Articles | Volume 23, issue 20
https://doi.org/10.5194/acp-23-13413-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-23-13413-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Oct 2023
Research article |
| 24 Oct 2023
| 24 Oct 2023
Ionospheric irregularity reconstruction using multisource data fusion via deep learning
Penghao Tian
Deep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Institute of Deep Space Sciences, Deep Space Exploration Laboratory, Hefei, China
Deep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Anhui Mengcheng Geophysics National Observation and Research Station, University of Science and Technology of China, Hefei, China
Hailun Ye
Deep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Deep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Anhui Mengcheng Geophysics National Observation and Research Station, University of Science and Technology of China, Hefei, China
Hefei National Laboratory, University of Science and Technology of China, Hefei, China
Jianfei Wu
Deep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Tingdi Chen
Deep Space Exploration Laboratory/School of Earth and Space Sciences, University of Science and Technology of China, Hefei, China
Anhui Mengcheng Geophysics National Observation and Research Station, University of Science and Technology of China, Hefei, China
Related authors
No articles found.
Liangyu Pu, Tingdi Chen, Yuli Han, Zhaowang Su, Yiming Song, Zhu Meng, Yuxin Wen, Chong Chen, Dongsong Sun, Xianghui Xue, and Xiankang Dou
EGUsphere, https://doi.org/10.5194/egusphere-2026-5254, https://doi.org/10.5194/egusphere-2026-5254, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Atmospheric wind and temperature are essential for understanding weather and atmospheric dynamics. We developed a new lidar receiver that measures both quantities from the same atmospheric backscatter signal without frequency scanning. Field experiments demonstrated wind measurements from 2 to 20 km and simultaneous wind and temperature measurements from 5.3 to 20 km, with good agreement with radiosondes.
Kexin Ming, Chong Wang, Wei Kong, Xianghui Xue, Genghua Huang, Ruikai Xue, Wenhao Li, Yudie Li, Chen Liang, Ruocan Zhao, Mingjiao Jia, and Xiang Shang
EGUsphere, https://doi.org/10.5194/egusphere-2026-4536, https://doi.org/10.5194/egusphere-2026-4536, 2026
This preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).
Short summary
Short summary
Boundary layer height is important for weather forecasting and climate studies, but consistent global observations remain difficult. We designed a spaceborne lidar and validated its performance through theoretical simulations and experiments with a scaled airborne prototype. Nighttime results in Yulin agreed closely with ground-based observations, while daytime tests in Hainan showed detection potential under strong background illumination, supporting future global monitoring from space.
Yuxia Jia, Chao Ban, Tao Li, Xin Fang, Zhaopeng Wu, Jianfei Wu, Weilin Pan, and Chengyun Yang
Atmos. Chem. Phys., 26, 11409–11420, https://doi.org/10.5194/acp-26-11409-2026, https://doi.org/10.5194/acp-26-11409-2026, 2026
Short summary
Short summary
In this study, a sodium lidar at the University of Science and Technology of China (USTC) observed multiple sporadic sodium layers (SSLs) and enhanced sodium patterns (ESPs) during a single night. By combining observations from a meteor radar and an ionosonde in Wuhan, the results show that horizontal winds can not only directly transport sodium atoms through advection but also indirectly influence sodium density by modulating ion convergence and Es layers via atmospheric waves.
Yiran Pan, Jialin Zhang, Chengyun Yang, Tao Li, Xin Fang, Xinyue Wang, Xianghui Xue, and Xiankang Dou
EGUsphere, https://doi.org/10.5194/egusphere-2026-3774, https://doi.org/10.5194/egusphere-2026-3774, 2026
Short summary
Short summary
In 2024, the upper atmosphere above Antarctica experienced two sudden warming events and then repeated warming and recovery instead of returning steadily to normal. Large-scale atmospheric waves disturbed the circling polar winds, and the altered winds then shaped how later waves moved upward, allowing the disturbance to persist. This helps explain how rare Antarctic events can last longer and affect links between the upper and lower atmosphere.
