REFERENCES
1. Jin, S.; Wang, Q.; Dardanelli, G. A review on Multi-GNSS for earth observation and emerging applications. Remote. Sens. 2022, 14, 3930.
2. Klobuchar, J. Ionospheric time-delay algorithm for single-frequency GPS users. IEEE. Trans. Aerosp. Electron. Syst. 1987, AES-23, 325-31.
3. Davies, K.; Hartmann, G. K. Studying the ionosphere with the Global Positioning System. Radio. Sci. 1997, 32, 1695-703.
5. Coster, A.; Komjathy, A. Space weather and the global positioning system. Space. Weather. 2008, 6, 2008SW000400.
6. Akmaev, R. A. Whole atmosphere modeling: connecting terrestrial and space weather. Rev. Geophys. 2011, 49, 2011RG000364.
7. Kauristie, K.; Andries, J.; Beck, P.; et al. Space weather services for civil aviation - challenges and solutions. Remote. Sens. 2021, 13, 3685.
8. Chou, M. Y.; Lin, C. C. H.; Yue, J.; et al. Concentric traveling ionosphere disturbances triggered by Super Typhoon Meranti (2016). Geophys. Res. Lett. 2017, 44, 1219-26.
9. Savastano, G.; Komjathy, A.; Verkhoglyadova, O.; et al. Real-time detection of tsunami ionospheric disturbances with a stand-alone GNSS receiver: a preliminary feasibility demonstration. Sci. Rep. 2017, 7, 46607.
10. Blanc, E.; Jacobson, A. R. Observation of ionospheric disturbances following a 5-kt chemical explosion 2. Prolonged anomalies and stratifications in the lower thermosphere after shock passage. Radio. Sci. 1989, 24, 739-46.
11. Huang, C. Y.; Helmboldt, J. F.; Park, J.; Pedersen, T. R.; Willemann, R. Ionospheric detection of explosive events. Rev. Geophys. 2019, 57, 78-105.
12. Booker, H. G. A local reduction of F -region ionization due to missile transit. J. Geophys. Res. 1961, 66, 1073-9.
13. Mendillo, M.; Hawkins, G. S.; Klobuchar, J. A. A sudden vanishing of the ionospheric F region due to the launch of Skylab. J. Geophys. Res. 1975, 80, 2217-28.
14. Savastano, G.; Komjathy, A.; Shume, E.; et al. Advantages of geostationary satellites for ionospheric anomaly studies: ionospheric plasma depletion following a rocket launch. Remote. Sens. 2019, 11, 1734.
15. Rasheed, R.; Chen, B.; Wu, D.; Wu, L. A comparative study on multi-parameter ionospheric disturbances associated with the 2015 Mw 7.5 and 2023 Mw 6.3 earthquakes in Afghanistan. Remote. Sens. 2024, 16, 1839.
16. Akhoondzadeh, M.; De, Santis. A.; Marchetti, D.; Wang, T. Developing a deep learning-based detector of magnetic, Ne, Te and TEC anomalies from swarm satellites: The Case of Mw 7.1 2021 Japan earthquake. Remote. Sens. 2022, 14, 1582.
17. Jin, S.; Occhipinti, G.; Jin, R. GNSS ionospheric seismology: Recent observation evidences and characteristics. Earth-Sci. Rev. 2015, 147, 54-64.
18. Astafyeva, E.; Shults, K. Ionospheric GNSS imagery of seismic source: possibilities, difficulties, and challenges. J. Geophys. Res. Space. Phys. 2019, 124, 534-43.
20. Scherliess, L.; Schunk, R. W.; Sojka, J. J.; Thompson, D. C. Development of a physics-based reduced state Kalman filter for the ionosphere. Radio. Sci. 2004, 39, 2002RS002797.
21. Immel, T. J.; Harding, B. J.; Heelis, R. A.; et al. Regulation of ionospheric plasma velocities by thermospheric winds. Nat. Geosci. 2021, 14, 893-8.
22. Pasko, V. P.; Stanley, M. A.; Mathews, J. D.; Inan, U. S.; Wood, T. G. Electrical discharge from a thundercloud top to the lower ionosphere. Nature 2002, 416, 152-4.
23. Lu, G.; Richmond, A. D.; Emery, B. A.; Roble, R. G. Magnetosphere-ionosphere-thermosphere coupling: Effect of neutral winds on energy transfer and field-aligned current. J. Geophys. Res. 1995, 100, 19643-59.
24. Basu, S.; Kudeki, E.; Basu, S.; et al. Scintillations, plasma drifts, and neutral winds in the equatorial ionosphere after sunset. J. Geophys. Res. 1996, 101, 26795-809.
25. Ridley, A. J.; Richmond, A. D.; Gombosi, T. I.; De, Zeeuw. D. L.; Clauer, C. R. Ionospheric control of the magnetospheric configuration: thermospheric neutral winds. J. Geophys. Res. 2003, 108, 2002JA009464.
26. Belehaki, A.; Stanislawska, I.; Lilensten, J. An overview of ionosphere - thermosphere models available for space weather purposes. Space. Sci. Rev. 2009, 147, 271-313.
27. Rees, D.; Fuller-rowell, T. Understanding the transport of atomic oxygen within the thermosphere, using a numerical global thermospheric model. Planet. Space. Sci. 1988, 36, 935-48.
