politècnica de Catalunya (UPC-IonSAT), Barcelona,
Spain
Institut d'Estudis Espacials de Catalunya (IEEC),
Barcelona, Spain
Heng Yang
School of Electronic Information and
Engineering, Yangtze Normal University, 408100 Chongqing, China
Department of Mathematics, Universitat Politècnica de Catalunya (UPC-IonSAT), Barcelona,
Spain
Enric Monte-Moreno
Department of Signal Theory and Communications, TALP, Universitat
Politècnica de Catalunya, 08034 Barcelona, Spain
David Roma-Dollase
Institut d'Estudis Espacials de Catalunya (IEEC),
Barcelona, Spain
Alberto García-Rigo
Department of Mathematics, Universitat Politècnica de Catalunya (UPC-IonSAT), Barcelona,
Spain
Institut d'Estudis Espacials de Catalunya (IEEC),
Barcelona, Spain
Zishen Li
Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing, China
Ningbo Wang
Aerospace Information Research Institute (AIR), Chinese Academy of Sciences (CAS), Beijing, China
Denis Laurichesse
Centre National d'Etudes Spatiales, Toulouse, France
Alexis Blot
Centre National d'Etudes Spatiales, Toulouse, France
Qile Zhao
GNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China
Collaborative Innovation Center of Earth and Space Science, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China
Qiang Zhang
GNSS Research Center, Wuhan University, No. 129 Luoyu Road, Wuhan 430079, China
André Hauschild
German Aerospace Center (DLR), German Space Operations Center (GSOC), 82234 Weßling, Germany
Loukis Agrotis
European Space Operations Center, European Space Agency, Darmstadt, Germany
Martin Schmitz
Geo++ GmbH, Steinriede 8, 30827 Garbsen, Germany
Gerhard Wübbena
Geo++ GmbH, Steinriede 8, 30827 Garbsen, Germany
Andrea Stürze
BKG, Federal Agency for Cartography and Geodesy, Frankfurt, Germany
Andrzej Krankowski
Space Radio-Diagnostics Research Centre, University of Warmia and Mazury in Olsztyn, 10-719 Olsztyn, Poland
Stefan Schaer
Astronomical Institute, the University of Bern, Sidlerstrasse 5, Bern 3012, Switzerland
Federal Office of Topography (swisstopo), Wabern, Switzerland
Joachim Feltens
Navigation Support Office, Telespazio Germany GmbH c/o European Space Agency/European Space Operations Centre, Robert-Bosch-Straße 5, 64293 Darmstadt, Germany
Attila Komjathy
Near Earth Tracking Systems Group (335S), NASA – Jet Propulsion
Laboratory, California Institute of Technology, 4800 Oak Grove Drive, M/S
138-317, Pasadena, CA 91109, USA
Reza Ghoddousi-Fard
Canadian Geodetic Survey, Natural Resources Canada, Ottawa, Canada
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Revised manuscript not accepted
Short summary
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This paper presents a data-driven model of the ionospheric electron density that has been developed for the EUMETSAT Polar System - Second Generation mission, with the main goal to improve the inversion of radio occultations for neutral atmospheric sounding. This model has been developed using occultation data from the COSMIC/FORMOSAT-3 satellite, which has been inverted using a LMS-based mechanization of the Abel inversion that implements the separability hypothesis.
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The work presented in this paper was done in the frame of an ESA activity. The aim of this project was to study ionosphere disturbances liable to impact navigation systems. This project has been running over several years, allowing enough data acquisition to gain sufficient knowledge of ionosphere variability. It was launched to support the European Satellite-Based Augmented System (EGNOS), also considering a possible extension of the system over Africa.
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We extend the kriging of the ionospheric electron density with slant total electron content (STEC) measurements based on a spatial covariance to kriging with a spatial–temporal covariance and develop a novel tomography approach by gradient-enhanced kriging assimilating STEC and F2 layer characteristics. The methods are cross-validated with independent measurements and point out the potential compensation for the often observed bias in the estimation of the F2 layer peak height.
