Empirical models of Total Electron Content based on functional fitting over Taiwan during geomagnetic quiet condition
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Abstract. Empirical models of Total Electron Content (TEC) based on functional fitting over Taiwan (120° E, 24° N) have been constructed using data of the Global Positioning System (GPS) from 1998 to 2007 during geomagnetically quiet condition (Dst>−30 nT). The models provide TEC as functions of local time (LT), day of year (DOY) and the solar activity (F), which are represented by 1–162 days mean of F10.7 and EUV. Other models based on median values have been also constructed and compared with the models based on the functional fitting. Under same values of F parameter, the models based on the functional fitting show better accuracy than those based on the median values in all cases. The functional fitting model using daily EUV is the most accurate with 9.2 TECu of root mean square error (RMS) than the 15-days running median with 10.4 TECu RMS and the model of International Reference Ionosphere 2007 (IRI2007) with 14.7 TECu RMS. IRI2007 overestimates TEC when the solar activity is low, and underestimates TEC when the solar activity is high. Though average of 81 days centered running mean of F10.7 and daily F10.7 is often used as indicator of EUV, our result suggests that average of F10.7 mean from 1 to 54 day prior and current day is better than the average of 81 days centered running mean for reproduction of TEC. This paper is for the first time comparing the median based model with the functional fitting model. Results indicate the functional fitting model yielding a better performance than the median based one. Meanwhile we find that the EUV radiation is essential to derive an optimal TEC.Keywords:
International Reference Ionosphere
Root mean square
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International Reference Ionosphere
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Abstract Compared with regional or global total electron content (TEC) empirical models, single‐station TEC empirical models may exhibit higher accuracy in describing TEC spatial and temporal variations for a single station. In this paper, a new single‐station empirical total electron content (TEC) model, called SSM‐month, for the O'Higgins Station in the Antarctic Peninsula is proposed by using Global Positioning System (GPS)‐TEC data from 01 January 2004 to 30 June 2015. The diurnal variation of TEC in the O'Higgins Station may have changing features in different months, sometimes even in opposite forms, because of ionospheric phenomena, such as the Mid‐latitude Summer Nighttime Anomaly (MSNA). To avoid the influence of different diurnal variations, the concept of monthly modeling is proposed in this study. The SSM‐month model, which is established by month (including 12 submodels that correspond to the 12 months), can effectively describe the diurnal variation of TEC in different months. Each submodel of the SSM‐month model exhibits good agreement with GPS‐TEC input data. Overall, the SSM‐month model fits the input data with a bias of 0.03 TECU (total electron content unit, 1 TECU = 10 16 el m −2 ) and a standard deviation of 2.78 TECU. This model, which benefits from the modeling method, can effectively describe the MSNA phenomenon without implementing any modeling correction. TEC data derived from Center for Orbit Determination in Europe global ionosphere maps (CODE GIMs), International Reference Ionosphere 2012 (IRI2012), and NeQuick are compared with the SSM‐month model in the years of 2001 and 2015–2016. Result shows that the SSM‐month model exhibits good consistency with CODE GIMs, which is better than that of IRI2012 and NeQuick, in the O'Higgins Station on the test days.
International Reference Ionosphere
Anomaly (physics)
Empirical modelling
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Ionosonde
International Reference Ionosphere
Anomaly (physics)
Solar minimum
Solar maximum
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Abstract An essential ionosphere parameter that can be applied for ionosphere corrections in radio systems is the ionosphere’s total electron content (TEC). TEC is a crucial parameter for ionospheric correction in the Global Navigation Satellite Systems (GNSS) of positioning, navigation, and radio science. This study uses the artificial neural network (ANN) application to improve the International Reference Ionospheric Model (IRI-2016) TEC maps across Egypt. The study period is based on the data that were accessible between 2013 and 2020. The ANN model input parameters are (year, day, hour, latitude, and longitude). The ANN1 and ANN2 estimate TEC values of the enhanced IRI-2020 and IRI-2016 according to the Center for Orbit Determination in Europe (CODE), respectively. ANN3 and ANN4 estimate TEC values of the enhanced IRI-2020 and IRI-2016 regarding IGS stations data analyzed by GNSS Analysis software for the multi-constellation and multi-frequency Precise Positioning (GAMP) model, respectively. The ANN model’s validations were based on the root mean square error (RMSE), correlation coefficient (CC), and T -test. According to the results, the suggested ANN can accurately predict the TEC over Egypt. In comparison to the IRI model, the TEC maps that the ANN models produced are significantly more in accordance with the related CODE and GAMP TEC maps. These results demonstrate that the developed approach can enhance IRI 2016 and IRI-2020s ability to estimate global TEC maps. For the ANN1 model, the mean CC and RMSE are 0.92, and 5.15 TECU for all the global data sets compared by CODE. On the other hand, the CC and RMSE between IRI-2020 and CODE are 0.847 and 7.67 TECU. For the ANN2, the mean CC and RMSE are 0.87, 5.59 TECU compared by CODE, respectively. Although the CC and RMSE between IRI-2016 and CODE are 0.820 and 9.052 TECU respectively. For the ANN3, the CC and RMSE are 0.830 and 4.87 TECU compared with GAMP for all global data, respectively. On the other hand, the CC and RMSE between IRI-2020 and GAMP are 0.644 and 10.41, respectively. For the ANN4 the CC and RMSE are 0.82, and 5.95 TECU compared with GAMP, respectively. Although the CC and RMSE between IRI-2016 and GAMP are 0.665 and 12.347 TECU respectively.