Wen Yi, Jianyuan Wang, Xianghui Xue, Chengyun Yang, Iain M. Reid, Robert A. Vincent, Andrew Mackinnon, Damian J. Murphy, Njål Gulbrandsen, Masaki Tsutsumi, Baiqi Ning, Guozhu Li, Nicholas J. Mitchell, Tracy Moffat-Griffin, Toshitaka Tsuda, Alan Z. Liu, Zishun Qiao, Haiying Li, Paulo P. Batista, Jianfei Wu, Tingdi Chen, and Xiankang Dou
EGUsphere, https://doi.org/10.5194/egusphere-2026-1883, https://doi.org/10.5194/egusphere-2026-1883, 2026
Short summary
Short summary
This study provides observational evidence that the solar zenith angle (SZA) influences the vertical structure of diurnal tides in the MLT region. Diurnal tides are strongest at low and mid-latitudes (10–50° N/S) and weaker near the Equator and at polar latitudes. The results further suggest that background zonal winds influence tidal propagation and structure through filtering and momentum drag.
Chao Ban, Xin Fang, Wen Yi, Jie Zeng, Gunter Stober, Weilin Pan, Tao Li, and Xianghui Xue
Atmos. Meas. Tech., 19, 1697–1709, https://doi.org/10.5194/amt-19-1697-2026, https://doi.org/10.5194/amt-19-1697-2026, 2026
Short summary
Short summary
This paper presents an intercomparison of zonal winds observed by a sodium fluorescence lidar and three types of meteor radars – a multistatic meteor radar, a conventional monostatic radar, and a forward remote receiver – located near Hefei, China. The multistatic meteor radar shows the best agreement with the lidar measurements, confirming the reliability of its wind retrievals in the mesosphere and lower thermosphere and highlighting its potential for future atmospheric dynamics studies.
Xingjin Wang, Xin Fang, Wenhao Gao, Xianhang Chen, Tai Liu, Chengyun Yang, Tingdi Chen, Tao Li, and Xianghui Xue
Atmos. Meas. Tech., 19, 1629–1641, https://doi.org/10.5194/amt-19-1629-2026, https://doi.org/10.5194/amt-19-1629-2026, 2026
Short summary
Short summary
This paper reports the deployment of the first narrowband sodium lidar in the low-latitude region of China. The lidar observations are compared with satellite measurements and atmospheric models, confirming the scientific credibility of these results. Calculations of vertical gravity wave heat and sodium fluxes are used to investigate the influence of the space environment. This lidar system will provide a new ground-based detection device for studying atmospheric environment above the area.
Chengyun Yang, Xiang Guo, Tao Li, Xinyue Wang, Jun Zhang, Xin Fang, and Xianghui Xue
Atmos. Chem. Phys., 26, 1021–1039, https://doi.org/10.5194/acp-26-1021-2026, https://doi.org/10.5194/acp-26-1021-2026, 2026
Short summary
Short summary
The Indian Ocean strongly influences weather and climate far beyond its region. We found that unusual sea surface warming patterns in the midlatitude Indian Ocean can disrupt winds and temperatures in the middle atmosphere, including the stratosphere and mesosphere, of the Southern Hemisphere. These disturbances alter ozone and air movement and may affect polar climate. Our results highlight the need to include Indian Ocean variability in climate models for better predictions.
Jianyuan Wang, Na Li, Wen Yi, Xianghui Xue, Iain M. Reid, Jianfei Wu, Hailun Ye, Jian Li, Zonghua Ding, Jinsong Chen, Guozhu Li, Yaoyu Tian, Boyuan Chang, Jiajing Wu, and Lei Zhao
Atmos. Chem. Phys., 24, 13299–13315, https://doi.org/10.5194/acp-24-13299-2024, https://doi.org/10.5194/acp-24-13299-2024, 2024
Short summary
Short summary
We present the impact of quasi-biennial oscillation (QBO) disruption events on diurnal tides over the low- and mid-latitude MLT region observed by a meteor radar chain. By using a global atmospheric model and reanalysis data, it is found that the stratospheric QBO winds can affect the mesospheric diurnal tides by modulating the subtropical ozone variability in the upper stratosphere and the interaction between tides and gravity waves in the mesosphere.