28. Roble, R. G.; Ridley, E. C.; Richmond, A. D.; Dickinson, R. E. A coupled thermosphere/ionosphere general circulation model. Geophys. Res. Lett. 1988, 15, 1325-8.
29. Richmond, A. D.; Ridley, E. C.; Roble, R. G. A thermosphere/ionosphere general circulation model with coupled electrodynamics. Geophys. Res. Lett. 1992, 19, 601-4.
30. Ridley, A.; Deng, Y.; Tóth, G. The global ionosphere-thermosphere model. J. Atmos. Sol-Terr. Phys. 2006, 68, 839-64.
31. Bilitza, D.; Altadill, D.; Truhlik, V.; et al. International reference ionosphere 2016: from ionospheric climate to real-time weather predictions. Space. Weather. 2017, 15, 418-29.
32. Pignalberi, A.; Pietrella, M.; Pezzopane, M. Towards a real-time description of the ionosphere: a comparison between international reference ionosphere (IRI) and IRI real-time assimilative mapping (IRTAM) models. Atmosphere 2021, 12, 1003.
33. Bilitza, D.; Xiong, C. A solar activity correction term for the IRI topside electron density model. Adv. Space. Res. 2021, 68, 2124-37.
34. Pignalberi, A.; Pezzopane, M.; Rizzi, R.; Galkin, I. Effective solar indices for ionospheric modeling: a review and a proposal for a real-time regional IRI. Surv. Geophys. 2018, 39, 125-67.
35. He, R.; Li, M.; Zhang, Q.; Zhao, Q. A comparison of a GNSS-GIM and the IRI-2020 model over China under different ionospheric conditions. Space. Weather. 2023, 21, e2023SW003646.
36. Radicella, S.; Leitinger, R. The evolution of the DGR approach to model electron density profiles. Adv. Space. Res. 2001, 27, 35-40.
37. Hochegger, G.; Nava, B.; Radicella, S.; Leitinger, R. A family of ionospheric models for different uses. Phys. Chem. Earth. C. 2000, 25, 307-10.
38. Wang, N.; Yuan, Y.; Li, Z.; Li, Y.; Huo, X.; Li, M. An examination of the Galileo NeQuick model: comparison with GPS and JASON TEC. GPS. Solut. 2017, 21, 605-15.
39. Kashcheyev, A.; Nava, B. Validation of NeQuick 2 model topside ionosphere and plasmasphere electron content using COSMIC POD TEC. J. Geophys. Res. Space. Phys. 2019, 124, 9525-36.
40. Feng, J.; Zhang, T.; Li, W.; Zhao, Z.; Han, B.; Wang, K. A new global TEC empirical model based on fusing multi-source data. GPS. Solut. 2023, 27, 1355.
41. Nava, B.; Coïsson, P.; Radicella, S. A new version of the NeQuick ionosphere electron density model. J. Atmos. Sol-Terr. Phys. 2008, 70, 1856-62.
42. Kelley, M. C.; Carlson, C. W. Observations of intense velocity shear and associated electrostatic waves near an auroral arc. J. Geophys. Res. 1977, 82, 2343-8.
43. Yue, X.; Schreiner, W. S.; Kuo, Y. Evaluating the effect of the global ionospheric map on aiding retrieval of radio occultation electron density profiles. GPS. Solut. 2013, 17, 327-35.
44. Radicella, S. M. New ways to modelling and predicting ionosphere variables. Atmosphere 2023, 14, 1788.
45. M. Decision tree, bagging and random forest methods detect TEC seismo-ionospheric anomalies around the time of the Chile, (Mw = 8.8) earthquake of 27 February 2010. Adv. Space. Res. 2016, 57, 2464-9.
46. Zhou, Y.; Liu, J.; Li, S.; Li, Q. Ionospheric TEC prediction based on ensemble learning models. Space. Weather. 2024, 22, e2023SW003790.
47. Han, Y.; Wang, L.; Fu, W.; Zhou, H.; Li, T.; Chen, R. Machine learning-based short-term GPS TEC forecasting during high solar activity and magnetic storm periods. IEEE. J. Sel. Top. Appl. Earth. Observations. Remote. Sensing. 2022, 15, 115-26.
48. Doven, S.; Güdar, B.; Al-nimer, K.; Aslan, Z. Estimating the effect of TEC data on rain with modelling and wavelet transformation analysis. ICCSA. 2023,. Computational. Science. and. Its. Applications. -. ICCSA. 2023. Workshops. , Gervasi, O.; Murgante, B.; Rocha, A. M .A. C.; Garau, C.; Scorza, F.; Karaca, Y.; Torre, C. M.; Eds.; Springer Nature, Cham, Switzerland, 2023; pp. 59-72.
49. Zhao, J.; Ren, B.; Wu, F.; Liu, H.; Li, G.; Li, D. TECX-TCN: Prediction of ionospheric total electron content at different latitudes in China based on XGBoost algorithm and temporal convolution network. J. Atmos. Sol-Terr. Phys. 2023, 249, 106091.
50. Nigusie, A.; Tebabal, A.; Galas, R. Modeling Ionospheric TEC using gradient boosting based and stacking machine learning techniques. Space. Weather. 2024, 22, e2023SW003821.
51. Han, Y.; Wang, L.; Chen, R.; et al. Topside ionospheric TEC modeling using multiple LEO satellites based on genetic algorithm-optimized machine learning models. GPS. Solut. 2024, 28, 1565.
