View Item 

Show simple item record

Forecast Global Ionospheric TEC: Apply Modified U-Net on VISTA TEC Data Set
dc.contributor.authorWang, Zihan
dc.contributor.authorZou, Shasha
dc.contributor.authorSun, Hu
dc.contributor.authorChen, Yang
dc.date.accessioned2023-09-06T00:44:37Z
dc.date.available2024-09-05 20:44:34en
dc.date.available2023-09-06T00:44:37Z
dc.date.issued2023-08
dc.identifier.citationWang, Zihan; Zou, Shasha; Sun, Hu; Chen, Yang (2023). "Forecast Global Ionospheric TEC: Apply Modified U-Net on VISTA TEC Data Set." Space Weather 21(8): n/a-n/a.
dc.identifier.issn1542-7390
dc.identifier.issn1542-7390
dc.identifier.urihttps://hdl.handle.net/2027.42/177572
dc.description.abstractThis work developed a modified U-Net model (a convolutional network architecture) to predict global Total Electron Content (TEC) maps. The input includes the current global TEC map, the current F10.7, the time history of the Interplanetary Magnetic Field Bz and SYM-H in the previous 4 days, the Hour of Day, and the Day of Year. The output is the global TEC map several hours or several days ahead. The modified U-Net was trained and validated on a brand new TEC database, the VISTA (Video Imputation with SoftImpute, Temporal smoothing and Auxiliary data) TEC database. The VISTA TEC maps can reveal important large-scale TEC structures and preserve mesoscale structures simultaneously. Taking advantage of the new neural network and the new database, our model achieves an root of the mean squared error from 1.2 TECU to 2.4 TECU as the prediction horizon increases from 1 hr to 7 days. In addition, the model could reveal multiscale structures in the predicted TEC maps.Plain Language SummaryThe ionospheric Total Electron Content (TEC) is the total number of electrons present along a path between a radio transmitter and a receiver. An accurate prediction of global ionospheric TEC is important for Global Navigation Satellite System positioning, navigation, and timing services, high-frequency radio communications, and other satellite communications. In this work, a new convolutional neural network was developed to forecast global TEC maps. Our new model not only has promising performance compared with state-of-the-art TEC prediction models but also includes multiscale structures. Our model can greatly mitigate the impacts of ionospheric space weather on our technological society.Key PointsA modified U-Net model was developed to forecast global Total Electron Content (TEC) mapsThe new model was trained and validated with the Video Imputation with SoftImpute, Temporal smoothing and Auxiliary data TEC databaseThe new model can reproduce multi-scale TEC structures
dc.publisherUniversity of Michigan
dc.publisherWiley Periodicals, Inc.
dc.subject.otherspace weather
dc.subject.otherTEC
dc.subject.otherionosphere
dc.subject.othermachine learning
dc.titleForecast Global Ionospheric TEC: Apply Modified U-Net on VISTA TEC Data Set
dc.typeArticle
dc.rights.robotsIndexNoFollow
dc.subject.hlbsecondlevelElectrical Engineering
dc.subject.hlbtoplevelEngineering
dc.description.peerreviewedPeer Reviewed
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/177572/1/swe21567.pdf
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/177572/2/swe21567_am.pdf
dc.identifier.doi10.1029/2023SW003494
dc.identifier.sourceSpace Weather
dc.identifier.citedreferenceTang, M., Liu, Y., & Durlofsky, L. J. ( 2020 ). A deep-learning-based surrogate model for data assimilation in dynamic subsurface flow problems. Journal of Computational Physics, 413, 109456. https://doi.org/10.1016/j.jcp.2020.109456
