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.
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.
Wei Zhong, Xianghui Xue, Wen Yi, Iain M. Reid, Tingdi Chen, and Xiankang Dou
Atmos. Meas. Tech., 14, 3973–3988, https://doi.org/10.5194/amt-14-3973-2021, https://doi.org/10.5194/amt-14-3973-2021, 2021
Bingkun Yu, Xianghui Xue, Christopher J. Scott, Jianfei Wu, Xinan Yue, Wuhu Feng, Yutian Chi, Daniel R. Marsh, Hanli Liu, Xiankang Dou, and John M. C. Plane
Atmos. Chem. Phys., 21, 4219–4230, https://doi.org/10.5194/acp-21-4219-2021, https://doi.org/10.5194/acp-21-4219-2021, 2021
Short summary
Short summary
A long-standing mystery of metal ions within Es layers in the Earth's upper atmosphere is the marked seasonal dependence, with a summer maximum and a winter minimum. We report a large-scale winter-to-summer transport of metal ions from 6-year multi-satellite observations and worldwide ground-based stations. A global atmospheric circulation is responsible for the phenomenon. Our results emphasise the effect of this atmospheric circulation on the transport of composition in the upper atmosphere.
Jianyuan Wang, Wen Yi, Jianfei Wu, Tingdi Chen, Xianghui Xue, Robert A. Vincent, Iain M. Reid, Paulo P. Batista, Ricardo A. Buriti, Toshitaka Tsuda, and Xiankang Dou
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2021-33, https://doi.org/10.5194/acp-2021-33, 2021
Revised manuscript not accepted
Short summary
Short summary
In this study, we report the climatology of migrating and non-migrating tides in mesopause winds estimated using multiyear observations from three meteor radars in the southern equatorial region. The results reveal that the climatological patterns of tidal amplitudes by meteor radars is similar to the Climatological Tidal Model of the Thermosphere (CTMT) results and the differences are mainly due to the effect of the stratospheric sudden warming (SSW) event.
Mingjiao Jia, Jinlong Yuan, Chong Wang, Haiyun Xia, Yunbin Wu, Lijie Zhao, Tianwen Wei, Jianfei Wu, Lu Wang, Sheng-Yang Gu, Liqun Liu, Dachun Lu, Rulong Chen, Xianghui Xue, and Xiankang Dou
Atmos. Chem. Phys., 19, 15431–15446, https://doi.org/10.5194/acp-19-15431-2019, https://doi.org/10.5194/acp-19-15431-2019, 2019
Short summary
Short summary
Gravitational waves (GWs) with periods ranging from 10 to 30 min over 10 h and 20 wave cycles are detected within a 2 km height in the atmospheric boundary layer (ABL) by a coherent Doppler wind lidar. Observations and computational fluid dynamics (CFD) simulations lead to a conclusion that the GWs are excited by the wind shear of a low-level jet under the condition of light horizontal wind. The GWs are trapped in the ABL due to a combination of thermal and Doppler ducts.
Chong Wang, Mingjiao Jia, Haiyun Xia, Yunbin Wu, Tianwen Wei, Xiang Shang, Chengyun Yang, Xianghui Xue, and Xiankang Dou
Atmos. Meas. Tech., 12, 3303–3315, https://doi.org/10.5194/amt-12-3303-2019, https://doi.org/10.5194/amt-12-3303-2019, 2019
Short summary
Short summary
To investigate the relationship between BLH and air pollution under different conditions, a compact micro-pulse lidar integrating both direct-detection lidar and coherent Doppler wind lidar is built. Evolution of atmospheric boundary layer height (BLH), aerosol layer and fine structure in cloud base are well retrieved. Negative correlation exists between BLH and PM2.5. Different trends show that the relationship between PM2.5 and BLH should be considered in different boundary layer categories.
Wen Yi, Xianghui Xue, Iain M. Reid, Damian J. Murphy, Chris M. Hall, Masaki Tsutsumi, Baiqi Ning, Guozhu Li, Robert A. Vincent, Jinsong Chen, Jianfei Wu, Tingdi Chen, and Xiankang Dou
Atmos. Chem. Phys., 19, 7567–7581, https://doi.org/10.5194/acp-19-7567-2019, https://doi.org/10.5194/acp-19-7567-2019, 2019
Short summary
Short summary
The seasonal variations in the mesopause densities, especially with regard to its global structure, are still unclear. In this study, we report the climatology of the mesopause density estimated using multiyear observations from nine meteor radars from Arctic to Antarctic latitudes. The results reveal a significant AO and SAO in mesopause density, an asymmetry between the two polar regions and evidence of intraseasonal oscillations (ISOs), perhaps associated with the ISOs of the troposphere.
Bingkun Yu, Xianghui Xue, Xin'an Yue, Chengyun Yang, Chao Yu, Xiankang Dou, Baiqi Ning, and Lianhuan Hu
Atmos. Chem. Phys., 19, 4139–4151, https://doi.org/10.5194/acp-19-4139-2019, https://doi.org/10.5194/acp-19-4139-2019, 2019
Short summary
Short summary
It reports the long-term climatology of the intensity of Es layers from COSMIC satellites. The global Es maps present high-resolution spatial distributions and seasonal dependence. It mainly occurs at mid-latitudes and polar regions. Based on wind shear theory, simulation results indicate the convergence of vertical ion velocity could partially explain the Es seasonal dependence and some disagreements between observations and simulations suggest other processes play roles in the Es variations.
Bingkun Yu, Xianghui Xue, Chengling Kuo, Gaopeng Lu, Xiankang Dou, Qi Gao, Jianfei Wu, Mingjiao Jia, Chao Yu, and Xiushu Qie
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2018-1025, https://doi.org/10.5194/acp-2018-1025, 2018
Preprint withdrawn
Short summary
Short summary
This paper explores the relationship between the intensifications of atomic sodium layer and Es layer in the Mesosphere/Lower Thermosphere (MLT) region (the earth's upper atmosphere at altitudes between 90 and 130 km above ground). The multi-instrument experiment of sodium lidar observations, ionospheric observations and sodium chemical simulations advances our understanding of the dynamical and chemical coupling processes in the mesosphere and ionosphere above thunderstorms.
X. Yue, W. S. Schreiner, Z. Zeng, Y.-H. Kuo, and X. Xue
Atmos. Meas. Tech., 8, 225–236, https://doi.org/10.5194/amt-8-225-2015, https://doi.org/10.5194/amt-8-225-2015, 2015
Short summary
Short summary
The occurrence of sporadic E (Es) layers has been a hot scientific topic for a long time. GNSS (global navigation satellite system)-based radio occultation (RO) has proven to be a powerful technique for detecting the global Es layers. In this paper, we show some examples of multiple Es layers occurring in one RO event and the occurrence of Es in a broad region during a certain time interval. The results are then evaluated by independent observations such as lidar and ionosondes.
Related subject area
Subject: Climate and Earth System | Research Activity: Remote Sensing | Altitude Range: Mesosphere | Science Focus: Physics (physical properties and processes)
The impact of quasi-biennial oscillation (QBO) disruptions on diurnal tides over the low- and mid-latitude mesosphere and lower thermosphere (MLT) region observed by a meteor radar chain
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.
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