52. Sivavaraprasad, G.; Lakshmi, Mallika. I.; Sivakrishna, K.; Venkata, Ratnam. D. A novel hybrid Machine learning model to forecast ionospheric TEC over low-latitude GNSS stations. Adv. Space. Res. 2022, 69, 1366-79.
53. Ji, G.; Jin, R.; Zhen, W.; Yang, H. Automatic GNSS Ionospheric scintillation detection with radio occultation data using machine learning algorithm. Appl. Sci. 2024, 14, 97.
54. Bals, A.; Thakrar, C.; Deshpande, K. B. Creating a Database to Identify high-latitude scintillation signatures with unsupervised machine learning. IEEE. J. Radio. Freq. Identif. 2022, 6, 240-9.
55. Zewdie, G. K.; Valladares, C.; Cohen, M. B.; Lary, D. J.; Ramani, D.; Tsidu, G. M. Data-driven forecasting of low-latitude ionospheric total electron content using the random forest and LSTM machine learning methods. Space. Weather. 2021, 19, e2020SW002639.
56. Zhukov, A.; Sidorov, D.; Mylnikova, A.; et al. Machine learning methodology for ionosphere total electron content nowcasting. Int. J. Artif. Intell. 2018, 16, 144-57.
57. Mcgranaghan, R. M.; Mannucci, A. J.; Wilson, B.; Mattmann, C. A.; Chadwick, R. New capabilities for prediction of high-latitude ionospheric scintillation: a novel approach with machine learning. Space. Weather. 2018, 16, 1817-46.
58. Liu, L.; Wan, W.; Chen, Y.; Le, H. Solar activity effects of the ionosphere: a brief review. Chin. Sci. Bull. 2011, 56, 1202-11.
59. Hernández-pajares, M.; Juan, J. M.; Sanz, J.; et al. The ionosphere: effects, GPS modeling and the benefits for space geodetic techniques. J. Geod. 2011, 85, 887-907.
60. Xue, D.; Wu, L.; Xu, T.; Wu, C.; Wang, Z.; He, Z. Space weather effects on transportation systems: a review of current understanding and future outlook. Space. Weather. 2024, 22, e2024SW004055.
61. Liu, L.; Wan, W.; Ning, B.; Pirog, O. M.; Kurkin, V. I. Solar activity variations of the ionospheric peak electron density. J. Geophys. Res. 2006, 111, 2006JA011598.
62. Liu, L.; Chen, Y.; Le, H.; et al. The ionosphere under extremely prolonged low solar activity. J. Geophys. Res. Space. Phys. 2011, 116, A04320.
63. Qian, L.; Burns, A. G.; Solomon, S. C. Annual/semiannual variation of the ionosphere. Geophys. Res. Lett. 2013, 40, 1928-33.
64. Zhao, B.; Wan, W.; Liu, L.; et al. Features of annual and semiannual variations derived from the global ionospheric maps of total electron content. Ann. Geophys. 2007, 25, 2513-27.
65. Azpilicueta, F.; Nava, B. A different view of the ionospheric winter anomaly. Adv. Space. Res. 2021, 67, 150-62.
66. Yasyukevich, Y. V.; Yasyukevich, A. S.; Ratovsky, K. G.; Klimenko, M. V.; Klimenko, V. V.; Chirik, N. V. Winter anomaly in NmF2 and TEC: when and where it can occur. J. Space. Weather. Space. Clim. 2018, 8, A45.
67. Shapley, A. H.; Beynon, W. J. G. ‘Winter Anomaly’ in ionospheric absorption and stratospheric warmings. Nature 1965, 206, 1242-3.
68. Hernández-Pajares, M.; Juan, J. M.; Sanz, J. Medium-scale traveling ionospheric disturbances affecting GPS measurements: Spatial and temporal analysis. J. Geophys. Res. 2006, 111, 2005JA011474.
69. Afraimovich, E. L.; Kosogorov, E. A.; Leonovich, L. A. The use of the international GPS network as the global detector (GLOBDET) simultaneously observing sudden ionospheric disturbances. Earth. Planet. Sp. 2000, 52, 1077-82.
70. Chernogor, L. F.; Mylovanov, Y. B.; Zhdanko, Y. H. Response of total electron content to the October 25, 2022 partial solar eclipse from high to low latitudes in the Euro-Asian region. Adv. Space. Res. 2024, 74, 1793-809.
71. N.; Liu, L. B.; Le, H. J. A brief review of equatorial ionization anomaly and ionospheric irregularities. Earth. Planet. Phys. 2018, 2, 257-75.
72. Eastes, R. W.; Solomon, S. C.; Daniell, R. E.; et al. Global-scale observations of the equatorial ionization anomaly. Geophys. Res. Lett. 2019, 46, 9318-26.
74. Ding, F.; Wan, W.; Xu, G.; Yu, T.; Yang, G.; Wang, J. Climatology of medium-scale traveling ionospheric disturbances observed by a GPS network in central China. J. Geophys. Res. 2011, 116, A09327.
76. Kintner, P. M.; Ledvina, B. M.; de, Paula. E. R. GPS and ionospheric scintillations. Space. Weather. 2007, 5, 2006SW000260.
78. Liu, J.; Wang, W.; Burns, A. G.; et al. In Upper Atmosphere Dynamics and Energetics; Structured storm-time polar ionosphere and its drivers: a review. American Geophysical Union and John Wiley & Sons, 2021;pp 239-52.