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Cited articles
Afraimovich, E., Astafyeva, E., Oinats, A., Yasukevich, Y. V., and
Zhivetiev, I.: Global electron content and solar activity: comparison with
IRI modeling results, in: poster presentation at IGS Workshop,
Darmdstadt, Germany, 8–11 May 2006. a
Caissy, M., Agrotis, L., Weber, G., Hernandez-Pajares, M., and
Hugentobler, U.: Innovation: Coming Soon-The International GNSS
Real-Time Service, available at: https://www.gpsworld.com/gnss-systemaugmentation-assistanceinnovation-coming-soon-13044/ (last access: 21 March 2021), 2012. a
Chen, J., Ren, X., Zhang, X., Zhang, J., and Huang, L.: Assessment and Validation of Three Ionospheric Models (IRI-2016, NeQuick2, and IGS-GIM) From 2002 to 2018, Space Weather, 18, e2019SW002422, https://doi.org/10.1029/2019SW002422, 2020. a
Ciraolo, L., Azpilicueta, F., Brunini, C., Meza, A., and Radicella, S.: Calibration errors on experimental slant total electron content (TEC) determined with GPS, J. Geodesy, 81, 111–120, https://doi.org/10.1007/s00190-006-0093-1, 2007. a
Elsobeiey, M. and Al-Harbi, S.: Performance of real-time Precise Point Positioning using IGS real-time service, GPS Solut., 20, 565–571, https://doi.org/10.1007/s10291-015-0467-z, 2016. a
Feltens, J.: Development of a new three-dimensional mathematical ionosphere model at European Space Agency/European Space Operations Centre, Space Weather, 5, S12002, https://doi.org/10.1029/2006SW000294, 2007. a
Feltens, J. and Schaer, S.: IGS Products for the Ionosphere, in:
Proceedings of the 1998 IGS Analysis Center Workshop Darmstadt, Germany, 9–11 February 1998,
pp. 3–5, 1998. a
Fernandes, M. J., Lázaro, C., Nunes, A. L., and Scharroo, R.: Atmospheric corrections for altimetry studies over inland water, Remote Sens.-Basel, 6, 4952–4997, https://doi.org/10.3390/rs6064952, 2014. a
Froń, A., Galkin, I., Krankowski, A., Bilitza, D., Hernández-Pajares, M., Reinisch, B., Li, Z., Kotulak, K., Zakharenkova, I., Cherniak, I., Roma Dollase, D., Wang, N., Flisek, P., and García-Rigo, A.: Towards Cooperative Global Mapping of the Ionosphere: Fusion Feasibility for IGS and IRI with Global Climate VTEC Maps, Remote Sens.-Basel, 12, 3531, https://doi.org/10.3390/rs12213531, 2020. a
García-Rigo, A., Monte, E., Hernández-Pajares, M., Juan, J., Sanz, J., Aragón-Angel, A., and Salazar, D.: Global prediction of the vertical total electron content of the ionosphere based on GPS data, Radio Sci., 46, RS0D25, https://doi.org/10.1029/2010RS004643, 2011. a
García Rigo, A., Roma Dollase, D., Hernández Pajares, M., Li, Z., Terkildsen, M., Ghoddousi Fard, R., Dettmering, D., Erdogan, E., Haralambous, H., Beniguel, Y., Berdermann, J., Kriegel, M., Krypiak-Gregorczyk, A., Gulyaeva, T., Komjathy, A., Vergados, P., Feltens, J., Zandbergen, R., Olivares, G., Fuller-Rowell, T., Altadill, D., Blanch, E., Bergeot, N., Krankowski, A., Agrotis, L., Galkin, I., Orus-Perez, R., and Prol, F. S.: St. Patrick's day 2015 geomagnetic storm analysis based on real time ionosphere monitoring, in: EGU 2017: European Geosciences Union General Assembly, Vienna, Austria, 23–28 April 2017, proceedings book, 2017. a
Ghoddousi-Fard, R.: GPS ionospheric mapping at Natural Resources Canada, in: IGS workshop, Pasadena, 23–27 June 2014. a