Jianfei Wu, Wuhu Feng, Xianghui Xue, Daniel Robert Marsh, and John Maurice Campbell Plane
Atmos. Chem. Phys., 24, 12133–12141, https://doi.org/10.5194/acp-24-12133-2024, https://doi.org/10.5194/acp-24-12133-2024, 2024
Short summary
Short summary
Metal layers occur in the mesosphere and lower thermosphere region 80–120 km from the ablation of cosmic dust. Nonmigrating diurnal tides are persistent global oscillations. We investigate nonmigrating diurnal tidal variations in metal layers using satellite observations and global climate model simulations; these have not been studied previously due to the limitations of measurements. The nonmigrating diurnal tides in temperature are strongly linked to the corresponding change in metal layers.
Christopher John Scott, Matthew N. Wild, Luke Anthony Barnard, Bingkun Yu, Tatsuhiro Yokoyama, Michael Lockwood, Cathryn Mitchel, John Coxon, and Andrew Kavanagh
Ann. Geophys., 42, 395–418, https://doi.org/10.5194/angeo-42-395-2024, https://doi.org/10.5194/angeo-42-395-2024, 2024
Short summary
Short summary
Long-term change in the ionosphere are expected due to increases in greenhouse gases in the lower atmosphere. Empirical formulae are used to estimate height. Through comparison with independent data we show that there are seasonal and long-term biases introduced by the empirical model. We conclude that estimates of long-term changes in ionospheric height need to account for these biases.
Wen Yi, Jie Zeng, Xianghui Xue, Iain Reid, Wei Zhong, Jianfei Wu, Tingdi Chen, and Xiankang Dou
Atmos. Meas. Tech. Discuss., https://doi.org/10.5194/amt-2022-254, https://doi.org/10.5194/amt-2022-254, 2022
Revised manuscript not accepted
Short summary
Short summary
In recent years, the concept of multistatic meteor radar systems has attracted the attention of the atmospheric radar community, focusing on the MLT region. In this study, we apply a multistatic meteor radar system consisting of a monostatic meteor radar in Mengcheng (33.36° N, 116.49° E) and a remote receiver in Changfeng (31.98° N, 117.22° E) to estimate the two-dimensional horizontal wind field, and the horizontal divergence and relative vorticity of the wind field.
Bingkun Yu, Xianghui Xue, Christopher J. Scott, Mingjiao Jia, Wuhu Feng, John M. C. Plane, Daniel R. Marsh, Jonas Hedin, Jörg Gumbel, and Xiankang Dou
Atmos. Chem. Phys., 22, 11485–11504, https://doi.org/10.5194/acp-22-11485-2022, https://doi.org/10.5194/acp-22-11485-2022, 2022
Short summary
Short summary
We present a study on the climatology of the metal sodium layer in the upper atmosphere from the ground-based measurements obtained from a lidar network, the Odin satellite measurements, and a global model of meteoric sodium in the atmosphere. Comprehensively, comparisons show good agreement and some discrepancies between ground-based observations, satellite measurements, and global model simulations.
Jianfei Wu, Wuhu Feng, Han-Li Liu, Xianghui Xue, Daniel Robert Marsh, and John Maurice Campbell Plane
Atmos. Chem. Phys., 21, 15619–15630, https://doi.org/10.5194/acp-21-15619-2021, https://doi.org/10.5194/acp-21-15619-2021, 2021
Short summary
Short summary
Metal layers occur in the MLT region (80–120 km) from the ablation of cosmic dust. The latest lidar observations show these metals can reach a height approaching 200 km, which is challenging to explain. We have developed the first global simulation incorporating the full life cycle of metal atoms and ions. The model results compare well with lidar and satellite observations of the seasonal and diurnal variation of the metals and demonstrate the importance of ion mass and ion-neutral coupling.
