ED‐ConvLSTM: A Novel Global Ionospheric Total Electron Content Medium‐Term Forecast Model

Xia, Guozhen; Zhang, Fubin; Wang, Cheng; Zhou, Chen

doi:10.1029/2021sw002959

Cited by 35 publications

(28 citation statements)

References 62 publications

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“…Xia, Zhang, et al. (2022) used an innovative encoder‐decoder structure with a convolutional LSTM network to forecast GIMs. In their test set 2018, they achieved an RMSE of 1.65, 1.78, and 2.10 TECU for 1‐day, 2‐day, and 6‐day predictions, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…These approaches are promising in solving nonlinear prediction problems (Han et al., 2021) and therefore can predict TEC more accurately (e.g., L. Liu et al., 2020). Various models were developed for single‐station (e.g., Huang & Yuan, 2014; Huang et al., 2015; Tebabal et al., 2018), regional (Ferreira et al., 2017; Song et al., 2018; Tebabal et al., 2019; Xia et al., 2021), and global TEC forecast (Cesaroni et al., 2020; Chen et al., 2022; Lee et al., 2021; L. Liu et al., 2020, 2022; J. Tang et al., 2022; Xia, Liu, et al., 2022; Xia, Zhang, et al., 2022; Yang et al., 2022). Unlike single‐station and regional forecasts that are usually trained with time series of TEC from different stations, global forecast models need to be trained with global TEC maps.…”

Section: Introductionmentioning

confidence: 99%

“…To the best of our knowledge, all global TEC forecast models mentioned above were trained with IGS GIMs or GIMs from a specific IAAC (Cesaroni et al., 2020; Chen et al., 2022; Lee et al., 2021; L. Liu et al., 2020, 2022; J. Tang et al., 2022; Xia, Zhang, et al., 2022; Yang et al., 2022) and achieved promising performance. Instead of aiming to outperform these models, this study aims to build a global TEC forecast model that uses the newly available VISTA TEC maps as input and can keep mesoscale structures in the output.…”

Section: Introductionmentioning

confidence: 99%

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Forecast Global Ionospheric TEC: Apply Modified U‐Net on VISTA TEC Data Set

et al. 2023

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This 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.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecast Global Ionospheric TEC: Apply Modified U‐Net on VISTA TEC Data Set

et al. 2023

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“…Machine learning methods can achieve 2.9-4.7 TECU pre-diction accuracy during periods of high solar activity period; the GBDT approach results in a 5.6% improvement compared to other machine learning methods. Xia et al (2021) investigated the performance of encoder-decoder convolution long short-term memory (ED-ConvLSTM) for medium-term ionosphere prediction on a global scale based on IGS GIMs. In 2018, this approach achieved 2.04 TECU for a five-day model.…”

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confidence: 99%

Real-Time Ionosphere Prediction Based on IGS Rapid Products Using Long Short-Term Memory Deep Learning

Chen

Gao

2023

navi

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The ionosphere is the layer of the Earth's atmosphere at heights between 50 km to 1300 km that contains most of the free electrons (Perez, 2018). The ionospheric layer causes a group delay in code measurements and advances in carrier phase measurements. The GNSS error caused by the ionosphere is the most significant when compared to other errors. Precise ionospheric information and modeling will therefore be useful to mitigate its effects on single-frequency receiver users and improve the convergence time and performance of dual-frequency receivers (Aggrey & Bisnath, 2019;Liu et al., 2021).The International GNSS Service (IGS) has developed the Global Ionosphere Map (GIM) in the IONosphere EXchange (IONEX) format (Schaer & Gurtner, 1998) with archived map data dating back to 1993. The typical spatial resolution of the GIMs is 2.5° in latitude and 5° in longitude, thus providing 71 x 73 grids globally (Schaer, 1997). Currently, there are three main types of IGS GIMs in terms of latencies, i.e., final, rapid, and predicted. The final GIM has an accuracy of 2-8 TECU and is thus a more accurate ionosphere map compared to the other two types (Products, 2020). Largely due to efforts and various approaches from

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“…Xia, G., Zhang, F.,Wang, C., & Zhou, C. (2022). Ed-convlstm: A novel global ionospheric total electron content medium-term forecast model Yang, K., & Liu, Y.…”

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confidence: 99%

Synthesis-Style Pre-trained Auto-Correlation Transformer: A Zero-shot Learner on Long Ionospheric TEC Series Forecasting

Yuan

Xia

Zhang

et al. 2023

Preprint

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In this paper, we present a novel approach to improve the accuracy of TEC prediction through data augmentation. Prior works that adopt various deep-learning-based approaches suffer from two major problems. First, from a deep model perspective: LSTM models exhibit low performance on long-term data dependency, while self-attention-based methods ignore the temporal nature of time series, which results in an information utilization bottleneck. Second, the existing TEC actual data is limited and existing generative models fail to generate sufficient high-quality datasets. Our work leverages a two-stage deep learning framework for TEC prediction, stage 1: a time series generative model synthesis of sufficient data close to real data distribution, and stage 2: an Anto-correlation-based transformer to model temporal dependencies by presenting series-wise connections. Experiment on the 2018 TEC testing benchmark demonstrates that our method improves the accuracy by a large margin. The models trained on synthetic data had a notably lower RMSE of 1.17 TECU, while the RMSE for the IRI2016 model was 2.88 TECU. Our results show that the model significantly reduces monthly RMSE, displaying higher reliability in mid, high, low latitudes. Our model shows higher reliability and significantly reduces monthly RMSE and latitude RMSE. However, although our model performs better than IRI2016, low latitudes RMSE needs improvement, as values are generally above 2.5 TECU. This finding has important implications for the development of advanced TEC prediction models and highlights the potential of transformer models trained on synthetic data for a range of applications in ionospheric research and satellite communication systems.

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ED‐ConvLSTM: A Novel Global Ionospheric Total Electron Content Medium‐Term Forecast Model

Cited by 35 publications

References 62 publications

Forecast Global Ionospheric TEC: Apply Modified U‐Net on VISTA TEC Data Set

Forecast Global Ionospheric TEC: Apply Modified U‐Net on VISTA TEC Data Set

Real-Time Ionosphere Prediction Based on IGS Rapid Products Using Long Short-Term Memory Deep Learning

Synthesis-Style Pre-trained Auto-Correlation Transformer: A Zero-shot Learner on Long Ionospheric TEC Series Forecasting

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