dc.identifier.citedreferenceLi, Z., Wang, N., Liu, A., Yuan, Y., Wang, L., Hernández-Pajares, M., et al. ( 2021 ). Status of CAS global ionospheric maps after the maximum of solar cycle 24. Satellite Navigation, 2, 1 – 15. https://doi.org/10.1186/S43020-021-00050-2/TABLES/5
dc.identifier.citedreferenceLiu, J., Zhao, B., & Liu, L. ( 2010 ). Time delay and duration of ionospheric total electron content responses to geomagnetic disturbances. Annales Geophysicae, 28 ( 3 ), 795 – 805. https://doi.org/10.5194/ANGEO-28-795-2010
dc.identifier.citedreferenceRideout, W., & Coster, A. ( 2006 ). Automated GPS processing for global total electron content data. GPS Solutions, 10 ( 3 ), 219 – 228. https://doi.org/10.1007/S10291-006-0029-5/FIGURES/6
dc.identifier.citedreferenceRidley, A. J., Deng, Y., & Tóth, G. ( 2006 ). The global ionosphere-thermosphere model. Journal of Atmospheric and Solar-Terrestrial Physics, 68 ( 8 ), 839 – 864. https://doi.org/10.1016/j.jastp.2006.01.008
dc.identifier.citedreferenceRoma-Dollase, D., Hernández-Pajares, M., Krankowski, A., Kotulak, K., Ghoddousi-Fard, R., Yuan, Y., et al. ( 2018 ). Consistency of seven different GNSS global ionospheric mapping techniques during one solar cycle. Journal of Geodesy, 92 ( 6 ), 691 – 706. https://doi.org/10.1007/S00190-017-1088-9/TABLES/4
dc.identifier.citedreferenceRonneberger, O., Fischer, P., & Brox, T. ( 2015 ). U-net: Convolutional networks for biomedical image segmentation. In International conference on medical image computing and computer-assisted intervention (pp. 234 – 241 ).
dc.identifier.citedreferenceSong, R., Zhang, X., Zhou, C., Liu, J., & He, J. ( 2018 ). Predicting TEC in China based on the neural networks optimized by genetic algorithm. Advances in Space Research, 62 ( 4 ), 745 – 759. https://doi.org/10.1016/J.ASR.2018.03.043
dc.identifier.citedreferenceSrebro, N., Rennie, J., & Jaakkola, T. S. ( 2005 ). Maximum-margin matrix factorization. In Advances in neural information processing systems (pp. 1329 – 1336 ).
dc.identifier.citedreferenceSun, H., Chen, Y., Zou, S., Ren, J., Chang, Y., Wang, Z., & Coster, A. ( 2023 ). Complete global total electron content map dataset based on a video imputation algorithm vista. Scientific Data, 10 ( 1 ), 236. https://doi.org/10.1038/s41597-023-02138-7
dc.identifier.citedreferenceSun, H., Hua, Z., Ren, J., Zou, S., Sun, Y., & Chen, Y. ( 2022 ). Matrix completion methods for the total electron content video reconstruction. Annals of Applied Statistics, 16 ( 3 ), 1333 – 1358. https://doi.org/10.1214/21-aoas1541
dc.identifier.citedreferenceSun, H., Ren, J., Chen, Y., Zou, S., Chang, Y., Wang, Z., & Coster, A. ( 2023 ). Vista TEC database (version 2.0) [Dataset]. https://doi.org/10.7302/jab6-2911
dc.identifier.citedreferenceTang, J., Li, Y., Yang, D., & Ding, M. ( 2022 ). An approach for predicting global ionospheric TEC using machine learning. Remote Sensing, 14 ( 7 ), 1585. https://doi.org/10.3390/RS14071585
dc.identifier.citedreferenceTebabal, A., Radicella, S. M., Nigussie, M., Damtie, B., Nava, B., & Yizengaw, E. ( 2018 ). Local TEC modelling and forecasting using neural networks. Journal of Atmospheric and Solar-Terrestrial Physics, 172, 143 – 151. https://doi.org/10.1016/J.JASTP.2018.03.004
dc.identifier.citedreferenceTebabal, A., Radicella, S. M., Damtie, B., Migoya-Orue, Y., Nigussie, M., & Nava, B. ( 2019 ). Feed forward neural network based ionospheric model for the East African region. Journal of Atmospheric and Solar-Terrestrial Physics, 191, 105052. https://doi.org/10.1016/J.JASTP.2019.05.016