79. Wang, X.; Aa, E.; Chen, Y.; et al. Midlatitude neutral wind response during the Mother’s day super-intense geomagnetic storm in 2024 using observations from the Chinese meridian project. J. Geophys. Res. Space. Phys. 2025, 130, e2024JA033574.
80. Kelley, M. C.; Vlasov, M. N.; Foster, J. C.; Coster, A. J. A quantitative explanation for the phenomenon known as storm-enhanced density. Geophys. Res. Lett. 2004, 31, 2004GL020875.
81. Zhang, Q.; Moen, J.; Lockwood, M.; et al. Polar cap patch transportation beyond the classic scenario. J. Geophys. Res. Space. Phys. 2016, 121, 9063-74.
82. Wang, H.; Lühr, H.; Ma, S. Y. The relation between subauroral polarization streams, westward ion fluxes, and zonal wind: Seasonal and hemispheric variations. J. Geophys. Res. 2012, 117, 2011JA017378.
83. Zong, Q.; Reinisch, B. W.; Song, P.; Wei, Y.; Galkin, I. A. Dayside ionospheric response to the intense interplanetary shocks-solar wind discontinuities: observations from the digisonde global ionospheric radio observatory. J. Geophys. Res. 2010, 115, 2009JA014796.
84. Galkin, I. A.; Reinisch, B. W.; Huang, X.; Bilitza, D. Assimilation of GIRO data into a real-time IRI. Radio. Sci. 2012, 47, 2011RS004952.
85. Galkin, I.; Froń, A.; Reinisch, B.; et al. Global monitoring of ionospheric weather by GIRO and GNSS data fusion. Atmosphere 2022, 13, 371.
86. Reinisch, B. W.; Galkin, I. A. Global ionospheric radio observatory (GIRO). Earth. Planets. Space. 2011, 63, 377-81.
87. Estey, L. H.; Meertens, C. M. TEQC: The Multi-Purpose Toolkit for GPS/GLONASS Data. GPS. Solutions. 1999, 3, 42-9.
88. Galas, R.; Köhler, W. A binary exchange format for GPS data. Phys. Chem. Earth. A. 2001, 26, 645-8.
90. Ciraolo, L.; Azpilicueta, F.; Brunini, C.; Meza, A.; Radicella, S. M. Calibration errors on experimental slant total electron content (TEC) determined with GPS. J. Geod. 2007, 81, 111-20.
91. Ma, G.; Maruyama, T. Derivation of TEC and estimation of instrumental biases from GEONET in Japan. Ann. Geophys. 2003, 21, 2083-93.
92. Feltens, J. The activities of the Ionosphere Working Group of the International GPS Service (IGS). GPS. Solut. 2003, 7, 41-6.
93. Wielgosz, P.; Milanowska, B.; Krypiak-gregorczyk, A.; Jarmołowski, W. Validation of GNSS-derived global ionosphere maps for different solar activity levels: case studies for years 2014 and 2018. GPS. Solut. 2021, 25, 1142.
94. Jerez, G. O.; Hernández-Pajares, M.; Prol, F. S.; et al. Assessment of global ionospheric maps performance by means of ionosonde data. Remote. Sens. 2020, 12, 3452.
96. Hernández-pajares, M.; Lyu, H.; Aragón-àngel, À.; et al. Polar electron content from GPS data-based global ionospheric maps: assessment, case studies, and climatology. J. Geophys. Res. Space. Phys. 2020, 125, e2019JA027677.
97. Liu, Q.; Hernández-pajares, M.; Lyu, H.; Goss, A. Influence of temporal resolution on the performance of global ionospheric maps. J. Geod. 2021, 95, 1483.
98. Yasyukevich, Y. V.; Kiselev, A. V.; Zhivetiev, I. V.; et al. SIMuRG: system for ionosphere monitoring and research from GNSS. GPS. Solut. 2020, 24, 983.
99. Choi, J.; Lin, C. C.; Rajesh, P. K.; et al. Giant ionospheric density hole near the 2022 Hunga-Tonga volcanic eruption: multi-point satellite observations. Earth. Planets. Space. 2023, 75, 1933.
100. Astafyeva, E.; Maletckii, B.; Mikesell, T. D.; et al. The 15 January 2022 Hunga Tonga eruption history as inferred from ionospheric observations. Geophys. Res. Lett. 2022, 49, e2022GL098827.
101. Ghent, J. N.; Crowell, B. W. Spectral characteristics of ionospheric disturbances over the Southwestern Pacific from the 15 January 2022 Tonga eruption and tsunami. Geophys. Res. Lett. 2022, 49, e2022GL100145.
102. Hong, J.; Kil, H.; Lee, W. K.; Kwak, Y.; Choi, B.; Paxton, L. J. Detection of different properties of ionospheric perturbations in the vicinity of the Korean Peninsula after the Hunga-Tonga volcanic eruption on 15 January 2022. Geophys. Res. Lett. 2022, 49, e2022GL099163.
103. Le, G.; Liu, G.; Yizengaw, E.; Englert, C. R. Intense equatorial electrojet and counter electrojet caused by the 15 January 2022 Tonga volcanic eruption: space- and ground-based observations. Geophys. Res. Lett. 2022, 49, e2022GL099002.
