Assessment and Validation of Three Ionospheric Models (IRI‐2016, NeQuick2, and IGS‐GIM) From 2002 to 2018

Chen, Jun; Ren, Xiaodong; Zhang, Xiaohong; Zhang, Jingcheng; Huang, Liangke

doi:10.1029/2019sw002422

Cited by 31 publications

(18 citation statements)

References 54 publications

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“…Both provide electron density information globally up to GNSS altitudes of 20,200 km. While in NeQuick2 the topside model is described by a semi-Epstein layer with a height-dependent thickness parameter that is empirically determined (Chen et al 2020), in IRI-Plas, the electron density as well as the ion density structures are modified via adapting the scale height by ingestion of 10 years of GIM-TEC data (Arikan et al 2015). Comparisons with TEC measurements from the precise orbit determination (POD) antennae of low earth orbiting satellites (e.g., COSMIC) show that both models generally underestimate the plasmaspheric electron content between about 800-20,000 km altitude (Zhang et al 2017;Kashcheyev and Nava 2019;Ren et al 2020).…”

Section: Gim Modelsmentioning

confidence: 99%

Comparison of different methods to account for the plasmaspheric electron content in GNSS-derived ionospheric altimeter corrections and their impact on sea level trend estimation

Dettmering

Schwatke

2023

Earth Planets Space

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Global ionospheric maps based on GNSS measurements are nowadays often used to correct satellite altimeter measurements when the instruments have only one frequency or measure over coasts and inland waters. If these corrections do not account for the free electron fraction over the altimeter satellites, this leads to systematic deviations in the range measurements and thus in the estimated sea level. This study compares and assesses different approaches to reduce GNSS-based corrections for the plasmaspheric electron content. It is shown that using a simple scaling with a constant factor of 0.881 gives the best results for the Jason-1 mission, while correcting with the commonly used model ratios leads to higher sea level trend artefacts and larger noise levels, especially for periods of lower solar activity. Using this approach, the sea level trend error for both Jason-1 and Sentinel-6A can be reduced to below 0.1 mm/year, with standard deviations of the differences from the dual-frequency altimeter corrections of 6.74 mm. A simple machine learning approach (boosted regression tree) is also investigated and shows promising results. However, due to the higher processing capacity requirements and the larger deviations from long-term trend, further improvements are recommended before such an approach can be used in routine processing of altimeter corrections. Graphical Abstract

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Section: Gim Modelsmentioning

confidence: 99%

Comparison of different methods to account for the plasmaspheric electron content in GNSS-derived ionospheric altimeter corrections and their impact on sea level trend estimation

Dettmering

Schwatke

2023

Earth Planets Space

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“…For example, the predicted vertical total electron content (VTEC) differences of NeQuick2 and IRI‐2016 relative to International GNSS Service (IGS) GIMs are approximately 10 total electron content units (TECU, 1 TECU = 10 16 el/m 2 ; Wang et al., 2017; J. Chen, Ren, et al., 2020; P. Chen, Liu, et al., 2020). On the other hand, some scholars have also proposed several statistical models to predict ionosphere TEC like Auto‐Regressive Moving Average (ARMA; Zhang et al., 2014), statistical Holt‐Winter (Chen et al., 2017; Elmunim et al., 2017), empirical orthogonal function (J. Chen, Ren, et al., 2020; P. Chen, Liu, et al., 2020), and Least‐squares collocation model (Schaer, 1999). These models are mainly based on accumulated ionospheric data and related data, fitting and extrapolating according to corresponding known mathematical formulas.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, some scholars have also proposed several statistical models to predict ionosphere TEC like Auto-Regressive Moving Average (ARMA; Zhang et al, 2014), statistical Holt-Winter (Chen et al, 2017;Elmunim et al, 2017), empirical orthogonal function (J. Chen, Ren, et al, 2020;P. Chen, Liu, et al, 2020), and Least-squares collocation model (Schaer, 1999).…”