dc.identifier.citedreferenceWang, Z. ( 2021 ). Dynamics and drivers of multi-scale storm-time ionospheric density structures. (Doctoral dissertation). University of Michigan. Deep Blue Documents.
dc.identifier.citedreferenceWang, Z., Zou, S., Coppeans, T., Ren, J., Ridley, A., & Gombosi, T. ( 2019 ). Segmentation of SED by boundary flows associated with westward drifting partial ring current. Geophysical Research Letters, 46 ( 14 ), 7920 – 7928. https://doi.org/10.1029/2019GL084041
dc.identifier.citedreferenceWang, Z., Zou, S., Liu, L., Ren, J., & Aa, E. ( 2021 ). Hemispheric asymmetries in the mid-latitude ionosphere during the September 7–8, 2017 storm: Multi-instrument observations. Journal of Geophysical Research: Space Physics, 126 ( 4 ), e2020JA028829. https://doi.org/10.1029/2020JA028829
dc.identifier.citedreferenceWanliss, J. A., & Showalter, K. M. ( 2006 ). High-resolution global storm index: Dst versus SYM-H. Journal of Geophysical Research, 111 ( A2 ), A02202. https://doi.org/10.1029/2005JA011034
dc.identifier.citedreferenceXia, G., Liu, M., Zhang, F., & Zhou, C. ( 2022 ). CAiTST: Conv-attentional image time sequence transformer for ionospheric TEC maps forecast. Remote Sensing, 14 ( 17 ), 4223. https://doi.org/10.3390/rs14174223
dc.identifier.citedreferenceXia, G., Liu, Y., Wei, T., Wang, Z., Huang, W., Du, Z., et al. ( 2021 ). Ionospheric TEC forecast model based on support vector machine with GPU acceleration in the China region. Advances in Space Research, 68 ( 3 ), 1377 – 1389. https://doi.org/10.1016/J.ASR.2021.03.021
dc.identifier.citedreferenceXia, G., Zhang, F., Wang, C., & Zhou, C. ( 2022 ). ED-ConvLSTM: A novel global ionospheric total electron content medium-term forecast model. Space Weather, 20 ( 8 ), e2021SW002959. https://doi.org/10.1029/2021SW002959
dc.identifier.citedreferenceXiong, C., Lühr, H., & Yamazaki, Y. ( 2019 ). An opposite response of the low-latitude ionosphere at Asian and American sectors during storm recovery phases: Drivers from below or above. Journal of Geophysical Research: Space Physics, 124 ( 7 ), 6266 – 6280. https://doi.org/10.1029/2019JA026917
dc.identifier.citedreferenceYang, D., Li, Q., Fang, H., & Liu, Z. ( 2022 ). One day ahead prediction of global TEC using Pix2pixhd. Advances in Space Research, 70 ( 2 ), 402 – 410. https://doi.org/10.1016/J.ASR.2022.03.038
dc.identifier.citedreferenceYounas, W., Khan, M., Amory-Mazaudier, C., Amaechi, P. O., & Fleury, R. ( 2022 ). Middle and low latitudes hemispheric asymmetries in ∑ O/N2 and TEC during intense magnetic storms of solar cycle 24. Advances in Space Research, 69 ( 1 ), 220 – 235. https://doi.org/10.1016/J.ASR.2021.10.027
dc.identifier.citedreferenceZernov, N. N., Gherm, V. E., & Strangeways, H. J. ( 2009 ). On the effects of scintillation of low-latitude bubbles on transionospheric paths of propagation. Radio Science, 44 ( 1 ). https://doi.org/10.1029/2008RS004074
dc.identifier.citedreferenceAa, E., Huang, W., Liu, S., Ridley, A., Zou, S., Shi, L., et al. ( 2018 ). Midlatitude plasma bubbles over China and adjacent areas during a magnetic storm on 8 September 2017. Space Weather, 16 ( 3 ), 321 – 331. https://doi.org/10.1002/2017SW001776
dc.identifier.citedreferenceAa, E., Zou, S., Ridley, A., Zhang, S., Coster, A. J., Erickson, P. J., et al. ( 2019 ). Merging of storm time midlatitude traveling ionospheric disturbances and equatorial plasma bubbles. Space Weather, 17 ( 2 ), 285 – 298. https://doi.org/10.1029/2018SW002101
dc.identifier.citedreferenceBilitza, D., Altadill, D., Truhlik, V., Shubin, V., Galkin, I., Reinisch, B., & Huang, X. ( 2017 ). International reference ionosphere 2016: From ionospheric climate to real-time weather predictions. Space Weather, 15 ( 2 ), 418 – 429. https://doi.org/10.1002/2016SW001593
