Abstract
The uneven distribution of GNSS observations and the scarcity of satellite data pose challenges for the continuous detection of traveling ionospheric disturbances (TIDs) induced by gravity waves (GWs). This paper proposes a short-term TID forecasting method based on the convolutional long short-term memory (ConvLSTM) neural network. This method utilizes 3-D electron density disturbance information from 3-dimensional computerized ionospheric tomography (3DCIT) results to predict the 3-D characteristics of ionospheric disturbances during Hurricane Matthew. A multi-channel forecasting pattern combining disturbance feature labels is used to improve the forecast accuracy. The results show that the ConvLSTM network can effectively identify the spatiotemporal features of TIDs. The inclusion of the labels greatly enhances the forecast performance, as shown by the reduction of the mean absolute error (MAE) and root mean square error (RMSE) over longer forecast durations, along with the increase in correlation. Additionally, the horizontal phase velocities of the TIDs at 200 and 300 km altitudes calculated using the 5-minute forecast results (∼150-173.33 m/s) are comparable to the original 3DCIT estimation (∼166.67-170 m/s).
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Artru, J., Lognonné, P., Occhipinti, G., et al.: Tsunami detection in the ionosphere. Space Res. Today 163, 23–27 (2005). https://doi.org/10.1016/s0045-8732(05)80048-8
Azeem, I., Vadas, S.L., Crowley, G., et al.: Traveling ionospheric disturbances over the United States induced by gravity waves from the 2011 Tohoku tsunami and comparison with gravity wave dissipative theory. J. Geophys. Res. Space Phys. 122(3), 3430–3447 (2017). https://doi.org/10.1002/2016ja023659
Ban, P.-P., Sun, S.-J., Chen, C., et al.: Forecasting of low-latitude storm-time ionospheric foF2 using support vector machine. Radio Sci. 46(6) (2011). https://doi.org/10.1029/2010rs004633
Boulch, A., Cherrier, N., Castaings, T.: Ionospheric activity prediction using convolutional recurrent neural networks (2018). https://doi.org/10.48550/arXiv.1810.13273
Chen, C., Wu, Z.-S., Ban, P.-P., et al.: Diurnal specification of the ionosphericf0F2parameter using a support vector machine. Radio Sci. 45(5), n/a–n/a (2010). https://doi.org/10.1029/2010rs004393
Chen, Z., Liao, W., Li, H., et al.: Prediction of Global Ionospheric TEC Based on Deep Learning. Space Weather 20(4) (2022C). https://doi.org/10.1029/2021sw002854
Chen, Y., Yue, D., Zhai, C., et al.: Detection and three-dimensional reconstruction of concentric traveling ionosphere disturbances induced by hurricane matthew on 7 October 2016. J. Geophys. Res. Space Phys. 127(12) (2022b). https://doi.org/10.1029/2022ja030690
Chen, J., Zhi, N., Liao, H., et al.: Global forecasting of ionospheric vertical total electron contents via ConvLSTM with spectrum analysis. GPS Solut. 26(3) (2022a). https://doi.org/10.1007/s10291-022-01253-z
Chou, M.Y., Lin, C.C.H., Yue, J., et al.: Medium-scale traveling ionospheric disturbances triggered by super typhoon nepartak (2016). Geophys. Res. Lett. 44(15), 7569–7577 (2017). https://doi.org/10.1002/2017gl073961
de Paulo, M.C.M., Marques, H.A., Feitosa, R.Q., et al.: New encoder–decoder convolutional LSTM neural network architectures for next-day global ionosphere maps forecast. GPS Solut. 27(2) (2023). https://doi.org/10.1007/s10291-023-01442-4
Elmunim, N.A., Abdullah, M., Hasbi, A.M., et al.: Comparison of GPS TEC variations with Holt-Winter method and IRI-2012 over Langkawi, Malaysia. Adv. Space Res. 60(2), 276–285 (2017). https://doi.org/10.1016/j.asr.2016.07.025
