Ingestion of GIM-derived TEC data for updating IRI-2016 driven by effective IG indices over the European region

Liu, Lei; Yao, Yibin; Zou, Shasha; Kong, Jian; Shan, Lulu; Zhai, Changzhi; Zhao, Chunhua; Wang, Youkun

doi:10.1007/s00190-019-01291-5

Cited by 15 publications

(10 citation statements)

References 44 publications

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“…Liu et al. (2019) ingested the TEC data from Global Ionosphere Maps into the IRI2016 model to improve estimations of TEC, F2 layer maximum electron density (NmF2), and foF2 over European region. In summary, all of these studies show that the data ingestion technique is an effective way to improve the IRI model performance.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, it is very important to improve the performance of the IRI model for different geomagnetic conditions and solar activity periods at different regions, especially at low-latitude regions where very few data have been used for model development (Jones & Gallet, 1962;Nigussie et al, 2012). The Global Positioning System (GPS) measurements of vertical total electron content (TEC) can be ingested into the IRI model to improve the IRI model estimation of TEC and foF2 (Habarulema & Ssessanga, 2017;Hernandez-Pajares et al, 2002;Liu et al, 2019;Ssessanga et al, 2015). Hernandez-Pajares et al (2002) combined the GPS-derived TEC and the IRI model to update the IRI model TEC prediction at middle latitudes.…”

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confidence: 99%

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Performance Evaluation of Modified IRI2016 and Its Application to the 24 hr Ahead Forecast foF2 Mapping Over China

Song

Zhang

et al. 2022

JGR Space Physics

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The F2 layer is the uppermost layer of the ionosphere, which plays an important role for long distance high frequency (HF) communication because of its thickness and minimal attenuation for probing radio waves. The F2 layer critical frequency foF2, which determines the maximum useable frequency in the HF band reflecting from the ionosphere, is one of the most pivotal parameters for the HF communication. However, in the real ionosphere, there are many "anomalies" observed in the F2 layer (Rishbeth, 1998(Rishbeth, , 2000Wills et al., 1994), such as the "winter anomaly," the "seasonal anomaly," the "semi-annual anomaly," and the "annual anomaly" of foF2. In addition, the variation of foF2 also depends on the solar activity (Balan et al., 1998;Yu et al., 2004) and magnetic activity (Stanislawska et al., 2002). Therefore, accurate measurement and determination of foF2 has become a key point for the HF communication systems (Ssessanga et al., 2015;Wichaipanich et al., 2017).However, due to the paucity of ionospheric measurements, a number of empirical ionospheric models have been developed to model and predict the spatial and temporal structures of the ionosphere. The most commonly used global empirical ionospheric model for predicting the foF2 is the International Reference Ionosphere (IRI) model. The IRI model can estimate and predict the ionospheric parameters especially the foF2 with appropriate inputs (Wichaipanich et al., 2017). The IRI model has been steadily improved with newer data and with better mathematical descriptions of ionospheric spatial/temporal variation patterns, and IRI2016 is now the latest version (S. Wang et al., 2016), which can be accessed from https://ccmc.gsfc.nasa.gov/pub/modelweb/ionospheric/iri/ iri2016 (Bilitza et al., 2017).The IRI model normally gives a complete spatial and temporal representation of the average state of the ionosphere based on available data from the ionosonde stations, incoherent scatter radars, alouette topside sounders, rockets and in situ measurements on several satellites (

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Section: Introductionmentioning

confidence: 99%

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confidence: 99%

Performance Evaluation of Modified IRI2016 and Its Application to the 24 hr Ahead Forecast foF2 Mapping Over China

Song

Zhang

et al. 2022

JGR Space Physics

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“…Since the ionosphere is the region of the atmosphere that is directly affected by solar activity, the GIMs are also applied to space weather and climate analysis (Jin et al 2017). Additionally, space weather studies use empirical ionosphere models, such as NeQuick 2 or the International Reference Ionosphere (IRI), which are often updated/validated using GIMs (Nava et al 2011;Liu et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Validation of GNSS-derived global ionosphere maps for different solar activity levels: case studies for years 2014 and 2018