Gonzalez, W. D., Tsurutani, B. T., and De Gonzalez, A. L. C.: Interplanetary origin of geomagnetic storms, Space Sci. Rev., 88, 529–562, https://doi.org/10.1023/A:1005160129098, 1999. a
Gulyaeva, T. L. and Stanislawska, I.: Derivation of a planetary ionospheric storm index, Ann. Geophys., 26, 2645–2648, https://doi.org/10.5194/angeo-26-2645-2008, 2008. a
Gulyaeva, T. L., Arikan, F., Hernandez-Pajares, M., and Stanislawska, I.: GIM-TEC adaptive ionospheric weather assessment and forecast system, J. Atmos. Sol.-Terr. Phy., 102, 329–340, https://doi.org/10.1016/j.jastp.2013.06.011, 2013. a
Hernández-Pajares, M., Juan, J., and Sanz, J.: Neural network modeling of the ionospheric electron content at global scale using GPS data, Radio Sci., 32, 1081–1089, https://doi.org/10.1029/97RS00431, 1997. a
Hernández-Pajares, M., Juan, J., Sanz, J., and Solé, J.: Global observation of the ionospheric electronic response to solar events using ground and LEO GPS data, J. Geophys. Res.-Space, 103, 20789–20796, https://doi.org/10.1029/98JA01272, 1998. a, b
Hernández-Pajares, M., Juan, J., and Sanz, J.: New approaches in global ionospheric determination using ground GPS data, J. Atmos. Sol.-Terr. Phy., 61, 1237–1247, https://doi.org/10.1016/S1364-6826(99)00054-1, 1999. a, b, c
Hernández-Pajares, M., Juan, J., Sanz, J., Orus, R., García-Rigo, A., Feltens, J., Komjathy, A., Schaer, S., and Krankowski, A.: The IGS VTEC maps: a reliable source of ionospheric information since 1998, J. Geodesy, 83, 263–275, https://doi.org/10.1007/s00190-008-0266-1, 2009. a, b, c
Hernández-Pajares, M., Roma-Dollase, D., Krankowski, A., García-Rigo, A., and Orús-Pérez, R.: Methodology and consistency of slant and vertical assessments for ionospheric electron content models, J. Geodesy, 91, 1405–1414, https://doi.org/10.1007/s00190-017-1032-z, 2017. a, b
Hernández-Pajares, M., Lyu, H., Aragón-Àngel, À., Monte-Moreno, E., Liu, J., An, J., and Jiang, H.: Polar Electron Content From GPS Data-Based Global Ionospheric Maps: Assessment, Case Studies, and Climatology, J. Geophys. Res.-Space, 125, e2019JA027677, https://doi.org/10.1029/2019JA027677, 2020. a
Hoque, M. M., Jakowski, N., and Orús-Pérez, R.: Fast ionospheric correction using Galileo Az coefficients and the NTCM model, GPS Solut., 23, 41, https://doi.org/10.1007/s10291-019-0833-3, 2019. a
Jakowski, N., Hoque, M., and Mayer, C.: A new global TEC model for estimating transionospheric radio wave propagation errors, J. Geodesy, 85, 965–974, https://doi.org/10.1007/s00190-011-0455-1, 2011. a
Jiang, H., Liu, J., Wang, Z., An, J., Ou, J., Liu, S., and Wang, N.: Assessment of spatial and temporal TEC variations derived from ionospheric models over the polar regions, J. Geodesy, 93, 455–471, https://doi.org/10.1007/s00190-018-1175-6, 2019. a
Johnston, G., Riddell, A., and Hausler, G.: The International GNSS Service, in: Springer Handbook of Global Navigation Satellite Systems, 1st edn., edited by: Teunissen, P. J. and Montenbruck, O., Springer International Publishing, Cham, Switzerland, 967–982, https://doi.org/10.1007/978-3-319-42928-1, 2017. a
Komjathy, A. and Born, G. H.: GPS-based ionospheric corrections for single frequency radar altimetry, J. Atmos. Sol.-Terr. Phy., 61, 1197–1203, https://doi.org/10.1016/S1364-6826(99)00051-6, 1999. a
