{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T12:56:48Z","timestamp":1784033808624,"version":"3.55.0"},"reference-count":48,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2024,5,10]],"date-time":"2024-05-10T00:00:00Z","timestamp":1715299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["41974184"],"award-info":[{"award-number":["41974184"]}]},{"name":"National Natural Science Foundation of China","award":["41674183"],"award-info":[{"award-number":["41674183"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this paper, we propose a global ionospheric total electron content (TEC) maps (GIM) prediction model based on deep learning methods that is both straightforward and practical, meeting the requirements of various applications. The proposed model utilizes an encoder-decoder structure with a Convolution Long Short-Term Memory (ConvLSTM) network and has a spatial resolution of 5\u00b0 longitude and 2.5\u00b0 latitude, with a time resolution of 1 h. We utilized the Center for Orbit Determination in Europe (CODE) GIM dataset for 18 years from 2002 to 2019, without requiring any other external input parameters, to train the ConvLSTM models for forecasting GIM 1, 2, and 3 days in advance. Using the CODE GIM data from 1 January 2020 to 31 December 2023 as the test dataset, the performance evaluation results show that the average root mean square errors (RMSE) for 1, 2 and 3 days of forecasts are 2.81 TECU, 3.16 TECU, and 3.41 TECU, respectively. These results show improved performance compared to the IRI-Plas model and CODE\u2019s 1-day forecast product c1pg, and comparable to CODE\u2019s 2-day forecast c2pg. The model\u2019s predictions get worse as the intensity of the storm increases, and the prediction error of the model increases with the lead time.<\/jats:p>","DOI":"10.3390\/rs16101700","type":"journal-article","created":{"date-parts":[[2024,5,13]],"date-time":"2024-05-13T08:33:03Z","timestamp":1715589183000},"page":"1700","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Operational Forecasting of Global Ionospheric TEC Maps 1-, 2-, and 3-Day in Advance by ConvLSTM Model"],"prefix":"10.3390","volume":"16","author":[{"given":"Jiayue","family":"Yang","sequence":"first","affiliation":[{"name":"State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Science and Technology on Environmental Space Situation Awareness, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1353-8873","authenticated-orcid":false,"given":"Wengeng","family":"Huang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Science and Technology on Environmental Space Situation Awareness, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2174-5904","authenticated-orcid":false,"given":"Guozhen","family":"Xia","sequence":"additional","affiliation":[{"name":"Department of Space Physics, School of Electronic Information, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2692-9451","authenticated-orcid":false,"given":"Chen","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Space Physics, School of Electronic Information, Wuhan University, Wuhan 430072, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2175-3936","authenticated-orcid":false,"given":"Yanhong","family":"Chen","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Space Weather, National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Key Laboratory of Science and Technology on Environmental Space Situation Awareness, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,5,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Kaselimi, M., Voulodimos, A., Doulamis, N., Doulamis, A., and Delikaraoglou, D. (2020). A Causal Long Short-Term Memory Sequence to Sequence Model for TEC Prediction Using GNSS Observations. Remote Sens., 12.","DOI":"10.3390\/rs12091354"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1029\/2001JA000214","article-title":"Global Impact of Ionospheric Outflows on the Dynamics of the Magnetosphere and Cross-Polar Cap Potential","volume":"107","author":"Winglee","year":"2002","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1134\/S1990793119040067","article-title":"Spatial and Temporal Variations of the Ionosphere during Meteorological Disturbances in December 2010","volume":"13","author":"Karpov","year":"2019","journal-title":"Russ. J. Phys. Chem. B"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long Short-Term Memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"177","DOI":"10.1029\/RG016i002p00177","article-title":"Goals and Status of the International Reference Ionosphere","volume":"16","author":"Rawer","year":"1978","journal-title":"Rev. Geophys."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"418","DOI":"10.1002\/2016SW001593","article-title":"International Reference Ionosphere 2016: From Ionospheric Climate to Real-time Weather Predictions","volume":"15","author":"Bilitza","year":"2017","journal-title":"Space Weather"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"A09324","DOI":"10.1029\/2012JA018017","article-title":"Two-phase Storm Profile of Global Electron Content in the Ionosphere and Plasmasphere of the Earth","volume":"117","author":"Gulyaeva","year":"2012","journal-title":"J. Geophys. Res."},{"key":"ref_8","unstructured":"Gulyaeva, T., and Bilitza, D. (2012). New Developments in the Standard Model, Nova Science Inc.. 39th COSPAR Scientific Assembly."