Cited articles
Altman, D. G. and Bland, J. M.: Measurement in medicine: the analysis of method comparison studies, J. Roy. Stat. Soc. D-Sta., 32, 307–317, 1983. a
Anthes, R. A., Bernhardt, P., Chen, Y., Cucurull, L., Dymond, K., Ector, D., Healy, S., Ho, S.-P., Hunt, D., and Kuo, Y.-H.: The COSMIC/FORMOSAT-3 mission: Early results, B. Am. Meteorol. Soc., 89, 313–334, https://doi.org/10.1175/BAMS-89-3-313, 2008. a
Arras, C., Wickert, J., Beyerle, G., Heise, S., Schmidt, T., and Jacobi, C.: A global climatology of ionospheric irregularities derived from GPS radio occultation, Geophys. Res. Lett., 35, L14809, https://doi.org/10.1029/2008GL034158, 2008. a, b, c
Arras, C., Jacobi, C., and Wickert, J.: Semidiurnal tidal signature in sporadic E occurrence rates derived from GPS radio occultation measurements at higher midlatitudes, Ann. Geophys., 27, 2555–2563, https://doi.org/10.5194/angeo-27-2555-2009, 2009. a
Bengio, Y., Simard, P., and Frasconi, P.: Learning long-term dependencies with gradient descent is difficult, IEEE T. Neural Networ., 5, 157–166, 1994. a
Bilitza, D., Pezzopane, M., Truhlik, V., Altadill, D., Reinisch, B. W., and Pignalberi, A.: The International Reference Ionosphere model: A review and description of an ionospheric benchmark, Rev. Geophys., 60, e2022RG000792, https://doi.org/10.1029/2022RG000792, 2022. a
Bowling, M. and Veloso, M.: Multiagent learning using a variable learning rate, Artif. Intell., 136, 215–250, 2002. a
Breiman, L.: Classification and regression trees, Routledge, https://doi.org/10.1201/9781315139470, 2017. a, b
Briggs, B. and Parkin, I.: On the variation of radio star and satellite scintillations with zenith angle, J. Atmos. Terr. Phys., 25, 339–366, 1963. a
Carter, L. N. and Forbes, J. M.: Global transport and localized layering of metallic ions in the upper atmospherer, Ann. Geophys., 17, 190–209, https://doi.org/10.1007/s00585-999-0190-6, 1999. a
Chapman, S.: The absorption and dissociative or ionizing effect of monochromatic radiation in an atmosphere on a rotating earth, P. Phys. Soc., 43, 26–45, https://doi.org/10.1088/0959-5309/43/1/305, 1931a. a
Chapman, S.: The absorption and dissociative or ionizing effect of monochromatic radiation in an atmosphere on a rotating earth part II. Grazing incidence, P. Phys. Soc., 43, 483–501, https://doi.org/10.1088/0959-5309/43/5/302, 1931b. a
Chu, Y.-H., Wang, C., Wu, K., Chen, K., Tzeng, K., Su, C.-L., Feng, W., and Plane, J.: Morphology of sporadic E layer retrieved from COSMIC GPS radio occultation measurements: Wind shear theory examination, J. Geophys. Res.-Space, 119, 2117–2136, 2014. a
Couronné, R., Probst, P., and Boulesteix, A.-L.: Random forest versus logistic regression: a large-scale benchmark experiment, BMC bioinformatics, 19, 1–14, 2018. a
Dewitte, K., Fierens, C., Stockl, D., and Thienpont, L. M.: Application of the Bland–Altman plot for interpretation of method-comparison studies: a critical investigation of its practice, Clin. Chem., 48, 799–801, 2002. a
Djuth, F., Zhang, L., Livneh, D., Seker, I., Smith, S., Sulzer, M., Mathews, J., and Walterscheid, R.: Arecibo's thermospheric gravity waves and the case for an ocean source, J. Geophys. Res.-Space, 115, A08305, https://doi.org/10.1029/2009JA014799, 2010. a
Emmons, D. J., Wu, D. L., and Swarnalingam, N.: A Statistical Analysis of Sporadic-E Characteristics Associated with GNSS Radio Occultation Phase and Amplitude Scintillations, Atmosphere, 13, 2098, https://doi.org/10.3390/atmos13122098, 2022. a