Komjathy, A., Galvan, D., Stephens, P., Butala, M., Akopian, V., Wilson, B., Verkhoglyadova, O., Mannucci, A., and Hickey, M.: Detecting ionospheric TEC perturbations caused by natural hazards using a global network of GPS receivers: The Tohoku case study, Earth Planets Space, 64, 1287–1294, https://doi.org/10.5047/eps.2012.08.003, 2012. a
Le, A. Q. and Tiberius, C.: Single-frequency precise point positioning with optimal filtering, GPS Solut., 11, 61–69, https://doi.org/10.1007/s10291-006-0033-9, 2007. a
Li, M., Yuan, Y., Wang, N., Li, Z., and Huo, X.: 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, 2018. a
Li, X., Ge, M., Zhang, H., and Wickert, J.: A method for improving uncalibrated phase delay estimation and ambiguity-fixing in real-time precise point positioning, J. Geodesy, 87, 405–416, https://doi.org/10.1007/s00190-013-0611-x, 2013. a
Li, Z., Yuan, Y., Wang, N., Hernandez-Pajares, M., and Huo, X.: SHPTS: towards a new method for generating precise global ionospheric TEC map based on spherical harmonic and generalized trigonometric series functions, J. Geodesy, 89, 331–345, https://doi.org/10.1007/s00190-014-0778-9, 2015. a, b
Li, Z., Wang, N., Hernández Pajares, M., Yuan, Y., Krankowski, A., Liu, A., Zha, J., García Rigo, A., Roma-Dollase, D., Yang, H., Laurichesse, D., and Blot, A.: IGS real-time service for global ionospheric total electron content modeling, J. Geodesy, 94, 32, https://doi.org/10.1007/s00190-020-01360-0, 2020. a, b, c, d, e, f
Liu, J.-Y., Chen, Y., Chuo, Y., and Chen, C.-S.: A statistical investigation of preearthquake ionospheric anomaly, J. Geophys. Res.-Space, 111, A05304, https://doi.org/10.1029/2005JA011333, 2006. a
Liu, L., Wan, W., Ning, B., and Zhang, M.-L.: Climatology of the mean total electron content derived from GPS global ionospheric maps, J. Geophys. Res.-Space, 114, A06308, https://doi.org/10.1029/2009JA014244, 2009. a
Liu, Q. and Hernández-Pajares, M.: The archive of IGS combined real-time GIM [data set], available at: http://chapman.upc.es/irtg/archive, last access: 10 September 2021a. a
Liu, Q. and Hernández-Pajares, M.: The latest results of IGS combined real-time GIM [data set], available at: http://chapman.upc.es/irtg/last_results, last access: 10 September 2021b. a
Liu, Q., Hernández-Pajares, M., Lyu, H., and Goss, A.: Influence of temporal resolution on the performance of global ionospheric maps, J. Geodesy, 95, 34, https://doi.org/10.1007/s00190-021-01483-y, 2021a. a
Liu, Q., Hernández-Pajares, M., Yang, H., Monte-Moreno, E., Roma, D., García Rigo, A., Li, Z., Wang, N., Laurichesse, D., Blot, A., Zhao, Q., and Zhang, Q.: Global Ionosphere Maps of vertical electron content combined in real-time from the RT-GIMs of CAS, CNES, UPC-IonSAT, and WHU International GNSS Service (IGS) centers (from Dec 1, 2020, to March 1, 2021), Zenodo [data set], https://doi.org/10.5281/zenodo.5042622, 2021b. a, b
Loewe, C. and Prölss, G.: Classification and mean behavior of magnetic storms, J. Geophys. Res.-Space, 102, 14209–14213, https://doi.org/10.1029/96JA04020, 1997. a
Lou, Y., Zheng, F., Gu, S., Wang, C., Guo, H., and Feng, Y.: Multi-GNSS precise point positioning with raw single-frequency and dual-frequency measurement models, GPS Solut., 20, 849–862, https://doi.org/10.1007/s10291-015-0495-8, 2016. a
