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

https://doi.org/10.1029/2021gl095561

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Characterization of high‐latitude ionospheric scintillation of GPS signals

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On the generation/decay of the storm‐enhanced density plumes: Role of the convection flow and field‐aligned ion flow

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Comparison of the effect of high‐latitude and equatorial ionospheric scintillation on GPS signals during the maximum of solar cycle 24

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Prediction of the level of ionospheric scintillation at equatorial latitudes in Brazil using a neural network

inferredcites →https://doi.org/10.1002/2016ja023352openalex-snapshot

Analysis of the global ionospheric disturbances of the March 2015 great storm

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Effects of Ionospheric Scintillation on GNSS-Based Positioning

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Automatic detection of ionospheric scintillation‐like GNSS satellite oscillator anomaly using a machine‐learning algorithm

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Multi-scale ionosphere responses to the May 2017 magnetic storm over the Asian sector

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Challenges to Equatorial Plasma Bubble and Ionospheric Scintillation Short-Term Forecasting and Future Aspects in East and Southeast Asia

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Solar Energy Forecasting and Resource Assessment

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GPS phase difference variation statistics: A comparison between phase scintillation index and proxy indices

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The International GPS Service (IGS): An interdisciplinary service in support of Earth sciences

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Specification and forecasting of scintillations in communication/navigation links: current status and future plans

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Detection of high‐altitude ionospheric irregularities with GPS/MET

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High‐resolution global storm index: Dst versus SYM‐H

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GPS and ionospheric scintillations

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Canadian High Arctic Ionospheric Network (CHAIN)

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Toward the probabilistic forecasting of high‐latitude GPS phase scintillation

inferredcites →https://doi.org/10.1029/2018ja026353openalex-snapshot

On the Relationship Between the Rate of Change of Total Electron Content Index (ROTI), Irregularity Strength (CkL), and the Scintillation Index (S4)

inferredcites →https://doi.org/10.1029/2018sw002018openalex-snapshot

New Capabilities for Prediction of High‐Latitude Ionospheric Scintillation: A Novel Approach With Machine Learning

inferredcites →https://doi.org/10.1029/2018sw002061openalex-snapshot

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

inferredcites →https://doi.org/10.1029/2019ja027681openalex-snapshot

Global View of Ionospheric Disturbance Impacts on Kinematic GPS Positioning Solutions During the 2015 St. Patrick's Day Storm

inferredcites →https://doi.org/10.1029/2020sw002501openalex-snapshot

Forecasting Global Ionospheric TEC Using Deep Learning Approach

inferredcites →https://doi.org/10.1029/97gl02273openalex-snapshot

Monitoring of global ionospheric irregularities using the Worldwide GPS Network

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Magnetospheric Current Systems

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Space weather challenges of the polar cap ionosphere

inferredcites →https://doi.org/10.1051/swsc/2013025/pdfopenalex-snapshot

Space weather challenges of the polar cap ionosphere

inferredcites →https://doi.org/10.1051/swsc/2016004openalex-snapshot

Overview of the 2015 St. Patrick’s day storm and its consequences for RTK and PPP positioning in Norway

inferredcites →https://doi.org/10.1109/jstars.2020.3022748openalex-snapshot

Multi-Instrumental Observations of Early Morning Equatorial Plasma Depletions During the 2017 Memorial Weekend Storm

inferredcites →https://doi.org/10.1109/plans.2018.8373500openalex-snapshot

Application of machine learning to the characterization of GPS L1 ionospheric amplitude scintillation

inferredcites →https://doi.org/10.1109/proc.1982.12313openalex-snapshot

Radio wave scintillations in the ionosphere

inferredcites →https://doi.org/10.1109/taes.2018.2850385openalex-snapshot

Detection of GNSS Ionospheric Scintillations Based on Machine Learning Decision Tree

inferredcites →https://doi.org/10.1162/089976600300015015openalex-snapshot

Learning to Forget: Continual Prediction with LSTM

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Long Short-Term Memory

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SuomiNet: A Real–Time National GPS Network for Atmospheric Research and Education

inferredcites →https://doi.org/10.1186/s40623-016-0506-1openalex-snapshot

High-latitude ionospheric irregularities: differences between ground- and space-based GPS measurements during the 2015 St. Patrick’s Day storm

inferredcites →https://doi.org/10.48550/arxiv.1412.6980openalex-snapshot

Adam: A Method for Stochastic Optimization

inferredcites →https://doi.org/10.48550/arxiv.1505.00853openalex-snapshot

Empirical Evaluation of Rectified Activations in Convolutional Network

inferredcites →https://doi.org/10.48550/arxiv.1506.04214openalex-snapshot

Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting

inferredcites →https://doi.org/10.48550/arxiv.1706.03458openalex-snapshot

Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model

inferredcites →https://doi.org/10.48550/arxiv.1910.01570openalex-snapshot

Prediction of GNSS Phase Scintillations: A Machine Learning Approach

inferredcites →https://doi.org/10.5194/amt-8-2789-2015openalex-snapshot

Use of radio occultation to probe the high-latitude ionosphere

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Neural Network-Generated Simulation of Ionospheric Amplitude Scintillation Using GAN-Based Approach