Giamalaki, K., Beaulieu, C., and Prochaska, J.: Assessing predictability of marine heatwaves with random forests, Geophys. Res. Lett., 49, e2022GL099069, https://doi.org/10.1029/2022GL099069, 2022. a
Glorot, X. and Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks, in: Proceedings of the thirteenth international conference on artificial intelligence and statistics, JMLR Workshop and Conference Proceedings, 13–15 May 2010, Chia Laguna Resort, Sardinia, Italy, 249–256, 2010. a
Goncharenko, L., Chau, J., Liu, H.-L., and Coster, A.: Unexpected connections between the stratosphere and ionosphere, Geophys. Res. Lett., 37, L10101, https://doi.org/10.1029/2010GL043125, 2010. a
Gooch, J. Y., Colman, J. J., Nava, O. A., and Emmons, D. J.: Global ionosonde and GPS radio occultation sporadic-E intensity and height comparison, J. Atmos. Sol.-Terr. Phys., 199, 105200, https://doi.org/10.1016/j.jastp.2020.105200, 2020. a
Haldoupis, C.: A tutorial review on sporadic E layers, in: Aeronomy of the Earth's Atmosphere and Ionosphere, edited by: Abdu, M. and Pancheva, D., 381–394, Vol. 2, Springer, Dordrecht, https://doi.org/10.1007/978-94-007-0326-1_29, 2011. a, b
He, K., Zhang, X., Ren, S., and Sun, J.: Deep residual learning for image recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), Las Vegas, NV, USA, 770–778, https://doi.org/10.1109/CVPR.2016.90, 2016. a
Hersbach, H., Bell, B., Berrisford, P., et al.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc., 146, 1999–2049, https://doi.org/10.1002/qj.3803, 2020. a
Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thépaut, J.-N.: ERA5 hourly data on pressure levels from 1940 to present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS) [data set], https://doi.org/10.24381/cds.bd0915c6, 2023. a
Hodos, T. J., Nava, O. A., Dao, E. V., and Emmons, D. J.: Global sporadic-E occurrence rate climatology using GPS radio occultation and ionosonde data, J. Geophys. Res.-Space, 127, e2022JA030795, https://doi.org/10.1029/2022JA030795, 2022. a
Jin, H., Miyoshi, Y., Fujiwara, H., Shinagawa, H., Terada, K., Terada, N., Ishii, M., Otsuka, Y., and Saito, A.: Vertical connection from the tropospheric activities to the ionospheric longitudinal structure simulated by a new Earth's whole atmosphere-ionosphere coupled model, J. Geophys. Res.-Space, 116, A01316, https://doi.org/10.1029/2010JA015925, 2011. a
Johnson, E. and Heelis, R.: Characteristics of ion velocity structure at high latitudes during steady southward interplanetary magnetic field conditions, J. Geophys. Res.-Space, 110, A12301, https://doi.org/10.1029/2005JA011130, 2005. a
Kim, T.-Y. and Cho, S.-B.: Predicting residential energy consumption using CNN-LSTM neural networks, Energy, 182, 72–81, 2019. a
King, J. H. and Papitashvili, N. E.: Solar wind spatial scales in and comparisons of hourly Wind and ACE plasma and magnetic field data, J. Geophys. Res., 110, A02209, https://doi.org/10.1029/2004JA010649, 2005 (data available at: https://omniweb.gsfc.nasa.gov/, last access: 16 October 2023). a, b
LeCun, Y., Bengio, Y., and Hinton, G.: Deep learning, Nature, 521, 436–444, 2015. a
Lei, J., Syndergaard, S., Burns, A. G., Solomon, S. C., Wang, W., Zeng, Z., Roble, R. G., Wu, Q., Kuo, Y.-H., Holt, J. M., Zhang, S.-R., Hysell, D. L., Rodrigues, F. S., and Lin, C. H.: Comparison of COSMIC ionospheric measurements with ground-based observations and model predictions: Preliminary results, J. Geophys. Res.-Space, 112, A07308, https://doi.org/10.1029/2006JA012240, 2007. a, b
