Prediction of Global Ionospheric TEC Based on Deep Learning

Prediction of Global Ionospheric TEC Based on Deep Learning

Abstract The accurate prediction of ionospheric Total Electron Content (TEC) is important for global navigation satellite systems (GNSS), satellite communications and other space communications applications. In this study, a prediction model of global IGS‐TEC maps are established based on testing se...

Full description

Saved in:

Bibliographic Details
Main Authors:	Zhou Chen, Wenti Liao, Haimeng Li, Jinsong Wang, Xiaohua Deng, Sheng Hong
Format:	Article
Language:	English
Published:	Wiley 2022-04-01
Series:	Space Weather
Online Access:	https://doi.org/10.1029/2021SW002854
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Global TEC Map Fusion Through a Hybrid Deep Learning Model: RFGAN
by: Zhou Chen, et al.
Published: (2023-01-01)

Forecasting Global Ionospheric TEC Using Deep Learning Approach
by: Lei Liu, et al.
Published: (2020-11-01)

Deep Learning for Global Ionospheric TEC Forecasting: Different Approaches and Validation
by: Xiaodong Ren, et al.
Published: (2022-05-01)

ML Prediction of Global Ionospheric TEC Maps
by: Lei Liu, et al.
Published: (2022-09-01)

Ionospheric TEC Prediction Based on Ensemble Learning Models
by: Yang Zhou, et al.
Published: (2024-03-01)

Storm‐Time Characteristics of Ionospheric Model (MSAP) Based on Multi‐Algorithm Fusion
by: Zhou Chen, et al.
Published: (2024-01-01)

Forecast Global Ionospheric TEC: Apply Modified U‐Net on VISTA TEC Data Set
by: Zihan Wang, et al.
Published: (2023-08-01)

Prediction of Global Ionospheric Total Electron Content (TEC) Based on SAM‐ConvLSTM Model
by: Hanze Luo, et al.
Published: (2023-12-01)

Global Ionospheric TEC Forecasting for Geomagnetic Storm Time Using a Deep Learning‐Based Multi‐Model Ensemble Method
by: Xiaodong Ren, et al.
Published: (2023-03-01)

The Study on Modeling of Global Plasmaspheric Hiss Amplitude Based on Deep Learning Algorithm
by: Rongxin Tang, et al.
Published: (2023-03-01)

Modeling Ionospheric TEC Using Gradient Boosting Based and Stacking Machine Learning Techniques
by: Ayanew Nigusie, et al.
Published: (2024-03-01)

Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks
by: Eun‐Young Ji, et al.
Published: (2020-05-01)

A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction
by: Jian Wang, et al.
Published: (2023-12-01)

Synthesis‐Style Auto‐Correlation‐Based Transformer: A Learner on Ionospheric TEC Series Forecasting
by: Yuhuan Yuan, et al.
Published: (2023-10-01)

The Error of Global Ionospheric Map‐TEC During Equatorial Plasma Bubble Event in the High Solar Activity Year
by: Long Tang, et al.
Published: (2024-06-01)

Research on ionospheric parameters prediction based on deep learning
by: Yuntian FENG, et al.
Published: (2021-04-01)

One‐Day Forecasting of Global TEC Using a Novel Deep Learning Model
by: Sujin Lee, et al.
Published: (2021-01-01)

Ionospheric Scale Index Map Based on TEC Data for Space Weather Studies and Applications
by: C. M. Denardini, et al.
Published: (2020-09-01)

Validation of Ionospheric Modeled TEC in the Equatorial Ionosphere During the 2013 March and 2021 November Geomagnetic Storms
by: Min‐Yang Chou, et al.
Published: (2023-06-01)

ED‐AttConvLSTM: An Ionospheric TEC Map Prediction Model Using Adaptive Weighted Spatiotemporal Features
by: Liangchao Li, et al.
Published: (2024-03-01)

3‐D Regional Ionosphere Imaging and SED Reconstruction With a New TEC‐Based Ionospheric Data Assimilation System (TIDAS)
by: Ercha Aa, et al.
Published: (2022-04-01)

Different Response of the Ionospheric TEC and EEJ to Ultra‐Fast Kelvin Waves in the Mesosphere and Lower Thermosphere
by: Ruidi Sun, et al.
Published: (2024-03-01)

TEC Map Completion Through a Deep Learning Model: SNP‐GAN
by: Yang Pan, et al.
Published: (2021-11-01)

Modeling TEC Irregularities in the Arctic Ionosphere Using Empirical Orthogonal Function Method
by: Yaqi Jin, et al.
Published: (2023-08-01)

An Investigation of Ionospheric TEC Prediction Maps Over China Using Bidirectional Long Short‐Term Memory Method
by: Shuangshuang Shi, et al.
Published: (2022-06-01)

The Prediction of Storm‐Time Thermospheric Mass Density by LSTM‐Based Ensemble Learning
by: Peian Wang, et al.
Published: (2022-03-01)

Ionospheric Scale Index Map Based on TEC Data During the Saint Patrick Magnetic Storm and EPBs
by: C. M. Denardini, et al.
Published: (2020-09-01)

The Short‐Time Prediction of Thermospheric Mass Density Based on Ensemble‐Transfer Learning
by: Peian Wang, et al.
Published: (2023-10-01)

3‐D Ionospheric Imaging Over the South American Region With a New TEC‐Based Ionospheric Data Assimilation System (TIDAS‐SA)
by: Ercha Aa, et al.
Published: (2024-02-01)

Morphology and Climatology of Nighttime Periodic Ionospheric TEC Disturbances Associated With MSTIDs Over China
by: Ke Li, et al.
Published: (2024-11-01)

Response of the Ionospheric TEC to SSW and Associated Geomagnetic Storm Over the American Low Latitudinal Sector
by: J. B. Fashae, et al.
Published: (2022-05-01)

The Short‐Time Prediction of the Energetic Electron Flux in the Planetary Radiation Belt Based on Stacking Ensemble‐Learning Algorithm
by: Rongxin Tang, et al.
Published: (2022-02-01)

A Regional Model of Topside Ionospheric Effective Scale Heights Derived From Ionosonde and GNSS TEC
by: Chunhua Jiang, et al.
Published: (2023-07-01)

Comparative analysis of empirical and deep learning models for ionospheric sporadic E layer prediction
by: BingKun Yu, et al.
Published: (2025-01-01)

Validation of Ionospheric Specifications During Geomagnetic Storms: TEC and foF2 During the 2013 March Storm Event‐II
by: J. S. Shim, et al.
Published: (2023-05-01)

Deep Learning Applications in Ionospheric Modeling: Progress, Challenges, and Opportunities
by: Renzhong Zhang, et al.
Published: (2025-01-01)

TEC Map Completion Using DCGAN and Poisson Blending
by: Yang Pan, et al.
Published: (2020-05-01)

Deep Learning‐Based Regional Ionospheric Total Electron Content Prediction—Long Short‐Term Memory (LSTM) and Convolutional LSTM Approach
by: Se‐Heon Jeong, et al.
Published: (2024-01-01)

Predicting Equatorial Ionospheric Convective Instability Using Machine Learning
by: D. Garcia, et al.
Published: (2023-12-01)

Transformer-Based Ionospheric Prediction and Explainability Analysis for Enhanced GNSS Positioning
by: He-Sheng Wang, et al.
Published: (2024-12-01)