dc.identifier.citedreferenceCandès, E. J., & Tao, T. ( 2010 ). The power of convex relaxation: Near-optimal matrix completion. IEEE Transactions on Information Theory, 56 ( 5 ), 2053 – 2080. https://doi.org/10.1109/tit.2010.2044061
dc.identifier.citedreferenceCesaroni, C., Spogli, L., Aragon-Angel, A., Fiocca, M., Dear, V., Franceschi, G. D., & Romano, V. ( 2020 ). Neural network based model for global total electron content forecasting. Journal of Space Weather and Space Climate, 10, 11. https://doi.org/10.1051/SWSC/2020013
dc.identifier.citedreferenceChen, Z., Jin, M., Deng, Y., Wang, J. S., Huang, H., Deng, X., & Huang, C. M. ( 2019 ). Improvement of a deep learning algorithm for total electron content maps: Image completion. Journal of Geophysical Research: Space Physics, 124 ( 1 ), 790 – 800. https://doi.org/10.1029/2018JA026167
dc.identifier.citedreferenceChen, Z., Liao, W., Li, H., Wang, J., Deng, X., & Hong, S. ( 2022 ). Prediction of global ionospheric TEC based on deep learning. Space Weather, 20 ( 4 ), e2021SW002854. https://doi.org/10.1029/2021SW002854
dc.identifier.citedreferenceFerreira, A. A., Borges, R. A., Paparini, C., Ciraolo, L., & Radicella, S. M. ( 2017 ). Short-term estimation of GNSS TEC using a neural network model in Brazil. Advances in Space Research, 60 ( 8 ), 1765 – 1776. https://doi.org/10.1016/J.ASR.2017.06.001
dc.identifier.citedreferenceHan, Y., Wang, L., Fu, W., Zhou, H., Li, T., & Chen, R. ( 2021 ). Machine learning-based short-term GPS TEC forecasting during high solar activity and magnetic storm periods. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 15, 115 – 126. https://doi.org/10.1109/JSTARS.2021.3132049
dc.identifier.citedreferenceHastie, T., Mazumder, R., Lee, J. D., & Zadeh, R. ( 2015 ). Matrix completion and low-rank SVD via fast alternating least squares. Journal of Machine Learning Research, 16 ( 1 ), 3367 – 3402.
dc.identifier.citedreferenceHernández-Pajares, M., Juan, J. M., Sanz, J., Orus, R., Garcia-Rigo, A., Feltens, J., et al. ( 2009 ). The IGS VTEC maps: A reliable source of ionospheric information since 1998. Journal of Geodesy, 83 ( 3–4 ), 263 – 275. https://doi.org/10.1007/S00190-008-0266-1
dc.identifier.citedreferenceHernández-Pajares, M., Juan, J. M., Sanz, J., & Solé, J. G. ( 1998 ). Global observation of the ionospheric electronic response to solar events using ground and LEO GPS data. Journal of Geophysical Research, 103 ( A9 ), 20789 – 20796. https://doi.org/10.1029/98JA01272
dc.identifier.citedreferenceHuang, Z., Li, Q. B., & Yuan, H. ( 2015 ). Forecasting of ionospheric vertical TEC 1-h ahead using a genetic algorithm and neural network. Advances in Space Research, 55 ( 7 ), 1775 – 1783. https://doi.org/10.1016/J.ASR.2015.01.026
dc.identifier.citedreferenceHuang, Z., & Yuan, H. ( 2014 ). Ionospheric single-station TEC short-term forecast using RBF neural network. Radio Science, 49 ( 4 ), 283 – 292. https://doi.org/10.1002/2013RS005247
dc.identifier.citedreferenceIsola, P., Zhu, J.-Y., Zhou, T., & Efros, A. A. ( 2017 ). Image-to-image translation with conditional adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1125 – 1134 ).
dc.identifier.citedreferenceKingma, D. P., & Ba, J. ( 2014 ). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
dc.identifier.citedreferenceLee, S., Ji, E. Y., Moon, Y. J., & Park, E. ( 2021 ). One-day forecasting of global TEC using a novel deep learning model. Space Weather, 19 ( 1 ), 2020SW002600. https://doi.org/10.1029/2020SW002600