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confidence: 99%

Deep Learning for Global Ionospheric TEC Forecasting: Different Approaches and Validation

et al. 2022

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The application of deep learning technology to ionospheric prediction has become a new research hotspot. However, there are still some gaps, such as the prediction effect with different input solar and geomagnetic activity parameters, and the forecast accuracy with different prediction methods as well as the validation of long period data results, to be filled. We developed an ionospheric long short‐term memory network (Ion‐LSTM) with multiple input parameters to predict the global ionospheric total electron content (TEC). Two solutions with different ionospheric data based on Ion‐LSTM were assessed, namely spherical harmonic coefficients (SHC) and vertical TEC (VTEC) prediction solution. The results show two solutions, both perform well in accuracy and stability. The input of the geomagnetic activity index improves the prediction effect of the model in the storm period. For the 1‐ and 2‐day‐predicted global ionospheric maps (GIMs) from 2015 to 2020, the root mean square error (RMSE) of SHC prediction solution is 1.69 TECU and 1.84 TECU while that of the VTEC prediction solution is 1.70 TECU and 1.84 TECU, respectively. Over 70% of the absolute residuals are within 3 TECU in high solar activity and over 96% in low solar activity. Further, by comparing the predicted results between Ion‐LSTM and conventional methods (e.g., Center for Orbit Determination in Europe (CODE) predicted GIMs), the evaluation results show that the RMSE of Ion‐LSTM is 0.7 TECU lower than that of CODE predicted GIMs under different solar and geomagnetic activities. Additionally, the accuracy of the Ion‐LSTM prediction results decreases slightly as the input time span increases.

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“…So far, various ionospheric forecast models have been proposed and discussed, which are mainly divided into two categories. The rst is empirical ionospheric models, such as the Klobuchar model (Cai et al, 2017;Pongracic et al, 2019; F. Wang et al, 2014) and the NeQuick model (N. Wang et al, 2017;Jun Chen et al, 2020). These ionospheric models have been widely used to reduce the in uence of the ionosphere on single-frequency signals, contributing to an overall 50-70% reduction of ionospheric delay.…”

Section: Introductionmentioning

confidence: 99%

LSTM-based short-term ionospheric TEC forecast model and position accuracy analysis

Xie

Dai

Zhu

et al. 2022

Preprint

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Ionospheric delay is one of the major error sources in global navigation satellite system (GNSS) single point positioning (SPP). Some empirical models have been proposed to correct ionospheric delay, which, however, are limited by the low accuracy. The single-point time-series-based ionospheric total electron content (TEC) forecast method theoretically introduces model error and the accumulation of forecast error increases during a day. Therefore, based on the regular variation characteristics of the ionosphere, to improve the forecast accuracy of the ionosphere, we propose a long short-term memory (LSTM) short-term forecast model using discrete GNSS data and ionospheric space environment data in the same period of multiple days. The LSTM ionospheric forecast model is constructed based on the 2014 single-site GNSS data from different regions (low, mid, and high latitude regions) of the Crustal Movement Observation Network of China (CMONOC). The results are compared with the Klobuchar model, CMONOC regional ionosphere maps (RIM) data, and GNSS derived TEC measurements. The performance of each model in the SPP is also compared and analyzed to further examine the feasibility of the LSTM ionospheric forecast model. The comprehensive statistical analysis shows that: i) LSTM forecast model is consistent with GNSS-TEC observations at high, mid, and low latitudes, and the forecast error is less than 3 TECu, which is much better than that of the Klobuchar model and RIM model, and is robust to anomalous values. The mean absolute error (MAE) and root mean square error (RMSE) of the LSTM forecast model decrease with increasing latitude. ii) When being used in SPP, the LSTM forecast model brings significant improvement to position accuracy, which is overall better than the RIM and Klobuchar models. The RMSE of 3D position error is 2–4 m for the LSTM forecast model, 2–5 m for the RIM model, and 4–5 m for the Klobuchar model. iii) In terms of geographic location, for 3D position accuracy, it is highest at mid-latitudes followed by high latitudes and worst at low latitudes. For the percentage of 3D position corrections relative to the reference, it is highest at high latitudes followed by mid-latitudes, and lowest at low latitudes. The percentage of LSTM forecast model is 85%-90% at low latitudes, which is better than Klobuchar's 40%-80% and RIM's 75%-85%. Such percentage is over 90% at mid-latitudes, which is comparable to the RIM and about 50% better than the Klobuchar model. It is noteworthy that the LSTM performs even better than the reference data at high latitudes.

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Assessment and Validation of Three Ionospheric Models (IRI‐2016, NeQuick2, and IGS‐GIM) From 2002 to 2018

Cited by 31 publications

References 54 publications

Comparison of different methods to account for the plasmaspheric electron content in GNSS-derived ionospheric altimeter corrections and their impact on sea level trend estimation

Comparison of different methods to account for the plasmaspheric electron content in GNSS-derived ionospheric altimeter corrections and their impact on sea level trend estimation

Deep Learning for Global Ionospheric TEC Forecasting: Different Approaches and Validation

LSTM-based short-term ionospheric TEC forecast model and position accuracy analysis

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