Lei, J., Thayer, J. P., Forbes, J. M., Wu, Q., She, C., Wan, W., and Wang, W.: Ionosphere response to solar wind high-speed streams, Geophys. Res. Lett., 35, L19105, https://doi.org/10.1029/2008GL035208, 2008. a
Li, G., Ning, B., Otsuka, Y., Abdu, M. A., Abadi, P., Liu, Z., Spogli, L., and Wan, W.: Challenges to equatorial plasma bubble and ionospheric scintillation short-term forecasting and future aspects in east and southeast Asia, Surv. Geophys., 42, 201–238, 2021. a
Licata, R. J., Mehta, P. M., Tobiska, W. K., and Huzurbazar, S.: Machine-learned HASDM thermospheric mass density model with uncertainty quantification, Space Weather, 20, e2021SW002915, https://doi.org/10.1029/2021SW002915, 2022. a
Liu, Y., Duffy, K., Dy, J. G., and Ganguly, A. R.: Explainable deep learning for insights in El Niño and river flows, Nat. Commun., 14, 339, 2023. a
Liu, Z., Fang, H., Yue, X., and Lyu, H.: Wavenumber-4 Patterns of the Sporadic E Over the Middle-and Low-Latitudes, J. Geophys. Res.-Space, 126, e2021JA029238, https://doi.org/10.1029/2021JA029238, 2021. a
Lühr, H., Alken, P., and Zhou, Y.-L.: The equatorial electrojet, in: Ionosphere Dynamics and Applications, edited by: Huang, C., Lu, G., Zhang, Y., and Paxton, L. J., Geophysical Monograph Series, 281–299, https://doi.org/10.1002/9781119815617.ch12, 2021. a
MacDougall, J., Jayachandran, P., and Plane, J.: Polar cap Sporadic-E: part 1, observations, J. Atmos. Sol.-Terr. Phys., 62, 1155–1167, 2000. a
Mathews, J.: Sporadic E: current views and recent progress, J. Atmos. Sol.-Terr. Phy., 60, 413–435, 1998. a
Matsushita, S. and Reddy, C.: A study of blanketing sporadic E at middle latitudes, J. Geophys. Res., 72, 2903–2916, 1967. a
Meridian Space Weather Monitoring Project: Digital ionosonde data, CMP [data set], https://data.meridianproject.ac.cn/data-directory/, last access: 16 October 2023. a
Nair, V. and Hinton, G. E.: Rectified linear units improve restricted boltzmann machines, in: Proceedings of the 27th international conference on machine learning (ICML-10), 21–24 June 2010, Haifa, Israel, 807–814, 2010. a
National Space Science Data Center: Digital ionosonde data, NSSDC [data set], http://www.nssdc.ac.cn, last access: 16 October 2023. a
Nygren, T., Jalonen, L., Oksman, J., and Turunen, T.: The role of electric field and neutral wind direction in the formation of sporadic E-layers, J. Atmos. Terr. Phys., 46, 373–381, 1984. a
Ogawa, T., Suzuki, A., and Kunitake, M.: Spatial distribution of mid-latitude sporadic E scintillations in summer daytime, Radio Sci., 24, 527–538, 1989. a
Oshiro, T. M., Perez, P. S., and Baranauskas, J. A.: How many trees in a random forest?, in: Machine Learning and Data Mining in Pattern Recognition: 8th International Conference, MLDM 2012, Berlin, Germany, 13–20 July 2012. Proceedings 8, pp. 154–168, Springer, 2012. a
Papitashvili, N. E. and King, J. H.: OMNI Hourly Data Set, NASA Space Physics Data Facility [data set], https://doi.org/10.48322/1SHR-HT18, 2020. a
Pavelyev, A., Liou, Y., Wickert, J., Schmidt, T., Pavelyev, A., and Liu, S.-F.: Effects of the ionosphere and solar activity on radio occultation signals: Application to CHAllenging Minisatellite Payload satellite observations, J. Geophys. Res.-Space, 112, A06326, https://doi.org/10.1029/2006JA011625, 2007. a
Plane, J. M.: Cosmic dust in the earth's atmosphere, Chem. Soc. Rev., 41, 6507–6518, 2012. a
Plane, J. M., Feng, W., and Dawkins, E. C.: The mesosphere and metals: Chemistry and changes, Chem. Rev., 115, 4497–4541, 2015. a
Priyadarshi, S.: A review of ionospheric scintillation models, Surv. Geophys., 36, 295–324, 2015. a