Fritts, D.C., Laughman, B., Wang, L., et al.: Gravity wave dynamics in a mesospheric inversion layer: 1. Reflection, trapping, and instability dynamics. J. Geophys. Res., Atmos. 123(2), 626–648 (2018). https://doi.org/10.1002/2017JD027440
Fritts, D.C., Dong, W., Lund, T.S., et al.: Self-acceleration and instability of gravity wave packets: 3. Three-dimensional packet propagation, secondary gravity waves, momentum transport, and transient mean forcing in tidal winds. J. Geophys. Res., Atmos. 125(3), e2019JD030692 (2020). https://doi.org/10.1029/2019JD030692
Gao, X., Yao, Y.: A storm-time ionospheric TEC model with multichannel features by the spatiotemporal ConvLSTM network. J. Geod. 97(1) (2023). https://doi.org/10.1007/s00190-022-01696-9
Habarulema, J.B., McKinnell, L.-A., Cilliers, P.J.: Prediction of global positioning system total electron content using neural networks over South Africa. J. Atmos. Sol.-Terr. Phys. 69(15), 1842–1850 (2007). https://doi.org/10.1016/j.jastp.2007.09.002
Habarulema, J.B., McKinnell, L.-A., Opperman, B.D.L.: Regional GPS TEC modeling; Attempted spatial and temporal extrapolation of TEC using neural networks. J. Geophys. Res. Space Phys. 116(A4) (2011). https://doi.org/10.1029/2010ja016269
Hoffmann, L., Alexander, M.J.: Occurrence frequency of convective gravity waves during the North American thunderstorm season. J. Geophys. Res., Atmos. 115(D20) (2010). https://doi.org/10.1029/2010jd014401
Huba, J.D., Becker, E., Vadas, S.L.: Simulation study of the 15 January 2022 Tonga event: Development of super equatorial plasma bubbles. Geophys. Res. Lett. 50(1) (2023). https://doi.org/10.1029/2022gl101185
Kong, J., Yao, Y., Zhou, C., et al.: Tridimensional reconstruction of the co-seismic ionospheric disturbance around the time of 2015 Nepal earthquake. J. Geod. 92(11), 1255–1266 (2018). https://doi.org/10.1007/s00190-018-1117-3
Krankowski, A., Kosek, W., Baran, L.W., et al.: Wavelet analysis and forecasting of VTEC obtained with GPS observations over European latitudes. J. Atmos. Sol.-Terr. Phys. 67(12), 1147–1156 (2005). https://doi.org/10.1016/j.jastp.2005.03.004
Lin, J.T., Rajesh, P.K., Lin, C.C.H., et al.: Rapid conjugate appearance of the giant ionospheric lamb wave signatures in the northern hemisphere after Hunga-Tonga Volcano Eruptions. Geophys. Res. Lett. 49(8) (2022). https://doi.org/10.1029/2022gl098222
Liu, L., Zou, S., Yao, Y., et al.: Forecasting global ionospheric TEC using deep learning approach. Space Weather 18(11) (2020). https://doi.org/10.1029/2020sw002501
Liu, L., Morton, Y.J., Liu, Y.: Machine learning prediction of storm-time high-latitude ionospheric irregularities from GNSS-derived ROTI maps. Geophys. Res. Lett. 48(20) (2021). https://doi.org/10.1029/2021gl095561
Liu, H.L., Wang, W., Huba, J.D., et al.: Atmospheric and Ionospheric responses to Hunga-Tonga volcano eruption simulated by WACCM-X. Geophys. Res. Lett. 50(10) (2023). https://doi.org/10.1029/2023gl103682
Mai, C.-L., Kiang, J.-F.: Reconstruction of ionospheric perturbation induced by 2004 Sumatra tsunami using a computerized tomography technique. IEEE Trans. Geosci. Remote Sens. 47(10), 3303–3312 (2009). https://doi.org/10.1109/tgrs.2009.2021261
Meng, X., Verkhoglyadova, O.P., Komjathy, A., et al.: Physics-based modeling of earthquake-induced ionospheric disturbances. J. Geophys. Res. Space Phys. 123(9), 8021–8038 (2018). https://doi.org/10.1029/2018ja025253
Nishioka, M., Tsugawa, T., Kubota, M., et al.: Concentric waves and short-period oscillations observed in the ionosphere after the 2013 Moore EF5 tornado. Geophys. Res. Lett. 40(21), 5581–5586 (2013). https://doi.org/10.1002/2013gl057963