104. Tang, L.; Li, Z.; Zhou, B. Large-area tsunami signatures in ionosphere observed by GPS TEC after the 2011 Tohoku earthquake. GPS. Solut. 2018, 22, 759.
105. Jayachandran, P. T.; Langley, R. B.; Macdougall, J. W.; et al. Canadian high arctic ionospheric network (CHAIN). Radio. Sci. 2009, 44, 2008RS004046.
106. Pereira VA, de Oliveira Camargo P. Brazilian active GNSS networks as systems for monitoring the ionosphere. GPS. Solut. 2017, 21, 1013-25.
107. Pashintsev, V. P.; Peskov, M. V.; Senokosov, M. A.; Mikhailov, D. A.; Skorik, A. D. A system for measuring the scintillation index based on the results of monitoring of small-scale fluctuations in the total electron content of the ionosphere. GPS. Solut. 2024, 28, 1550.
108. Niu, Z. Contemporary velocity field of crustal movement of Chinese mainland from Global Positioning System measurements. Chin. Sci. Bull. 2005, 50, 939-41.
109. Pignalberi, A.; Bilitza, D.; Coïsson, P.; et al. Validation of the IRI-2020 topside ionosphere options through in-situ electron density observations by low-Earth-orbit satellites. Adv. Space. Res. 2025, 75, 4192-216.
110. Smirnov, A.; Shprits, Y.; Zhelavskaya, I.; et al. Intercalibration of the plasma density measurements in earth’s topside ionosphere. J. Geophys. Res. Space. Phys. 2021, 126, e2021JA029334.
111. Smirnov, A.; Shprits, Y.; Prol, F. A novel neural network model of Earth’s topside ionosphere. Sci. Rep. 2023, 13, 1303.
112. Lin, C. H.; Wang, W.; Hagan, M. E.; et al. Plausible effect of atmospheric tides on the equatorial ionosphere observed by the FORMOSAT-3/COSMIC: three-dimensional electron density structures. Geophys. Res. Lett. 2007, 34, 2007GL029265.
113. Lin, C. Y.; Matsuo, T.; Liu, J. Y.; et al. Data assimilation of ground-based GPS and radio occultation total electron content for global ionospheric specification. J. Geophys. Res. Space. Phys. 2017, 122.
114. Zhao, B.; Wan, W.; Yue, X.; et al. Global characteristics of occurrence of an additional layer in the ionosphere observed by COSMIC/FORMOSAT-3. Geophys. Res. Lett. 2011, 38.
115. Pedatella, N. M.; Yue, X.; Schreiner, W. S. An improved inversion for FORMOSAT-3/COSMIC ionosphere electron density profiles. J. Geophys. Res. Space. Phys. 2015, 120, 8942-53.
116. Yue, X.; Schreiner, W. S.; Pedatella, N.; et al. Space weather observations by GNSS radio occultation: from FORMOSAT-3/COSMIC to FORMOSAT-7/COSMIC-2. Space. Weather. 2014, 12, 616-21.
117. Lin, C.; Lin, C. C.; Liu, J.; et al. The early results and validation of FORMOSAT-7/COSMIC-2 space weather products: global ionospheric specification and ne-aided abel electron density profile. J. Geophys. Res. Space. Phys. 2020, 125, e2020JA028028.
118. Pedatella, N. M.; Zakharenkova, I.; Braun, J. J.; et al. Processing and validation of FORMOSAT-7/COSMIC-2 GPS total electron content observations. Radio. Sci. 2021, 56, e2021RS007267.
119. La Beaujardière O. C/NOFS: a mission to forecast scintillations. J. Atmos. Sol-Terr. Phys. 2004, 66, 1573-91.
120. Jin, Y.; Spicher, A.; Xiong, C.; et al. Ionospheric plasma irregularities characterized by the swarm satellites: statistics at high latitudes. J. Geophys. Res. Space. Phys. 2019, 124, 1262-82.
121. Isham, B.; La, Hoz. C.; Kohl, H.; Hagfors, T.; Leyser, T.; Rietveld, M. Recent EISCAT heating results using chirped ISR. J. Atmos. Terr. Phys. 1996, 58, 369-83.
122. Yue, X.; Wan, W.; Ning, B.; et al. Development of the sanya incoherent scatter radar and preliminary results. J. Geophys. Res. Space. Phys. 2022, 127, e2022JA030451.
123. Yue, X.; Wan, W.; Ning, B.; Jin, L. An active phased array radar in China. Nat. Astron. 2022, 6, 619-619.
124. Wannberg, G.; Wolf, I.; Vanhainen, L.; et al. The EISCAT Svalbard radar: a case study in modern incoherent scatter radar system design. Radio. Sci. 1997, 32, 2283-307.
125. Liu, Y.; Morton, Y. J. Automatic detection of ionospheric scintillation-like GNSS satellite oscillator anomaly using a machine-learning algorithm. Navigation 2020, 67, 651-62.
126. Yang, D.; Fang, H. A low-latitude three-dimensional ionospheric electron density model based on radio occultation data using artificial neural networks with prior knowledge. Space. Weather. 2023, 21, e2022SW003299.
127. Harper, R.; Woodman, R. Preliminary multiheight radar observations of waves and winds in the mesosphere over Jicamarca. J. Atmos. Terr. Phys. 1977, 39, 959-63.
128. Camporeale, E. The challenge of machine learning in space weather: nowcasting and forecasting. Space. Weather. 2019, 17, 1166-207.
