Determining regional ionospheric model and comparing with global models

Başçiftçi, Fuat; İnal, Cevat; Yıldırım, Ömer; Bülbül, Sercan

doi:10.15292//geodetski-vestnik.2017.03.427-440

Cited by 7 publications

(9 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It can be found that the TEC values predicted by the HASR are significantly greater than the Kriging method and closer to the observed values under the premise of basically maintaining the same changing trend. Similarly, the predicted values of the IRI model in Europe is also much smaller than the observed value (Basciftci et al., 2017), which further demonstrates the correctness and necessity of the HASR method.…”

Section: Validation and Analysismentioning

confidence: 78%

“…Considering that TEC at different stations has various sampling intervals, and this paper focuses on the modeling of spatial characteristics of TEC, to ensure the uniformity of data format, we choose 60 min as the unified sampling interval. From the previous literature reading and background research, it was found that the accuracy of the IRI model in characterizing TEC dynamic variation characteristics during the ionospheric quiet period in the European mid‐latitude region still needs to be improved (Basciftci et al., 2017). As shown in Figure 1, the modeling region of this paper covers 30°N−60°N latitude and 15°W−35°E longitude, and the spatial resolution is 1° × 1°.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction

Wang,

Liu,

Shi

2023

Space Weather

View full text Add to dashboard Cite

In order to achieve more accurate spatial reconstruction of ionospheric total electron content (TEC) and promote improved satellite positioning and ranging applications, a high accuracy spatial reconstruction (HASR) method for TEC is proposed based on the surface theory. The core theory of this method is as follows: (a) Any surface can be uniquely determined by its first and second fundamental quantities; (b) By direct difference approximation, differential equations are transformed into algebraic equations to solve Gauss equations faster. At the same time, taking parts of Europe as an example, the proposed HASR method is used to determine the correlation coefficients and the number of iterations of the model by using the relative root mean square error (RRMSE) as the evaluation criterion. The statistical results show that the TEC predicted by the HASR method is highly consistent with the actual observed values of ionospheric observation stations, and the prediction RRMSE is 9.75%. Compared with the Kriging interpolation with scale factor, the prediction accuracy of the HASR method is improved by 8.5%. We hope this method can provide ideas for the spatial reconstruction of other ionospheric parameters and further promote the realization of complete and accurate space weather forecast.

show abstract

Section: Validation and Analysismentioning

confidence: 78%

Section: Methodsmentioning

confidence: 99%

A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction

Wang,

Liu,

Shi

2023

Space Weather

View full text Add to dashboard Cite

show abstract

“…Before large volcanic eruptions, the rate of occurrence of TEC anomalies are related to volcanic type and geographical location (Li et al, 2016). Thus, the effects of earthquakes and volcanic eruptions may be monitored (Basciftci et al, 2017b).…”

Section: L4 = L1 -L2mentioning

confidence: 99%

“…Many institutions across the world produce a global ionospheric TEC map (GIM). These institutions are: ESA / ESOC (European Space Operations Centre, Germany), JPL (Jet Propulsion Laboratory, California), NOAA (National Oceanic and Atmospheric Office, USA), CODE (Centre for Orbit Determination in Europe, Switzerland), DLR (Fernerkundungstation Neustrelitz, Germany), NRCan (Natural Resources, Canada), ROB (Belgium Royal Observatory, Belgium), UNB (New Brunswick University, Canada), and UPC (Catalan Polytechnic University) (Schaer, 1999;Basciftci et al, 2017b). A global ionosphere map (GIM) is published in IONEX (IONosphere map EXchange) format.…”

Section: Ionospheric Models Presented By the International Gnss Service (Igs)mentioning

confidence: 99%

Comparison of regional and global TEC values: Turkey model

Başçiftçi

İnal

Yıldırım

et al. 2018

International Journal of Engineering and Geosciences

Self Cite

View full text Add to dashboard Cite

The ionosphere is a layer of atmosphere 60 km to 1100 km above the earth and is composed of solar rays and ionized gases. The ionosphere is an important layer affecting Global Navigation Satellite System (GNSS) measures. The quality of GNSS measures is directly related to the changes in the ionosphere. For this reason, monitoring changes in the ionosphere is quite important. One of the important parameters expressing the characteristic of the ionosphere is the Total Electron Content (TEC), which is a function of electron density exhibiting transformation with solar radiation.In this study, 68 stations including 56 Continuously Operating Reference Stations-Turkey (CORS-TR) stations and also IGS stations were taken for evaluation. Bernese v5.2 GNSS software developed by Bern University of Switzerland was employed at the evaluation stage. From 2009 until 2015, TEC values were calculated at two hourly intervals, one day for each month. In the study, where a Single Layer Model was used, TEC values obtained from GNSS measurements have been compared with the TEC (IRI-2012 TEC) values obtained from the global ionosphere map (GIM-TEC) and the international ionosphere reference model programme published by the Centre for Orbit Determination in Europe (CODE), the European Space Agency (ESA), and the Jet Propulsion Laboratory (JPL). As a result, the regional (RIM) TEC values and the global (CODE, ESA, JPL) TEC values showed a large similarity, and the IRI obtained TEC values remained lower than these four values was observed. Correlation coefficient was calculated to determine the relationship between regional TEC values obtained after the evaluation and global TEC values. There is a positive and quite high correlation between the regional (RIM) TEC values produced by the calculated correlation coefficients and the global (CODE, ESA, JPL) TEC and IRI TEC values.

show abstract

“…El-Diasty [41] combined a neural network (NN) algorithm and wavelet decomposition algorithm to establish a short-term forecast model of TEC, which can adapt to various ionospheric environments more flexibly and has good robustness. A regional TEC model was established using the single-layer model and proved superior to the IRI model [42]. Constantly updated data measurement methods, data processing techniques, and modeling methods are designed to improve TEC prediction accuracy for military and civilian applications.…”

Section: Introductionmentioning

confidence: 99%

A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map

Liu

Wang²,

Yang³

et al. 2022

Remote Sensing

View full text Add to dashboard Cite

In order to achieve the high-accuracy prediction of the total electron content (TEC) of the regional ionosphere for supporting the application of satellite navigation, positioning, measurement, and controlling, we proposed a modeling method based on machine learning (ML) and use this method to establish an empirical prediction model of TEC for parts of Europe. The model has three main characteristics: (1) The principal component analysis (PCA) is used to separate TEC’s temporal and spatial variation characteristics and to establish its corresponding map, (2) the solar activity parameters of the 12-month mean flux of the solar radio waves at 10.7 cm (F10.712) and the 12-month mean sunspot number (R12) are introduced into the temporal map as independent variables to reflect the temporal variation characteristics of TEC, and (3) The modified Kriging spatial interpolation method is used to achieve the spatial reconstruction of TEC. Finally, the regression learning method is used to determine the coefficients and harmonic numbers of the model by using the root mean square error (RMSE) and its relative value (RRMSE) as the evaluation standard. Specially, the modeling process is easy to understand, and the determined model parameters are interpretable. The statistical results show that the monthly mean values of TEC predicted by the proposed model in this paper are highly consistent with the observed values curve of TEC, and the RRMSE of the predicted results is 12.76%. Furthermore, comparing the proposed model with the IRI model, it can be found that the prediction accuracy of TEC by the proposed model is much higher than that of the IRI model either with CCIR or URSI coefficients, and the improvement is 38.63% and 35.79%, respectively.

show abstract

Determining regional ionospheric model and comparing with global models

Cited by 7 publications

References 5 publications

A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction

A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction

Comparison of regional and global TEC values: Turkey model

A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map

Contact Info

Product

Resources

About