Orus Perez, R.: Using TensorFlow-based neural network to estimate GNSS single frequency ionospheric delay (IONONet). Adv. Space Res. 63(5), 1607–1618 (2019). https://doi.org/10.1016/j.asr.2018.11.011
Roberts, D.H., Klobuchar, J.A., Fougere, P.F., et al.: A large-amplitude traveling ionospheric disturbance produced by the May 18, 1980, explosion of Mount St. Helens. J. Geophys. Res. 87(A8) (1982). https://doi.org/10.1029/JA087iA08p06291
Rolland, L.M., Occhipinti, G., Lognonné, P., et al.: Ionospheric gravity waves detected offshore Hawaii after tsunamis. Geophys. Res. Lett. 37(17), n/a–n/a (2010). https://doi.org/10.1029/2010gl044479
Rolland, L.M., Lognonné, P., Astafyeva, E., et al.: The resonant response of the ionosphere imaged after the 2011 off the Pacific coast of Tohoku Earthquake. Earth Planets Space 63(7), 853–857 (2011). https://doi.org/10.5047/eps.2011.06.020
Shi, X., Chen, Z., Wang, H., et al.: In: Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting, vol. 1, pp. 802–810 (2015). https://doi.org/10.48550/arXiv.1506.04214
Shults, K., Astafyeva, E., Adourian, S.: Ionospheric detection and localization of volcano eruptions on the example of the April 2015 Calbuco events. J. Geophys. Res. Space Phys. 121(10) (2016). https://doi.org/10.1002/2016ja023382
Song, R., Zhang, X., Zhou, C., et al.: Predicting TEC in China based on the neural networks optimized by genetic algorithm. Adv. Space Res. 62(4), 745–759 (2018). https://doi.org/10.1016/j.asr.2018.03.043
Song, Q., Ding, F., Zhang, X.X., et al.: Medium-scale traveling ionospheric disturbances induced by Typhoon Chan-hom over China. J. Geophys. Res. Space Phys. 124(3), 2223–2237 (2019). https://doi.org/10.1029/2018ja026152
Ssessanga, N., Kim, Y.H., Kim, E.: Vertical structure of medium-scale traveling ionospheric disturbances. Geophys. Res. Lett. 42(21), 9156–9165 (2015). https://doi.org/10.1002/2015gl066093
Ssessanga, N., Yamamoto, M., Saito, S., et al.: Complementing regional ground GNSS-STEC computerized ionospheric tomography (CIT) with ionosonde data assimilation. GPS Solut. 25(3) (2021). https://doi.org/10.1007/s10291-021-01133-y
Tang, L.: Ionospheric disturbances of the January 15, 2022, Tonga volcanic eruption observed using the GNSS network in New Zealand. GPS Solut. 27(1) (2023). https://doi.org/10.1007/s10291-023-01395-8
Tang, L., Li, Z., Zhou, B.: Large-area tsunami signatures in ionosphere observed by GPS TEC after the 2011 Tohoku earthquake. GPS Solut. 22(4) (2018). https://doi.org/10.1007/s10291-018-0759-1
Tulunay, E., Senalp, E.T., Radicella, S.M., et al.: Forecasting total electron content maps by neural network technique. Radio Sci. 41(4) (2006). https://doi.org/10.1029/2005rs003285
Vadas, S.L.: Horizontal and vertical propagation and dissipation of gravity waves in the thermosphere from lower atmospheric and thermospheric sources. J. Geophys. Res. Space Phys. 112(A6) (2007). https://doi.org/10.1029/2006ja011845
Vadas, S.L., Azeem, I.: Concentric Secondary Gravity Waves in the Thermosphere and Ionosphere over the Continental United States on March 25–26, 2015 from Deep Convection. J. Geophys. Res. Space Phys. 126(2) (2021). https://doi.org/10.1029/2020ja028275
Vadas, S.L., Crowley, G.: Sources of the traveling ionospheric disturbances observed by the ionospheric TIDDBIT sounder near Wallops Island on 30 October 2007. J. Geophys. Res. Space Phys. 115(A7) (2010). https://doi.org/10.1029/2009ja015053
Vadas, S.L., Liu, H.L.: Numerical modeling of the large-scale neutral and plasma responses to the body forces created by the dissipation of gravity waves from 6 h of deep convection in Brazil. J. Geophys. Res. Space Phys. 118(5), 2593–2617 (2013). https://doi.org/10.1002/jgra.50249