129. Wang, Z.; Zou, S.; Sun, H.; Chen, Y. Forecast global ionospheric TEC: apply modified U-Net on VISTA TEC data set. Space. Weather. 2023, 21, e2023SW003494.
130. Sun, H.; Chen, Y.; Zou, S.; et al. Complete global total electron content map dataset based on a video imputation algorithm VISTA. Sci. Data. 2023, 10, 236.
131. Zhou, H.; Zhang, S.; Peng, J.; et al. Informer: beyond efficient transformer for long sequence time-series forecasting. Proc. AAAI. Conf. Artif. Intell. 2021, 35, 11106-15.
132. Chen, L.; Zhong, X.; Zhang, F.; et al. FuXi: a cascade machine learning forecasting system for 15-day global weather forecast. NPJ. Clim. Atmos. Sci. 2023, 6, 512.
133. Bi, C.; Ren, P.; Yin, T.; Zhang, Y.; Li, B.; Xiang, Z. An informer architecture-based ionospheric foF2 model in the middle latitude region. IEEE. Geosci. Remote. Sens. Lett. 2022, 19, 1-5.
134. Zhang, Y.; Long, M.; Chen, K.; et al. Skilful nowcasting of extreme precipitation with NowcastNet. Nature 2023, 619, 526-32.
135. Lin, M.; Zhu, X.; Tu, G.; Chen, X. Optimal transformer modeling by space embedding for ionospheric total electron content prediction. IEEE. Trans. Instrum. Meas. 2022, 71, 1-14.
136. Xia, G.; Liu, M.; Zhang, F.; Zhou, C. CAiTST: conv-attentional image time sequence transformer for ionospheric TEC maps forecast. Remote. Sens. 2022, 14, 4223.
137. Luo, H.; Gong, Y.; Chen, S.; et al. Prediction of global ionospheric total electron content (TEC) based on SAM-ConvLSTM model. Space. Weather. 2023, 21, e2023SW003707.
138. Mukesh, R.; Karthikeyan, V.; Soma, P.; Sindhu, P. Prediction of TEC using NavIC/GPS data with geostatistical method/forecasting capability comparison with other models. Astrophys. Space. Sci. 2020, 365, 3868.
139. Zhukov, A. V.; Yasyukevich, Y. V.; Bykov, A. E. GIMLi: global ionospheric total electron content model based on machine learning. GPS. Solut. 2021, 25, 1055.
140. Chen, Z.; An, B.; Liao, W.; et al. Ionospheric electron density model by electron density grid deep neural network (EDG-DNN). Atmosphere 2023, 14, 810.
142. Wu, X.; Fan, C.; Tang, J.; Cheng, Y. Forecast of global ionospheric TEC using an improved transformer model. Adv. Space. Res. 2024, 73, 4519-38.
143. Tang, J.; Xu, L.; Fan, C.; Xu, C.; Ning, Y. A short-term prediction of ionospheric TEC using the RF-Prophet model for GNSS stations in China. Adv. Space. Res. 2025, 76, 3654-69.
144. Shidler, S. A.; Rodrigues, F. S. Modeling equatorial ionospheric vertical plasma drifts using machine learning. Earth. Planets. Space. 2020, 72, 1227.
145. Nigusie, A.; Tebabal, A.; Feyissa, F. Machine learning based storm time modeling of ionospheric vertical total electron content over Ethiopia. Sci. Rep. 2024, 14, 19293.
146. Han, C.; Chong, X. R.; Ou, M.; et al. Global ionospheric slab thickness prediction model using XGBoost and ensemble learning. Space. Weather. 2026, 24, e2025SW004606.
147. Ren, X.; Yang, P.; Liu, H.; Chen, J.; Liu, W. Deep learning for global ionospheric TEC forecasting: different approaches and validation. Space. Weather. 2022, 20, e2021SW003011.
148. Xia, G.; Zhang, F.; Wang, C.; Zhou, C. ED-ConvLSTM: a novel global ionospheric total electron content medium-term forecast model. Space. Weather. 2022, 20, e2021SW002959.
149. Li, L.; Liu, H.; Le, H.; et al. ED-AttConvLSTM: An ionospheric TEC map prediction model using adaptive weighted spatiotemporal features. Space. Weather. 2024, 22, e2023SW003740.
150. Wang, H.; Liu, H.; Yuan, J.; Le, H.; Shan, W.; Li, L. MAOOA-residual-attention-BiConvLSTM: an automated deep learning framework for global TEC map prediction. Space. Weather. 2024, 22, e2024SW003954.
151. Goodfellow, I.; Pouget-Abadie, J. Generative adversarial nets. In: The Twenty-Eighth Annual Conference on Neural Information Processing Systems, Advances in Neural Information Processing Systems, Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., Weinberger, K. Q., Eds.; Neural Information Processing Systems Foundation, Inc.: Montreal, Canada, 2014; Vol. 27. https://proceedings.neurips.cc/paper_files/paper/2014/file/f033ed80deb0234979a61f95710dbe25-Paper.pdf (accessed 2026-2-10).
152. Ji, E.; Moon, Y.; Park, E. Improvement of IRI global TEC maps by deep learning based on conditional generative adversarial networks. Space. Weather. 2020, 18, e2019SW002411.
153. Chen, Z.; Jin, M.; Deng, Y.; et al. Improvement of a deep learning algorithm for total electron content maps: image completion. J. Geophys. Res. Space. Phys. 2019, 124, 790-800.