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"929","DOI":"10.5047\/eps.2011.04.007","article-title":"Inter-Hemispheric Imaging of the Ionosphere with the Upgraded IRI-Plas Model during the Space Weather Storms","volume":"63","author":"Gulyaeva","year":"2011","journal-title":"Earth Planet Space"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1109\/TAES.1987.310829","article-title":"Ionospheric Time-Delay Algorithm for Single-Frequency GPS Users","volume":"AES-23","author":"Klobuchar","year":"1987","journal-title":"IEEE Trans. Aerosp. Electron. Syst."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/0273-1177(90)90301-F","article-title":"An Analytical Model of the Electron Density Profile in the Ionosphere","volume":"10","author":"Radicella","year":"1990","journal-title":"Adv. Space Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1556\/AGeod.37.2002.2-3.7","article-title":"Electron Density Models for Assessment Studies-New Developments","volume":"37","author":"Leitinger","year":"2002","journal-title":"Acta Geod. Geophys. Hung"},{"key":"ref_13","first-page":"A03328","article-title":"A global model: Empirical orthogonal function analysis of total electron content 1999\u20132009 Data","volume":"117","author":"Zhang","year":"2012","journal-title":"J. Geophys. Res."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1410","DOI":"10.1029\/2018SW001987","article-title":"An Ionosphere Specification Technique Based on Data Ingestion Algorithm and Empirical Orthogonal Function Analysis Method","volume":"16","author":"Aa","year":"2018","journal-title":"Space Weather"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"S74","DOI":"10.1134\/S0016793222600667","article-title":"Aeronomic and Dynamic Correction of the Global Model GTEC for Disturbed Conditions","volume":"62","author":"Shubin","year":"2022","journal-title":"Geomagn. Aeron."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1002\/cjg2.20021","article-title":"A new method for detection of pre-earthquake ionospheric anomalies","volume":"56","author":"Xiao","year":"2013","journal-title":"Chin. J. Geophys."},{"key":"ref_17","first-page":"118","article-title":"Short-term TEC prediction of ionosphere based on ARIMA model","volume":"43","author":"Zhang","year":"2014","journal-title":"Acta Geod. Cartogr. Sin."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1007\/s10509-020-03817-2","article-title":"Ionospheric TEC Forecasting Using Gaussian Process Regression (GPR) and Multiple Linear Regression (MLR) in Turkey","volume":"365","author":"Inyurt","year":"2020","journal-title":"Astrophys. Space Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1377","DOI":"10.1016\/j.asr.2021.03.021","article-title":"Ionospheric TEC Forecast Model Based on Support Vector Machine with GPU Acceleration in the China Region","volume":"68","author":"Xia","year":"2021","journal-title":"Adv. Space Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"48","DOI":"10.11728\/cjss2018.01.048","article-title":"Prediction model for ionospheric total electron content based on deep learning recurrent neural network","volume":"38","author":"Yuan","year":"2018","journal-title":"Chin. J. Space Sci."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Cander, L.R., and Lamming, X. (1997, January 14\u201317). Neural Networks in Ionospheric Prediction and Short-Term Forecasting. Proceedings of the Tenth International Conference on Antennas and Propagation (Conf. Publ. No. 436), Edinburgh, UK.","DOI":"10.1049\/cp:19970323"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1016\/j.jastp.2007.09.002","article-title":"Prediction of Global Positioning System Total Electron Content Using Neural Networks over South Africa","volume":"69","author":"Habarulema","year":"2007","journal-title":"J. Atmos. Sol. Terr. Phys."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"A4","DOI":"10.1029\/2010JA016269","article-title":"Regional GPS TEC Modeling; Attempted Spatial and Temporal Extrapolation of TEC Using Neural Networks","volume":"116","author":"Habarulema","year":"2011","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"745","DOI":"10.1016\/j.asr.2018.03.043","article-title":"Predicting TEC in China Based on the Neural Networks Optimized by Genetic Algorithm","volume":"62","author":"Song","year":"2018","journal-title":"Adv. Space Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1126\/science.1127647","article-title":"Reducing the Dimensionality of Data with Neural Networks","volume":"313","author":"Hinton","year":"2006","journal-title":"Science"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"e2020SW002706","DOI":"10.1029\/2020SW002706","article-title":"Long Short-Term Memory Neural Network for Ionospheric Total Electron Content Forecasting Over China","volume":"19","author":"Xiong","year":"2021","journal-title":"Space Weather"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"e2022SW003135","DOI":"10.1029\/2022SW003135","article-title":"ML Prediction of Global Ionospheric TEC Maps","volume":"20","author":"Liu","year":"2022","journal-title":"Space Weather"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"e2021SW003011","DOI":"10.1029\/2021SW003011","article-title":"Deep Learning for Global Ionospheric TEC Forecasting: Different Approaches and Validation","volume":"20","author":"Ren","year":"2022","journal-title":"Space Weather"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"e2022SW003231","DOI":"10.1029\/2022SW003231","article-title":"Global Ionospheric TEC Forecasting for Geomagnetic Storm Time Using a Deep Learning-Based Multi-Model Ensemble Method","volume":"21","author":"Ren","year":"2023","journal-title":"Space Weather"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e2023SW003485","DOI":"10.1029\/2023SW003485","article-title":"Neural