Mannucci, A., Wilson, B., Yuan, D., Ho, C., Lindqwister, U., and Runge, T.: A global mapping technique for GPS-derived ionospheric total electron content measurements, Radio Sci., 33, 565–582, https://doi.org/10.1029/97RS02707, 1998. a
Orús, R., Hernández-Pajares, M., Juan, J., and Sanz, J.: Improvement of global ionospheric VTEC maps by using kriging interpolation technique, J. Atmos. Sol.-Terr. Phy., 67, 1598–1609, https://doi.org/10.1016/j.jastp.2005.07.017, 2005. a
Ren, X., Chen, J., Li, X., Zhang, X., and Freeshah, M.: Performance evaluation of real-time global ionospheric maps provided by different IGS analysis centers, GPS Solut., 23, 113, https://doi.org/10.1007/s10291-019-0904-5, 2019. a
Roma-Dollase, D., Gómez Cama, J. M., Hernández Pajares, M., and
García-Rigo, A.:
Real-time Global Ionospheric modelling from GNSS data with
RT-TOMION model, in: 5th International Colloquium Scientific and
Fundamental Aspects of the Galileo Programme, Braunschweig, Germany, 27–29 October 2015. a
Roma-Dollase, D., Hernández-Pajares, M., García Rigo, A., Krankowski, A., Fron, A., Laurichesse, D., Blot, A., Orús-Pérez, R., Yuan, Y., Li, Z., Wang, N., Schmidt, M., and Erdogan, E.: Looking for optimal ways to combine global ionospheric maps in real-time, in: IGS workshop 2018, Wuhan, 29 October–2 November, 2018a. a, b
Roma-Dollase, D., Hernández-Pajares, M., Krankowski, A., Kotulak, K., Ghoddousi-Fard, R., Yuan, Y., Li, Z., Zhang, H., Shi, C., Wang, C., Feltens, J., Vergados, P., Komjathy, A., Schaer, S., García-Rigo, A., and Gómez-Cama, J. M.: Consistency of seven different GNSS global ionospheric mapping techniques during one solar cycle, J. Geodesy, 92, 691–706, https://doi.org/10.1007/s00190-017-1088-9, 2018b. a
Schaer, S., Beutler, G., Rothacher, M., and Springer, T. A.: Daily global ionosphere maps based on GPS carrier phase data routinely produced by the CODE Analysis Center, in: Proceedings of the IGS AC Workshop, Silver Spring, MD, USA, 19–21 March, 1996. a
Sezen, U., Arikan, F., Arikan, O., Ugurlu, O., and Sadeghimorad, A.: Online, automatic, near-real time estimation of GPS-TEC: IONOLAB-TEC, Space Weather, 11, 297–305, https://doi.org/10.1002/swe.20054, 2013. a
Sotomayor-Beltran, C., Sobey, C., Hessels, J., De Bruyn, G. et al.: Calibrating high-precision Faraday rotation measurements for LOFAR and the next generation of low-frequency radio telescopes, Astron. Astrophys., 552, A58, https://doi.org/10.1051/0004-6361/201220728, 2013. a
Tange, O.: Gnu parallel-the command-line power tool, The USENIX Magazine, 36, 42–47, https://doi.org/10.5281/zenodo.16303, 2011. a
Tomaszewski, D., Wielgosz, P., Rapiński, J., Krypiak-Gregorczyk, A., Kaźmierczak, R., Hernández-Pajares, M., Yang, H., and OrúsPérez, R.: Assessment of Centre National d'Etudes Spatiales Real-Time Ionosphere Maps in Instantaneous Precise Real-Time Kinematic Positioning over Medium and Long Baselines, Sensors, 20, 2293, https://doi.org/10.3390/s20082293, 2020. a
Wang, N., Li, Z., Duan, B., Hugentobler, U., and Wang, L.: GPS and GLONASS observable-specific code bias estimation: comparison of solutions from the IGS and MGEX networks, J. Geodesy, 94, 74, https://doi.org/10.1007/s00190-020-01404-5, 2020. a
Weber, G., Mervart, L., Lukes, Z., Rocken, C., and Dousa, J.: Real-time clock
and orbit corrections for improved point positioning via NTRIP, in:
Proceedings of the 20th international technical meeting of the satellite
division of the institute of navigation (ION GNSS 2007), Fort Worth, USA, 25–28 September 2007,
pp. 1992–1998, 2007. a
Weber, G., Mervart, L., Stürze, A., Rülke, A., and Stöcker, D.: BKG Ntrip Client, Version 2.12, vol. 49 of Mitteilungen des Bundesamtes für Kartographie und Geodäsie, Verlag des Bundesamtes für Kartographie und Geodäsie, Frankfurt am Main, 2016. a
Wright, J., Yang, A. Y., Ganesh, A., Sastry, S. S., and Ma, Y.: Robust face
recognition via sparse representation, IEEE T. Pattern Anal., 31, 210–227, https://doi.org/10.1109/TPAMI.2008.79, 2008. a
Wright, J., Ma, Y., Mairal, J., Sapiro, G., Huang, T. S., and Yan, S.: Sparse representation for computer vision and pattern recognition, P. IEEE, 98, 1031–1044, https://doi.org/10.1109/JPROC.2010.2044470, 2010. a
Yang, H., Monte-Moreno, E., Hernández-Pajares, M., and Roma-Dollase, D.: Real-time interpolation of global ionospheric maps by means of sparse representation, J. Geodesy, 95, 71, https://doi.org/10.1007/s00190-021-01525-5, 2021.
a
Zhang, B., Teunissen, P. J., Yuan, Y., Zhang, X., and Li, M.: A modified carrier-to-code leveling method for retrieving ionospheric observables and detecting short-term temporal variability of receiver differential code biases, J. Geodesy, 93, 19–28, https://doi.org/10.1007/s00190-018-1135-1, 2019. a
Zhang, H., Gao, Z., Ge, M., Niu, X., Huang, L., Tu, R., and Li, X.: On the convergence of ionospheric constrained precise point positioning (IC-PPP) based on undifferential uncombined raw GNSS observations, Sensors, 13, 15708–15725, https://doi.org/10.3390/s131115708, 2013a. a
Zhang, H., Xu, P., Han, W., Ge, M., and Shi, C.: Eliminating negative VTEC in global ionosphere maps using inequality-constrained least squares, Adv. Space Res., 51, 988–1000, https://doi.org/10.1016/j.asr.2012.06.026, 2013b. a
Zhang, Q. and Zhao, Q.: Global ionosphere mapping and differential code bias estimation during low and high solar activity periods with GIMAS software, Remote Sens.-Basel, 10, 705, https://doi.org/10.3390/rs10050705, 2018. a
Zhang, Q. and Zhao, Q.: Analysis of the data processing strategies of spherical harmonic expansion model on global ionosphere mapping for moderate solar activity, Adv. Space Res., 63, 1214–1226, https://doi.org/10.1016/j.asr.2018.10.031, 2019. a
Zhao, B., Wan, W., Liu, L., Mao, T., Ren, Z., Wang, M., and Christensen, A. B.: Features of annual and semiannual variations derived from the global ionospheric maps of total electron content, Ann. Geophys., 25, 2513–2527, https://doi.org/10.5194/angeo-25-2513-2007, 2007. a
Short summary
The upper part of the atmosphere, the ionosphere, is partially ionized, and it is being crossed by many multi-frequency signals of the Global Navigation Satellite System (GNSS) satellites. This unique source of data can be acquired in real time from hundreds of permanent GNSS receivers. The real-time processing providing the distribution of the ionospheric free electrons (Global Ionospheric Maps) can be done as well in real time. We present their updated real-time assessment and combination.
The upper part of the atmosphere, the ionosphere, is partially ionized, and it is being crossed...
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