154. Jeong, S.; Lee, W. K.; Jang, S.; et al. Reconstruction of the regional total electron content maps over the Korean peninsula using deep convolutional generative adversarial network and Poisson blending. Space. Weather. 2022, 20, e2022SW003131.
155. Pan, Y.; Jin, M.; Zhang, S.; Deng, Y. TEC map completion using DCGAN and Poisson blending. Space. Weather. 2020, 18, e2019SW002390.
156. Chen, J.; Fang, H.; Liu, Z. The application of a deep convolutional generative adversarial network on completing global TEC maps. J. Geophys. Res. Space. Phys. 2021, 126, e2020JA028418.
157. Pan, Y.; Jin, M.; Zhang, S.; Deng, Y. TEC map completion through a deep learning model: SNP-GAN. Space. Weather. 2021, 19, e2021SW002810.
158. Yang, K.; Liu, Y. Global Ionospheric total electron content completion with a GAN-based deep learning framework. Remote. Sens. 2022, 14, 6059.
159. Tian, P.; Yu, B.; Ye, H.; Xue, X.; Wu, J.; Chen, T. Estimation model of global ionospheric irregularities: an artificial intelligence approach. Space. Weather. 2022, 20, e2022SW003160.
160. Lan, T.; Hu, H.; Jiang, C.; Yang, G.; Zhao, Z. A comparative study of decision tree, random forest, and convolutional neural network for spread-F identification. Adv. Space. Res. 2020, 65, 2052-61.
161. Trebeschi, S.; van Griethuysen, J. J. M.; Lambregts, D. M. J.; et al. Deep learning for fully-automated localization and segmentation of rectal cancer on multiparametric MR. Sci. Rep. 2017, 7, 5301.
162. Liu, P.; Yokoyama, T.; Fu, W.; Yamamoto, M. Statistical analysis of medium-scale traveling ionospheric disturbances over Japan based on deep learning instance segmentation. Space. Weather. 2022, 20, e2022SW003151.
163. Lai, C.; Xu, J.; Lin, Z.; et al. Statistical characteristics of nighttime medium-scale traveling ionospheric disturbances from 10-years of airglow observation by the machine learning method. Space. Weather. 2023, 21, e2023SW003430.
164. Brissaud, Q.; Astafyeva, E. Near-real-time detection of co-seismic ionospheric disturbances using machine learning. Geophys. J. Int. 2022, 230, 2117-30.
165. Price, I.; Sanchez-Gonzalez, A.; Alet, F.; et al. Probabilistic weather forecasting with machine learning. Nature 2025, 637, 84-90.
166. Lam, R.; Sanchez-Gonzalez, A.; Willson, M.; et al. Learning skillful medium-range global weather forecasting. Science 2023, 382, 1416-21.
167. Bodnar, C.; Bruinsma, W. P.; Lucic, A.; et al. A foundation model for the Earth system. Nature 2025, 641, 1180-7.
168. Bi, K.; Xie, L.; Zhang, H.; Chen, X.; Gu, X.; Tian, Q. Accurate medium-range global weather forecasting with 3D neural networks. Nature 2023, 619, 533-8.
169. Sun, Y.; Liu, J.; Tsai, H.; Krankowski, A. Global ionosphere map constructed by using total electron content from ground-based GNSS receiver and FORMOSAT-3/COSMIC GPS occultation experiment. GPS. Solut. 2017, 21, 1583-91.
170. Liu, L.; Zou, S.; Yao, Y.; Wang, Z. Forecasting Global ionospheric TEC using deep learning approach. Space. Weather. 2020, 18, e2020SW002501.
171. Kaselimi, M.; Voulodimos, A.; Doulamis, N.; Doulamis, A.; Delikaraoglou, D. A causal long short-term memory sequence to sequence model for TEC prediction using GNSS observations. Remote. Sens. 2020, 12, 1354.
172. Kim, J.; Kwak, Y.; Kim, Y.; Moon, S.; Jeong, S.; Yun, J. Potential of regional ionosphere prediction using a long short-term memory deep-learning algorithm specialized for geomagnetic storm period. Space. Weather. 2021, 19, e2021SW002741.
173. Xiong, P.; Zhai, D.; Long, C.; Zhou, H.; Zhang, X.; Shen, X. Long short-term memory neural network for ionospheric total electron content forecasting over China. Space. Weather. 2021, 19, e2020SW002706.
174. Chen, J.; Zhi, N.; Liao, H.; Lu, M.; Feng, S. Global forecasting of ionospheric vertical total electron contents via ConvLSTM with spectrum analysis. GPS. Solut. 2022, 26, 1253.
175. Chen, Z.; Liao, W.; Li, H.; Wang, J.; Deng, X.; Hong, S. Prediction of global ionospheric TEC based on deep learning. Space. Weather. 2022, 20, e2021SW002854.
176. Xie, T.; Dai, Z.; Zhu, X.; Chen, B.; Ran, C. LSTM-based short-term ionospheric TEC forecast model and positioning accuracy analysis. GPS. Solut. 2023, 27, 1406.
177. Gao, X.; Yao, Y. A storm-time ionospheric TEC model with multichannel features by the spatiotemporal ConvLSTM network. J. Geod. 2023, 97, 1696.