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Abstract. In this paper, an empirical total electron content (TEC) model and trends in the TEC over the African low-latitude region are presented. GPS-derived TEC data from Malindi, Kenya (geographic coordinates 40.194∘ E, 2.996∘ S), and global ionospheric maps (GIMs) were used. We employed an empirical orthogonal function (EOF) analysis method together with least-squares regression to model the TEC. The EOF-based TEC model was validated through comparisons with GIMs, the GPS-derived TEC and the TEC derived from the International Reference Ionosphere 2016 (IRI-2016) model for selected quiet and storm conditions. The single-station EOF-based TEC model over Malindi satisfactorily reproduced the known diurnal, semiannual and annual variations in the TEC. Comparison of the EOF-based TEC model results with the TEC derived from the IRI-2016 model showed that the EOF-based model predicted the TEC over Malindi with fewer errors than the IRI-2016. For the selected storms, the EOF-based TEC model simulated the storm time TEC response over Malindi better than the IRI-2016. In the case of the regional model, the EOF-based TEC model was able to reproduce the TEC characteristics in the equatorial ionization anomaly region. The EOF-based TEC model was then used as a background for estimating TEC trends. A latitudinal dependence in the trends was observed over the African low-latitude region.
Empirical orthogonal functions
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Empirical modelling
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The ionosphere layer is very important to the communication system and the Total Electron Content (TEC) plays an important role in the study of ionospheric
behavior. This project involves the comparison of TEC simulated using theoretical models and real data during the disturbances due to geomagnetic quiet and the
disturbed day at Equatorial region and Middle Latitude Region. Prediction of TEC is carried out using two theoretical models which are International Reference
Ionosphere 2001(IRI-2001) PC using window version and NeQuick using the sample driver’s slQu.exe. Both theoretical models are the standard model available from the International Telecommunication Union-Radio Sector (ITU-R). The real data in the form of IONospheric map Exchange (IONEX) from the Centre for Atmosphere Determination (CODE) is used for comparison. Results show that the TEC values for Equatorial Region is higher than the Middle Latitude region. It is recommended that
for undergo improvement, both theoretical models (IRI2001 and NeQuick) should include ionospheric disturbance for more reliable TEC calculation.
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Noon
International Reference Ionosphere
Crest
Anomaly (physics)
Low latitude
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International Reference Ionosphere
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We have gathered total electron content (TEC) data from a range of mid‐latitudes and low latitudes and longitudes for a wide range of solar activity. This data was used to evaluate the performance of six publicly available ionospheric models as predictors of total electron content. TEC is important for correcting modern DoD space systems, which propagate radio waves from the earth to satellites, for time delay effects of the ionosphere. The TEC data were obtained from polarimeter receivers located in North America, the Pacific, and the East coast of Asia. The ionospheric models evaluated are (1) the International Reference Ionosphere, (2) the Bent model, (3) the Ionospheric Conductivity and Electron Density model, (4) the Penn State model, (5) the Fully Analytic Ionospheric Model, and (6) a hybrid model consisting of the Union Radio Scientifique Internationale 88 (URSI‐88) coefficients coupled with the Damen‐Hartranft profile model. We will present extensive comparisons between monthly median TEC and model TEC obtained by integrating electron density profiles produced by the six models. These comparisons demonstrate that although most of the models do very well at representing ƒ 0 F 2 , none of them do very well with TEC, probably because of inaccurate representation of the topside profile. We suggest that one approach to obtaining better representations of TEC is the use of ƒ 0 F 2 from the CCIR or URSI‐88 coefficients coupled with a good climatological slab thickness model.
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Space Weather
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Solstice
International Reference Ionosphere
Equinox
Sunset
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