Qian, L., Burns, A. G., Emery, B. A., Foster, B., Lu, G., Maute, A., Richmond, A. D., Roble, R. G., Solomon, S. C., and Wang, W.: The NCAR TIE-GCM: A community model of the coupled thermosphere/ionosphere system, in: Modeling the ionosphere–thermosphere system, edited by: Huba, J., Schunk, R., and Khazanov, G., 73–83, https://doi.org/10.1002/9781118704417.ch7, 2014. a
Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., and Carvalhais, N.: Deep learning and process understanding for data-driven Earth system science, Nature, 566, 195–204, 2019. a
Resende, L. C. A., Batista, I. S., Denardini, C. M., Batista, P. P., Carrasco, A. J., de Fátima Andrioli, V., and Moro, J.: Simulations of blanketing sporadic E-layer over the Brazilian sector driven by tidal winds, J. Atmos. Sol.-Terr. Phys., 154, 104–114, 2017. a
Roble, R. and Ridley, E.: A thermosphere-ionosphere-mesosphere-electrodynamics general circulation model (TIME-GCM): Equinox solar cycle minimum simulations (30–500 km), Geophys. Res. Lett., 21, 417–420, 1994. a
Rocken, C., Ying-Hwa, K., Schreiner, W. S., Hunt, D., Sokolovskiy, S., and McCormick, C.: COSMIC system description, Terr. Atmos. Ocean. Sci., 11, 21–52, 2000. a
Ruder, S.: An overview of gradient descent optimization algorithms, arXiv [preprint], arXiv:1609.04747, 2016. a
Salcedo-Sanz, S., Ghamisi, P., Piles, M., Werner, M., Cuadra, L., Moreno-Martínez, A., Izquierdo-Verdiguier, E., Muñoz-Marí, J., Mosavi, A., and Camps-Valls, G.: Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources, Inform. Fusion, 63, 256–272, 2020. a
Schmidhuber, J.: Deep learning in neural networks: An overview, Neural Networks, 61, 85–117, 2015. a
Schreiner, W., Rocken, C., Sokolovskiy, S., Syndergaard, S., and Hunt, D.: Estimates of the precision of GPS radio occultations from the COSMIC/FORMOSAT-3 mission, Geophys. Res. Lett., 34, L04808, https://doi.org/10.1029/2006GL027557, 2007. a
Schreiner, W., Sokolovskiy, S., Hunt, D., Rocken, C., and Kuo, Y.-H.: Analysis of GPS radio occultation data from the FORMOSAT-3/COSMIC and Metop/GRAS missions at CDAAC, Atmos. Meas. Tech., 4, 2255–2272, https://doi.org/10.5194/amt-4-2255-2011, 2011. a
Shinagawa, H., Miyoshi, Y., Jin, H., and Fujiwara, H.: Global distribution of neutral wind shear associated with sporadic E layers derived from GAIA, J. Geophys. Res.-Space, 122, 4450–4465, 2017. a
Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., and Hassabis, D.: Mastering the game of go without human knowledge, Nature, 550, 354–359, 2017. a
Simonyan, K. and Zisserman, A.: Very deep convolutional networks for large-scale image recognition, arXiv [preprint], arXiv:1409.1556, 2014. a
Sokolovskiy, S., Schreiner, W., Zeng, Z., Hunt, D., Lin, Y.-C., and Kuo, Y.-H.: Observation, analysis, and modeling of deep radio occultation signals: Effects of tropospheric ducts and interfering signals, Radio Sci., 49, 954–970, 2014. a
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A.: Going deeper with convolutions, in: Proceedings of the IEEE conference on computer vision and pattern recognition, Boston, MA, USA, 1–9, https://doi.org/10.1109/CVPR.2015.7298594, 2015. a
Thayer, J. P. and Semeter, J.: The convergence of magnetospheric energy flux in the polar atmosphere, J. Atmos. Sol.-Terr. Phys., 66, 807–824, 2004. a
Tian, P., Yu, B., Ye, H., Xue, X., Wu, J., and Chen, T.: Ionospheric Irregularities Reconstruction Using Multi-Source Data Fusion via Deep Learning, Zenodo [code], https://doi.org/10.5281/zenodo.10016010, 2023. a
Titheridge, J.: Modelling the peak of the ionospheric E-layer, J. Atmos. Sol.-Terr. Phy., 62, 93–114, 2000. a