178. Tang, J.; Zhong, Z.; Hu, J.; Wu, X. Forecasting regional ionospheric TEC maps over China using BiConvGRU deep learning. Remote. Sens. 2023, 15, 3405.
179. Ren, X.; Yang, P.; Mei, D.; Liu, H.; Xu, G.; Dong, Y. Global ionospheric TEC forecasting for geomagnetic storm time using a deep learning-based multi-model ensemble method. Space. Weather. 2023, 21, e2022SW003231.
180. Yang, D.; Fang, H.; Liu, Z. Completion of global ionospheric TEC maps using a deep learning approach. J. Geophys. Res. Space. Phys. 2022, 127, e2022JA030326.
181. Chen, Z.; Zhou, K.; Li, H.; Wang, J.; Ouyang, Z.; Deng, X. Global TEC map fusion through a hybrid deep learning model: RFGAN. Space. Weather. 2023, 21, e2022SW003341.
182. Hornik, K.; Stinchcombe, M.; White, H. Multilayer feedforward networks are universal approximators. Neural. Netw. 1989, 2, 359-66.
183. Hornik, K. Approximation capabilities of multilayer feedforward networks. Neural. Netw. 1991, 4, 251-7.
184. Schmidt-Hieber, J. The Kolmogorov-Arnold representation theorem revisited. Neural. Netw. 2021, 137, 119-26.
185. Erven T, Harremoes P. Rényi divergence and Kullback-Leibler divergence. IEEE. Trans. Inf. Theory. 2014, 60, 3797-820.
186. Menéndez, M.; Pardo, J.; Pardo, L.; Pardo, M. The Jensen-Shannon divergence. J. Frankl. Inst. 1997, 334, 307-18.
187. Shore, J.; Johnson, R. Properties of cross-entropy minimization. IEEE. Trans. Inform. Theory. 1981, 27, 472-82.
188. Alizadeh, M. M.; Schuh, H.; Todorova, S.; Schmidt, M. Global ionosphere maps of VTEC from GNSS, satellite altimetry, and formosat-3/COSMIC data. J. Geod. 2011, 85, 975-87.
189. Chen, G.; Wei, N.; Li, M.; Zhao, Q.; Zhang, J. BDS-3 and GPS/Galileo integrated PPP using broadcast ephemerides. GPS. Solut. 2022, 26, 1311.
190. Mukhtarov, P.; Pancheva, D.; Andonov, B.; Pashova, L. Global TEC maps based on GNSS data: 1. empirical background TEC model. J. Geophys. Res. Space. Phys. 2013, 118, 4594-608.
191. Mukhtarov, P.; Pancheva, D.; Andonov, B.; Pashova, L. Global TEC maps based on GNNS data: 2. model evaluation. J. Geophys. Res. Space. Phys. 2013, 118, 4609-17.
192. Prol, F. D. S.; Camargo, P. D. O.; Monico, J. F. G.; Muella, M. T. D. A. H. Assessment of a TEC calibration procedure by single-frequency PPP. GPS. Solut. 2018, 22, 701.
193. Santos Prol F, de Oliveira Camargo P, Hernandez-pajares M, de Assis Honorato Muella MT. A new method for ionospheric tomography and its assessment by ionosonde electron density, GPS TEC, and single-frequency PPP. IEEE. Trans. Geosci. Remote. Sensing. 2019, 57, 2571-82.
194. Rovira-garcia, A.; Juan, J. M.; Sanz, J.; González-casado, G.; Ibáñez, D. Accuracy of ionospheric models used in GNSS and SBAS: methodology and analysis. J. Geod. 2016, 90, 229-40.
195. Sunda, S.; Yadav, S.; Sridharan, R.; et al. SBAS-derived TEC maps: a new tool to forecast the spatial maps of maximum probable scintillation index over India. GPS. Solut. 2017, 21, 1469-78.
196. Sunda, S.; Sridharan, R.; Vyas, B. M.; et al. Satellite-based augmentation systems: A novel and cost-effective tool for ionospheric and space weather studies. Space. Weather. 2015, 13, 6-15.
197. Su, K.; Jin, S.; Hoque, M. M. Evaluation of ionospheric delay effects on multi-GNSS positioning performance. Remote. Sens. 2019, 11, 171.
198. Han, Y.; Wang, L.; Chen, R.; Fu, W.; Li, T.; Zhou, H. Toward real-time construction of global ionosphere map from ground and space-borne observations. GPS. Solut. 2022, 26, 1337.
199. Hu, T.; Xu, X.; Luo, J. Multi-source data ingestion for IRI-2020 model: a combination of ground-based and space-borne observations. GPS. Solut. 2024, 28, 1620.
200. Pedatella, N. M.; Anderson, J. L. The impact of assimilating COSMIC-2 observations of electron density in WACCMX. J. Geophys. Res. Space. Phys. 2022, 127, e2021JA029906.
201. Jakowski, N.; Hoque, M. M.; Mayer, C. A new global TEC model for estimating transionospheric radio wave propagation errors. J. Geod. 2011, 85, 965-74.
202. Hoque, M. M.; Jakowski, N. An alternative ionospheric correction model for global navigation satellite systems. J. Geod. 2015, 89, 391-406.
203. Hoque, M. M.; Jakowski, N.; Berdermann, J. Ionospheric correction using NTCM driven by GPS Klobuchar coefficients for GNSS applications. GPS. Solut. 2017, 21, 1563-72.