Vadas, S., Yue, J., Nakamura, T.: Mesospheric concentric gravity waves generated by multiple convective storms over the North American Great Plain. J. Geophys. Res., Atmos. 117 (2012). https://doi.org/10.1029/2011jd017025
Wang, R., Zhou, C., Deng, Z., et al.: Predicting foF2 in the China region using the neural networks improved by the genetic algorithm. J. Atmos. Sol.-Terr. Phys. 92, 7–17 (2013). https://doi.org/10.1016/j.jastp.2012.09.010
Xie, T., Dai, Z., Zhu, X., et al.: LSTM-based short-term ionospheric TEC forecast model and positioning accuracy analysis. GPS Solut. 27(2) (2023). https://doi.org/10.1007/s10291-023-01406-8
Xiong, P., Zhai, D., Long, C., et al.: Long Short-Term Memory Neural Network for Ionospheric Total Electron Content Forecasting over China. Space Weather 19(4) (2021). https://doi.org/10.1029/2020sw002706
Xu, S., Yue, J., Xue, X.H., et al.: Dynamical coupling between Hurricane Matthew and the middle to upper atmosphere via gravity waves. J. Geophys. Res. Space Phys. 124(5), 3589–3608 (2019). https://doi.org/10.1029/2018ja026453
Yue, J., Vadas, S.L., She, C.-Y., et al.: Concentric gravity waves in the mesosphere generated by deep convective plumes in the lower atmosphere near Fort Collins, Colorado. J. Geophys. Res. 114(D6) (2009). https://doi.org/10.1029/2008jd011244
Zewdie, G.K., Valladares, C., Cohen, M.B., et al.: Data-driven forecasting of low-latitude ionospheric total electron content using the random forest and LSTM machine learning methods. Space Weather 19(6) (2021). https://doi.org/10.1029/2020sw002639
Zhai, C., Lu, G., Yao, Y., et al.: 3-D tomographic reconstruction of SED plume during 17 March 2013 storm. J. Geophys. Res. Space Phys. 125(11) (2020). https://doi.org/10.1029/2020ja028257
Zhai, C., Yao, Y., Kong, J.: Three-dimensional reconstruction of seismo-traveling ionospheric disturbances after March 11, 2011, Japan Tohoku earthquake. J. Geod. 95(7) (2021). https://doi.org/10.1007/s00190-021-01533-5
Zhao, B., Hao, Y.: Ionospheric and geomagnetic disturbances caused by the 2008 Wenchuan earthquake: a revisit. J. Geophys. Res. Space Phys. 120(7), 5758–5777 (2015). https://doi.org/10.1002/2015ja021035
Zheng, D., Yao, Y., Nie, W., et al.: A new three-dimensional computerized ionospheric tomography model based on a neural network. GPS Solut. 25(1) (2020). https://doi.org/10.1007/s10291-020-01047-1
Acknowledgements
This research was supported by the National Natural Science Foundation of China (42104009), China Postdoctoral Science Foundation (2022M720988), Fundamental Research Funds for the Central Universities (B230201012). The authors gratefully acknowledge the CEDAR Madrigal open database for GNSS line-of-sights and the National Hurricane Center (NHC) for track data of Hurricane Matthew. The GNSS line-of-sight TEC can be accessed from CEDAR Madrigal Database via http://cedar.openmadrigal.org, and the track of Hurricane Matthew is available from National Hurricane Center (NHC) at https://www.nhc.noaa.gov.
Author information
Authors and Affiliations
Contributions
Y.C. and D.Y. contributed to the conception of the study. Y.C. and C.Z. programmed and verified the model. Y.C. and D.Y. contributed significantly to the data analysis and manuscript preparation. All authors reviewed the manuscript.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Chen, Y., Yue, D. & Zhai, C. Forecasting 3-dimensional ionospheric disturbances during Hurricane Matthew using ConvLSTM neural network. Astrophys Space Sci 368, 99 (2023). https://doi.org/10.1007/s10509-023-04258-3
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s10509-023-04258-3