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Leveraging State-of-the Art Computational Models to Forecast TEC and Categorize Ionospheric Scintillations Using ML Methodologies

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A storm-time ionospheric TEC model with multichannel features by the spatiotemporal ConvLSTM network

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A short-term prediction model of global ionospheric VTEC based on the combination of long short-term memory and convolutional long short-term memory

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Leveraging machine learning techniques and GPS measurements for precise TEC rate predictions

inferred← citeshttps://doi.org/10.1007/s10509-023-04258-3openalex-snapshot

Forecasting 3-dimensional ionospheric disturbances during Hurricane Matthew using ConvLSTM neural network

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Physics-informed loss functions for vertical total electron content forecast

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Natural-hazard monitoring with global navigation satellite systems (GNSS)

inferred← citeshttps://doi.org/10.1016/j.asr.2024.05.022openalex-snapshot

Machine learning approach for prediction of ionospheric irregularities on ROTI index over the Northern anomaly crest in Egypt during solar cycle 24

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A framework to nowcast soil moisture with NASA SMAP level 4 data using in-situ measurements and deep learning

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Advanced detection methods and machine learning analysis of temporal and spatial patterns of equatorial plasma bubble depth

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Predicting Swarm Equatorial Plasma Bubbles via Machine Learning and Shapley Values

inferred← citeshttps://doi.org/10.1029/2022sw003135openalex-snapshot

ML Prediction of Global Ionospheric TEC Maps

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Estimation Model of Global Ionospheric Irregularities: An Artificial Intelligence Approach

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Deep Learning Insights Into Ionospheric Sporadic E Irregularities Under Different Solar Activity Conditions

inferred← citeshttps://doi.org/10.1029/2024jh000499openalex-snapshot

Classification of High‐Latitude Source Region‐Based Ionospheric Scintillation Signatures Using Machine Learning

inferred← citeshttps://doi.org/10.1029/2024rs007982openalex-snapshot

Revealing Inconsistencies in ROTI Index Using Multi‐GNSS Constellation Measurements: Impact of Sampling Rates and Time Window

inferred← citeshttps://doi.org/10.1029/2024sw004224openalex-snapshot

A Novel Short‐Term Prediction Model for Regional Equatorial Plasma Bubble Irregularities in East and Southeast Asia

inferred← citeshttps://doi.org/10.1029/2025ja034935openalex-snapshot

Prediction of Arctic Ionospheric Scintillation Using Stacked Machine Learning

inferred← citeshttps://doi.org/10.1029/2025jh000608openalex-snapshot

High‐Precision Prediction of Ionospheric TEC in the China Region Based on CMONOC High‐Resolution Data and an Auxiliary Attention Temporal Convolutional Network

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Extreme two-phase change of ionospheric electron temperature overshoot during geomagnetic storms

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Regime-dependent predictability of high-latitude ionospheric TEC: A comparative machine learning study

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Harnessing ML Methodologies to Forecast TEC and Classify Ionospheric Scintillations

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Global Ionospheric ROTI Prediction Based on DeepONet

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Reconstruction of an Independent Data-Driven TEC Model Using Machine Learning

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Ionospheric VTEC Maps Forecasting Based on Graph Neural Network With Transformers

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A Hybrid CNN-Transformer Framework for Rapid Ionospheric TEC Forecasting Over the 60 °W International Meridian Circle Sector

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A Systematic Review of Machine Learning Techniques for GNSS Use Cases

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Synthetic Generator for Ionospheric Amplitude Scintillation Fading Channels Using Generative Adversarial Networks

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Arctic TEC Mapping Using Integrated LEO-Based GNSS-R and Ground-Based GNSS Observations: A Simulation Study

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The Short-Term Prediction of Low-Latitude Ionospheric Irregularities Leveraging a Hybrid Ensemble Model

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The 4-D Evolution of Conjugate Ionospheric Disturbances After the Tonga Volcanic Eruption

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Prediction of the Ionospheric Irregularities Based on Residual Compensation WHO-RF Model

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Leveraging SMAP-Based Soil Moisture to Identify Runoff Thresholds on Flash Flood–Prone Basins in the Mediterranean Region

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Global ROTI forecasting with a Bayesian model based in long-tail distributions

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Ionospheric VTEC Map Forecasting based on Graph Neural Network with Transformers

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Global Ionospheric TEC Map Prediction Based on Multichannel ED-PredRNN

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Optimizing Deep Learning Models with Improved BWO for TEC Prediction

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A Spatiotemporal Network Model for Global Ionospheric TEC Forecasting

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Spatiotemporal Prediction of Ionospheric Total Electron Content Based on ED-ConvLSTM

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Prediction of Ionospheric Scintillation with ConvGRU Networks Using GNSS Ground-Based Data across South America

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Estimating Ionospheric Phase Scintillation Indices in the Polar Region from 1 Hz GNSS Observations Using Machine Learning

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Predicting Swarm Equatorial Plasma Bubbles via Machine Learning and Shapley Values

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Prediction of Ionospheric Scintillation with a Combination of Deep Learning Algorithms Using GNSS Ground-Based Network across South America

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