et al. 2021

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Ionosphere Associate Analysis Centers (IAACs) of the International GNSS Service (IGS) independently produce global ionosphere maps (GIMs) of the total electron content (TEC). The GIMs are based on different modeling techniques, resulting in different TEC levels and accuracies. In this study, we evaluated the accuracy and consistency of the IAAC GIMs during high (2014) and low (2018) solar activity periods of the 24th solar cycle. In our study, we applied two different evaluation methods. First, we carried out a comparison of the GIM-derived slant TEC (STEC) with carrier phase geometry-free combination of GNSS signals obtained from 25 globally distributed stations. Second, vertical TEC (VTEC) from GIMs was compared to altimetry-derived VTEC obtained from the Jason-2 and Jason-3 satellites and complemented for plasmaspheric TEC. The analyzed GIMs obtained STEC RMS values reaching from 1.98 to 3.00 TECU and from 0.96 to 1.29 TECU during 2014 and 2018, respectively. The comparison to altimetry data resulted in VTEC STD values that varied from 3.61 to 5.97 TECU and from 1.92 to 2.78 TECU during 2014 and 2018, respectively. The results show that among the IAACs, the Center for Orbit Determination in Europe global maps performed best in low and high solar activity periods. However, the highest accuracy was obtained by a non-IGS product—UQRG GIMs provided by Universitat Politècnica de Catalunya. It was also shown that the best results were obtained using a modified single layer model mapping function and that the map time interval has a relatively small influence on the resulting map accuracy.

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“…For example, ErCha et al, using IRI as the background model and GNSS data as the observation values, applied a three-dimensional variational method and the Kalman filter to assimilate the ionospheric data, and generated quasi-real-time predictions of ionospheric TEC over China and adjacent areas [32]. Methods in the second category rely on the ingestion of GNSS data to minimize the difference between the high-precision TEC values extracted from GNSS data and the TEC results output from the IRI model by adjusting the IG 12 index and the RZ 12 index to improve the model's accuracy [35][36][37][38][39][40][41]. For instance, Nicholas Ssessanga et al ingested GNSS-TEC data into the IRI-2012 model, and by adjusting the IG 12 and RZ 12 indices simultaneously, obtained a modified IRI-2012 model that was more accurate than the original model in estimating TEC [39].…”

Section: Introductionmentioning

confidence: 99%

“…For instance, Nicholas Ssessanga et al ingested GNSS-TEC data into the IRI-2012 model, and by adjusting the IG 12 and RZ 12 indices simultaneously, obtained a modified IRI-2012 model that was more accurate than the original model in estimating TEC [39]. Lei Liu et al incorporated global ionosphere map (GIM) TEC data from Europe into IRI-2016 and retrieved the effective ionospheric index per hour at different latitudes to improve the accuracy of the IRI model [41]. Notably, in this method, the updated IG 12 /RZ 12 index is a parameter that includes the error of the CCIR/URSI coefficient rather than a means of characterizing the original sunspot and ionospheric variation activities.…”

Section: Introductionmentioning

confidence: 99%

Algorithm Research Using GNSS-TEC Data to Calibrate TEC Calculated by the IRI-2016 Model over China

et al. 2021

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The International Reference Ionosphere (IRI) is an empirical model widely used to describe ionospheric characteristics. In the previous research, high-precision total ionospheric electron content (TEC) data derived from global navigation satellite system (GNSS) data were used to adjust the ionospheric global index IG12 used as a driving parameter in the standard IRI model; thus, the errors between IRI-TEC and GNSS-TEC were minimized, and IRI-TEC was calibrated by modifying IRI with the updated IG12 index (IG-up). This paper investigates various interpolation strategies for IG-up values calculated from GNSS reference stations and the calibrated TEC accuracy achieved using the modified IRI-2016 model with the interpolated IG-up values as driving parameters. Experimental results from 2015 and 2019 show that interpolating IG-up with a 2.5° × 5° spatial grid and a 1-h time resolution drives IRI-2016 to generate ionospheric TEC values consistent with GNSS-TEC. For 2015 and 2019, the mean absolute error (MAE) of the modified IRI-TEC is improved by 78.57% and 77.42%, respectively, and the root mean square error (RMSE) is improved by 78.79% and 77.14%, respectively. The corresponding correlations of the linear regression between GNSS-TEC and the modified IRI-TEC are 0.986 and 0.966, more than 0.2 higher than with the standard IRI-TEC.

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Ingestion of GIM-derived TEC data for updating IRI-2016 driven by effective IG indices over the European region

Cited by 15 publications

References 44 publications

Performance Evaluation of Modified IRI2016 and Its Application to the 24 hr Ahead Forecast foF2 Mapping Over China

Performance Evaluation of Modified IRI2016 and Its Application to the 24 hr Ahead Forecast foF2 Mapping Over China

Validation of GNSS-derived global ionosphere maps for different solar activity levels: case studies for years 2014 and 2018

Algorithm Research Using GNSS-TEC Data to Calibrate TEC Calculated by the IRI-2016 Model over China

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