dc.identifier.citedreferenceLiu, L., Morton, Y. J., & Liu, Y. ( 2022 ). Ml prediction of global ionospheric TEC maps. Space Weather, 20 ( 9 ), e2022SW003135. https://doi.org/10.1029/2022SW003135
dc.identifier.citedreferenceLiu, L., Zou, S., Yao, Y., & Wang, Z. ( 2020 ). Forecasting global ionospheric TEC using deep learning approach. Space Weather, 18 ( 11 ), e2020SW002501. https://doi.org/10.1029/2020SW002501
dc.identifier.citedreferenceMannucci, A. J., Wilson, B. D., Yuan, D. N., Ho, C. H., Lindqwister, U. J., & Runge, T. F. ( 1998 ). A global mapping technique for GPS-derived ionospheric total electron content measurements. Radio Science, 33 ( 3 ), 565 – 582. https://doi.org/10.1029/97RS02707
dc.identifier.citedreferenceMazumder, R., Hastie, T., & Tibshirani, R. ( 2010 ). Spectral regularization algorithms for learning large incomplete matrices. Journal of Machine Learning Research, 11, 2287 – 2322.
dc.identifier.citedreferenceNava, B., Coïsson, P., & Radicella, S. M. ( 2008 ). A new version of the NeQuick ionosphere electron density model. Journal of Atmospheric and Solar-Terrestrial Physics, 70 ( 15 ), 1856 – 1862. https://doi.org/10.1016/J.JASTP.2008.01.015
dc.identifier.citedreferenceOrús, R., Hernández-Pajares, M., Juan, J. M., & Sanz, J. ( 2005 ). Improvement of global ionospheric VTEC maps by using kriging interpolation technique. Journal of Atmospheric and Solar-Terrestrial Physics, 67 ( 16 ), 1598 – 1609. https://doi.org/10.1016/J.JASTP.2005.07.017
dc.identifier.citedreferencePan, Y., Jin, M., Zhang, S., & Deng, Y. ( 2020 ). TEC map completion using DCGAN and Poisson blending. Space Weather, 18 ( 5 ), e2019SW002390. https://doi.org/10.1029/2019SW002390
dc.identifier.citedreferencePan, Y., Jin, M., Zhang, S., & Deng, Y. ( 2021 ). TEC map completion through a deep learning model: SNP-GAN. Space Weather, 19 ( 11 ), e2021SW002810. https://doi.org/10.1029/2021SW002810
dc.identifier.citedreferencePerlongo, N. J., Ridley, A. J., Cnossen, I., & Wu, C. ( 2018 ). A year-long comparison of GPS TEC and global ionosphere-thermosphere models. Journal of Geophysical Research: Space Physics, 123 ( 2 ), 1410 – 1428. https://doi.org/10.1002/2017JA024411
dc.identifier.citedreferenceQian, L., Burns, A. G., Emery, B. A., Foster, B., Lu, G., Maute, A., et al. ( 2014 ). The NCAR TIE-GCM: A community model of the coupled thermosphere/ionosphere system. Modeling the Ionosphere–Thermosphere System, 73 – 83. https://doi.org/10.1002/9781118704417
dc.identifier.citedreferenceReddybattula, K. D., & Panda, S. K. ( 2019 ). Performance analysis of quiet and disturbed time ionospheric TEC responses from GPS-based observations, IGS-GIM, IRI-2016 and SPIM/IRI-Plas 2017 models over the low latitude Indian region. Advances in Space Research, 64 ( 10 ), 2026 – 2045. https://doi.org/10.1016/J.ASR.2019.03.034
dc.working.doiNOen
dc.owningcollnameInterdisciplinary and Peer-Reviewed


Files in this item

Name:
swe21567.pdf
Size:
3.353MB
Format:
PDF
View/Open
Name:
swe21567_am.pdf
Size:
19.69MB
Format:
PDF
View/Open

Show simple item record

Remediation of Harmful Language

The University of Michigan Library aims to describe its collections in a way that respects the people and communities who create, use, and are represented in them. We encourage you to Contact Us anonymously if you encounter harmful or problematic language in catalog records or finding aids. More information about our policies and practices is available at Remediation of Harmful Language.

Accessibility

If you are unable to use this file in its current format, please select the Contact Us link and we can modify it to make it more accessible to you.