Tsai, L.-C., Su, S.-Y., Liu, C.-H., Schuh, H., Wickert, J., and Alizadeh, M. M.: Global morphology of ionospheric sporadic E layer from the FormoSat-3/COSMIC GPS radio occultation experiment, GPS Solutions, 22, 1–12, 2018. a
UCAR COSMIC Program: COSMIC-1 Data Products, UCAR/NCAR – COSMIC [data set], https://doi.org/10.5065/ZD80-KD74, 2022. a
Vadas, S. L. and Liu, H.-l.: Generation of large-scale gravity waves and neutral winds in the thermosphere from the dissipation of convectively generated gravity waves, J. Geophys. Res.-Space, 114, A10310, https://doi.org/10.1029/2009JA014108, 2009. a
Weber, E., Tsunoda, R., Buchau, J., Sheehan, R., Strickland, D., Whiting, W., and Moore, J.: Coordinated measurements of auroral zone plasma enhancements, J. Geophys. Res.-Space, 90, 6497–6513, 1985. a
Whitehead, J.: Production and prediction of sporadic E, Rev. Geophys., 8, 65–144, 1970. a
Wu, J., Feng, W., Liu, H.-L., Xue, X., Marsh, D. R., and Plane, J. M. C.: Self-consistent global transport of metallic ions with WACCM-X, Atmos. Chem. Phys., 21, 15619–15630, https://doi.org/10.5194/acp-21-15619-2021, 2021. a
Ye, H., Xue, X., Yu, T., Sun, Y.-Y., Yi, W., Long, C., Zhang, W., and Dou, X.: Ionospheric F-layer scintillation variabilities over the American sector during sudden stratospheric warming events, Space Weather, 19, e2020SW002703, https://doi.org/10.1029/2020SW002703, 2021. a, b
Ye, H., Yi, W., Zhou, B., Wu, J., Yu, B., Tian, P., Wang, J., Long, C., Lu, M., Xue, X., Chen, T., and Dou, X.: Multi-Instrumental Observations of Midlatitude Plasma Irregularities over Eastern Asia during a Moderate Magnetic Storm on 16 July 2003, Remote Sens.-Basel, 15, 1160, https://doi.org/10.3390/rs15041160, 2023. a, b
Yu, B., Scott, C. J., Xue, X., Yue, X., and Dou, X.: Derivation of global ionospheric Sporadic E critical frequency (fo Es) data from the amplitude variations in GPS/GNSS radio occultations, Roy. Soc. Open Sci., 7, 200320, https://doi.org/10.1098/rsos.200320, 2020. a, b, c, d
Yu, B., Xue, X., Scott, C. J., Wu, J., Yue, X., Feng, W., Chi, Y., Marsh, D. R., Liu, H., Dou, X., and Plane, J. M. C.: Interhemispheric transport of metallic ions within ionospheric sporadic E layers by the lower thermospheric meridional circulation, Atmos. Chem. Phys., 21, 4219–4230, https://doi.org/10.5194/acp-21-4219-2021, 2021b. a, b, c
Yu, B., Xue, X., Scott, C. J., Yue, X., and Dou, X.: An empirical model of the ionospheric sporadic E layer based on GNSS radio occultation data, Space Weather, 20, e2022SW003113, https://doi.org/10.1029/2022SW003113, 2022. a, b, c
Yu, S. and Ma, J.: Deep learning for geophysics: Current and future trends, Rev. Geophys., 59, e2021RG000742, https://doi.org/10.1029/2021RG000742, 2021. a
Zeng, Z. and Sokolovskiy, S.: Effect of sporadic E clouds on GPS radio occultation signals, Geophys. Res. Lett., 37, L18817, https://doi.org/10.1029/2010GL044561, 2010. a
Short summary
Modeling and prediction of ionospheric irregularities is an important topic in upper-atmospheric and upper-ionospheric physics. We proposed an artificial intelligence model to reconstruct the E-region ionospheric irregularities and first developed an open-source application for the community. The model reveals complex relationships between ionospheric irregularities and external driving factors. The findings suggest that spatiotemporal information plays an important role in the reconstruction.
Modeling and prediction of ionospheric irregularities is an important topic in upper-atmospheric...
Altmetrics
Final-revised paper
Preprint