Machine Learning Prediction of Storm‐Time High‐Latitude Ionospheric Irregularities From GNSS‐Derived ROTI Maps

Liu, Lei; Morton, Y. Jade; Liu, Yunxiang

doi:10.1029/2021gl095561

Cited by 22 publications

(7 citation statements)

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“…The convLSTM architecture is capable of learning features from a spatiotemporal sequence. It has been successfully applied in many fields of multi-dimensional spatiotemporal predictions (Shi et al, 2015(Shi et al, , 2017Liu et al, 2021). In this study, the convLSTM layer is used as the core module to predict global TEC maps.…”

Section: Convlstm ML Algorithmmentioning

confidence: 99%

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ML Prediction of Global Ionospheric TEC Maps

2022

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This paper applies the convolutional long short‐term memory (convLSTM)‐based machine learning models to forecast global ionospheric total electron content (TEC) maps with up to 24 hr of lead time at a 1‐hr interval. Four convLSTM‐based models were investigated, and the one that implements the L1 loss function and the residual prediction strategy demonstrates the best performance. The convLSTM models are trained and evaluated using Center for Orbit Determination in Europe (CODE) global TEC maps over a period of nearly seven years from 19 October 2014 to 21 July 2021. Results show that the best convLSTM model outperforms the 1‐day predicted global TEC products released by CODE analysis center (c1pg) and persistence models under various levels of solar and geomagnetic activities, except for a lead time beyond 8 hr during the storm time where the c1pg has slightly better performance. The convLSTM forecasting performance degrades as the lead time increases.

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Section: Convlstm ML Algorithmmentioning

confidence: 99%

“…, X𝑋47, X𝑋48 ) shown in Figure 3. Detailed descriptions of this architecture can be found in Shi et al (2015) and Liu et al (2021). Here, two prediction strategies are implemented to predict global TEC maps.…”

Section: Convlstm ML Algorithmmentioning

confidence: 99%

ML Prediction of Global Ionospheric TEC Maps

2022

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“…EPBs are a known cause of radio wave scintillations (Kintner et al., 2007), and ML has been used to predict when and where scintillations may occur (Jiao et al., 2017; Linty et al., 2018; McGranaghan et al., 2018). Lastly, deep learning has also been applied to predict storm‐driven irregularities within the ionosphere (Liu et al., 2021).…”

Section: Introductionmentioning

confidence: 99%

Predicting Swarm Equatorial Plasma Bubbles via Machine Learning and Shapley Values

et al. 2023

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In this study we present AI Prediction of Equatorial Plasma Bubbles (APE), a machine learning model that can accurately predict the Ionospheric Bubble Index (IBI) on the Swarm spacecraft. IBI is a correlation (R2) between perturbations in plasma density and the magnetic field, whose source can be Equatorial Plasma Bubbles (EPBs). EPBs have been studied for a number of years, but their day‐to‐day variability has made predicting them a considerable challenge. We build an ensemble machine learning model to predict IBI. We use data from 2014 to 2022 at a resolution of 1s, and transform it from a time‐series into a 6‐dimensional space with a corresponding EPB R2 (0–1) acting as the label. APE performs well across all metrics, exhibiting a skill, association and root mean squared error score of 0.96, 0.98 and 0.08 respectively. The model performs best post‐sunset, in the American/Atlantic sector, around the equinoxes, and when solar activity is high. This is promising because EPBs are most likely to occur during these periods. Shapley values reveal that F10.7 is the most important feature in driving the predictions, whereas latitude is the least. The analysis also examines the relationship between the features, which reveals new insights into EPB climatology. Finally, the selection of the features means that APE could be expanded to forecasting EPBs following additional investigations into their onset.

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“…More recently, there has been extensive research in artificial intelligence (AI) (Chai et al., 2020; Hu et al., 2021; Ravuri et al., 2021; Rouet‐Leduc et al., 2020; L. Liu et al., 2020; Vech & Malaspina, 2021). The rapid progress in AI research has substantially impacted many scientific fields, including geophysical sciences, on account of the increase in data and serious computational power (Kadow et al., 2020; Lee et al., 2021; L. Liu et al., 2021; Reichstein et al., 2019; Sai Gowtam & Tulasi Ram, 2017). Examples include unsupervised learning to classify seismic events (Cui et al., 2021), deep convolutional neural networks for the recognition of extreme events such as coronal mass ejections (Wang et al., 2019) and predicting storm‐time ionospheric irregularities using image‐based convolutional long short‐term memory machine learning algorithms (Xiong et al., 2021), to name a few.…”

Section: Introductionmentioning

confidence: 99%

“…Liu et al, 2020;Vech & Malaspina, 2021). The rapid progress in AI research has substantially impacted many scientific fields, including geophysical sciences, on account of the increase in data and serious computational power (Kadow et al, 2020;Lee et al, 2021;L. Liu et al, 2021;Reichstein et al, 2019;Sai Gowtam & Tulasi Ram, 2017).…”

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confidence: 99%

Estimation Model of Global Ionospheric Irregularities: An Artificial Intelligence Approach

Tian

et al. 2022

Space Weather

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The ionospheric sporadic E layer, the ionospheric irregularities of enhanced electron density, appears in the Earth's ionosphere at altitudes between 90 and 120 km, which supports the real‐world radio communication needs of many sectors reliant on ionosphere‐dependent decision‐making. The prediction of the occurrence of sporadic E layers has been extremely difficult due to the highly complex behavior. Conventional numerical methods are limited because of the inability to extract high‐level information from data. Deep learning is a powerful tool for mining latent features from data, which can theoretically avoid assumptions constraining physical methods. Inspired by feature extraction, we applied deep learning to explore latent relationships between mapping observable lower atmospheric data and ionospheric data from limited observations. The proposed model was trained with high‐resolution ERA5 data during 1 January 2007–30 August 2018 as input and corresponding ionospheric sporadic E data collected from COSMIC RO measurements as output. The results show that the model can learn complex relevance as bridges connecting the input and the desired output and obtain excellent performance and generalization capability by applying multiple evaluation criteria. Additionally, we established several model architecture training methods to explore the performance of the model with different input data. The statistic results show that model inference performance is proportional to the abundance of input information and is impacted by intraseasonal variability. The inference capability of the model achieves the best performance in the June–August (JJA) and December–February (DJF) seasons, which is the exact period of sporadic E layer significant occurrence, although different models are evaluated.

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Machine Learning Prediction of Storm‐Time High‐Latitude Ionospheric Irregularities From GNSS‐Derived ROTI Maps

Abstract: Ionospheric plasma irregularities can lead to phase and amplitude scintillations of L-band radio signals traversing the ionosphere to degrade satellite navigation and radio communication system performances

Cited by 22 publications

References 42 publications

ML Prediction of Global Ionospheric TEC Maps

ML Prediction of Global Ionospheric TEC Maps

Predicting Swarm Equatorial Plasma Bubbles via Machine Learning and Shapley Values

Estimation Model of Global Ionospheric Irregularities: An Artificial Intelligence Approach

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