Networks for Operational SYM-H Forecasting Using Attention and SWICS Plasma Features","volume":"21","author":"Cid","year":"2023","journal-title":"Space Weather"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"e2020SW002589","DOI":"10.1029\/2020SW002589","article-title":"ForecastingSYM-Hindex: A comparison betweenlong short-term memory andconvolutional neural networks","volume":"19","author":"Siciliano","year":"2021","journal-title":"Space Weather"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"777","DOI":"10.1029\/2018SW001861","article-title":"The Importance of Ensemble Techniques for Operational Space Weather Forecasting","volume":"16","author":"Murray","year":"2018","journal-title":"Space Weather"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1002\/swe.20096","article-title":"Forecasting propagation and evolution of CMEs in an operational setting: Whathas been learned","volume":"11","author":"Zheng","year":"2013","journal-title":"Space Weather"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1002\/swe.20099","article-title":"Transitioning Research to Operations: Transforming the \u201cValley of Death\u201d Into a \u201cValley of Opportunity\u201d","volume":"11","author":"Merceret","year":"2013","journal-title":"Space Weather"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1002\/2013SW001007","article-title":"The main pillar: Assessment ofspace weather observational assetperformance supporting nowcasting, forecasting and research to operations","volume":"12","author":"Posner","year":"2014","journal-title":"Space Weather"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e2023SW003457","DOI":"10.1029\/2023SW003457","article-title":"Solar wind data assimilation in an operational context: Use of near-real-time data and the forecast value of an L5 monitor","volume":"21","author":"Turner","year":"2023","journal-title":"Space Weather"},{"key":"ref_37","first-page":"802","article-title":"Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting","volume":"28","author":"Shi","year":"2015","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"e2021SW002959","DOI":"10.1029\/2021SW002959","article-title":"ED-ConvLSTM: A Novel Global Ionospheric Total Electron Content Medium-Term Forecast Model","volume":"20","author":"Xia","year":"2022","journal-title":"Space Weather"},{"key":"ref_39","unstructured":"Kingma, D., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. Int. Conf. Learn. Represent."},{"key":"ref_40","unstructured":"Schaer, S. (1999). Mapping and Predicting the Earth\u2019s Ionosphere Using the Global Positioning System, Institut f\u00fcr Geod\u00e4sie und Photogrammetrie, Eidg. Technische Hochschule Z\u00fcrich."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"5771","DOI":"10.1029\/93JA02867","article-title":"What Is a Geomagnetic Storm?","volume":"99","author":"Gonzalez","year":"1994","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"6666","DOI":"10.1002\/jgra.50576","article-title":"Global Empirical Model of TEC Response to Geomagnetic Activity","volume":"118","author":"Mukhtarov","year":"2013","journal-title":"J. Geophys. Res. Space Phys."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"e2020JA027966","DOI":"10.1029\/2020JA027966","article-title":"Statistical Analysis of Joule Heating and Thermosphere Response During Geomagnetic Storms of Different Magnitudes","volume":"125","author":"Wang","year":"2020","journal-title":"JGR Space Phys."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"e2020JA028747","DOI":"10.1029\/2020JA028747","article-title":"Latitudinal impacts of Joule heating on the high-latitude thermo-spheric density enhancement during geomagnetic storms","volume":"126","author":"Wang","year":"2021","journal-title":"JGR Space Phys."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"e2021SW003017","DOI":"10.1029\/2021SW003017","article-title":"Using temporal rela-tionship of thermospheric density with geomagnetic activity indices and Joule heating as calibration for NRLMSISE-00 during geomagnetic storms","volume":"20","author":"Wang","year":"2022","journal-title":"Space Weather"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"e2020SW002641","DOI":"10.1029\/2020SW002641","article-title":"The Geomagnetic Kp Index and Derived Indices of Geomagnetic Activity","volume":"19","author":"Matzka","year":"2021","journal-title":"Space Weather"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"e2023JA032354","DOI":"10.1029\/2023JA032354","article-title":"Ionospheric conductances due to elec-tron and ion precipitations: A comparison between EISCAT and DMSP estimates","volume":"129","author":"Wang","year":"2024","journal-title":"JGR Space Phys."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Shahzad, R., Shah, M., Tariq, M.A., Calabia, A., Melgarejo-Morales, A., Jamjareegulgarn, P., and Liu, L. (2023). Ionospheric\u2013Thermospheric Responses to Geomagnetic Storms from Multi-Instrument Space Weather Data. Remote Sens., 15.","DOI":"10.3390\/rs15102687"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1700\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:43:53Z","timestamp":1760107433000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/16\/10\/1700"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,10]]},"references-count":48,"journal-issue":{"issue":"10","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["rs16101700"],"URL":"https:\/\/doi.org\/10.3390\/rs16101700","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,10]]}}}