Applicability Analysis of VTEC Derived from the Sophisticated Klobuchar Model in China

Huang, Liangke; Liu, Lilong; Wu, Pituan; Qin, Xuyuan

doi:10.3390/ijgi6030075

Cited by 14 publications

(5 citation statements)

References 32 publications

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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%

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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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Section: Introductionmentioning

confidence: 99%

“…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.…”

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confidence: 99%

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

et al. 2022

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“…Generally, real‐time Global Navigation Satellite System (GNSS) users often apply the following approaches to correct ionospheric delays. The first one is the broadcast ionosphere models, such as Klobuchar and its refined models for Global Positioning System (GPS) (Bi et al., 2017; Chen et al., 2017; Klobuchar, 1987; N. Wang et al., 2016), NeQuickG model for GALILEO (Leitinger et al., 2005; Montenbruck & González‐Rodríguez, 2019; Nava et al., 2008), broadcast ionospheric model and its improvements for Beidou Navigation Satellite System (BDS) (N. Wang et al., 2018, 2021; Yuan et al., 2019), Neustrelitz Total Electron Content Model (NTCM) and its modifications for the next‐generation GNSS (Hoque & Jakowski, 2015; Hoque et al., 2017, 2018, 2019; X. Zhang et al., 2017). However, their accuracy is limited since they mainly use empirical equations to calculate ionospheric delays.…”

Section: Introductionmentioning

confidence: 99%

Method and Validation of Real‐Time Global Ionosphere Modeling Constraint by Multi‐Source GNSS/LEO Data

Chen,

Ren,

et al. 2024

Space Weather

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This study applies the zero‐differenced integer ambiguity method, named PPP‐Fixed, to extract real‐time ionospheric data and eliminate the latencies of rapid/final Global Ionosphere Maps (GIMs). The PPP‐Fixed method is also used to derive ionospheric data for post‐processed GIM generation, named SGG Post‐GIM, combined with low earth orbit satellite data. The obtained hardware delays are applied to revise real‐time ionospheric data. Meanwhile, the estimated multi‐source ionospheric model is regarded as historical data to estimate an ionospheric prediction model for constraint using the semi‐parameter model. Then, the Kalman filter is employed to estimate the parameters to generate real‐time GIM. Finally, the accuracy of estimated real‐time GIM, named SGG RT‐GIM, and SGG Post‐GIM is assessed. During the experimental period, the mean differences of SGG Post‐GIM and SGG RT‐GIM relative to GIMs provided by the international Global Navigation Satellite System service, named IGSG, are −0.46 and −0.57 Total Electron Content Unit (TECU), respectively. The corresponding Root Mean Square (RMS) values are 1.64 and 3.08 TECU. Over the test period, the mean positioning errors of the single‐frequency precise point positioning corrected by IGSG, SGG Post‐GIM, SGG RT‐GIM, and Klobuchar model are 0.14, 0.19, 0.21, and 0.25 m in the horizontal direction, respectively, while the corresponding errors are 0.36, 0.33, 0.38, and 0.64 m in the up direction. Further, the mean biases of experimental days for the self‐consistency assessment are 0.06, −0.01, and −0.07 TECU for IGSG, SGG Post‐GIM, and SGG RT‐GIM, respectively. The corresponding RMS values are 1.19, 1.15, and 1.57 TECU.

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“…The evaluation of the Klobuchar model in Taiwan worked by Li et al has illustrated that the differences between the Klobuchar model and the measured dualfrequency ionospheric delays vary with solar activity and latitudes also [15]. Chen et al have proposed a Sophisticated Klobuchar Model (SKM) based on Holt-Winters exponential smoothing model over China region and tested for one day using six days of training data sets [16]. This method has focused to fill the missing data for dual-frequency receiver and forecasting ionospheric delays.…”

Section: Introductionmentioning

confidence: 99%

A New Ionospheric Model for Single Frequency GNSS User Applications Using Klobuchar Model Driven by Auto Regressive Moving Average (SAKARMA) Method Over Indian Region

et al. 2020

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The single-frequency users of Global Navigation Satellite System (GNSS) require an effective mathematical model that mitigate the dominant errors due to ionospheric delays. Klobuchar model approximately reduces ionospheric effect up to 50% through its coefficients in the navigation message, which is not sufficient for the GNSS single frequency users at critical applications. Hence, a new model, for Single Frequency GNSS User Applications using Klobuchar model driven by Auto Regressive Moving Average Method (SAKARMA) is proposed to forecast and enhance the precision of ionospheric delay estimations for GNSS users. The hourly VTEC maps are obtained from the Assimilated Indian Regional Vertical Total Electron Content (AIRAVAT) by the process of data assimilation using the Kalman filter exclusively for the Indian region (longitude: 65 • E to 100 • E; latitude: 5 • N to 40 • N) using 26 GPS TEC stations over Indian region. The accuracy of the SAKARMA model is investigated using the AIRAVAT maps for various Indian geographic regions during both geomagnetic quiet and disturbed conditions of September month in 2016 year. Furthermore, in order to test SAKARMA model, a dual-frequency Navigation with Indian Constellation (NavIC) receiver located at the KL Education Foundation, Guntur, India (geographic: 16.37 • N, 80.37 • E; geomagnetic: 7.44 • N, 153.75 • E) is used to collect the observations during 2-12 September 2017. Furthermore, SAKARMA model is also validated with Klobuchar model, Klobuchar-style coefficients provided by the Center for Orbit Determination in Europe (CODE) (CODKlob) Model, BeiDou System (BDS2) Model and NeQuick 2 Model over a low latitude NavIC station in forecasting the ionospheric delays. The experimental results of SAKARMA for NavIC have revealed that the MAPE for proposed SAKARMA model is 9-17% (accuracy: 83-91%), while 34-53% (accuracy: 47-66%) for the Klobuchar model. Thus, the results illustrate that the proposed SAKARMA model is capable of predicting the ionospheric delays for single frequency GNSS/NavIC users. INDEX TERMS GNSS, NavIC, ionospheric delay, Klobuchar model, auto regressive moving average (ARMA) model, SAKARMA model. The associate editor coordinating the review of this manuscript and approving it for publication was Halil Ersin Soken .

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Applicability Analysis of VTEC Derived from the Sophisticated Klobuchar Model in China

Cited by 14 publications

References 32 publications

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

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

Method and Validation of Real‐Time Global Ionosphere Modeling Constraint by Multi‐Source GNSS/LEO Data

A New Ionospheric Model for Single Frequency GNSS User Applications Using Klobuchar Model Driven by Auto Regressive Moving Average (SAKARMA) Method Over Indian Region

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