Performance of GPS-TEC assisted NTCM-model to describe the East-African equatorial ionosphere

Getahun, Biruk; Nigussie, Melessew

doi:10.1016/j.asr.2021.08.029

Cited by 3 publications

(9 citation statements)

References 20 publications

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“…The minimum (the maximum) STD for NTCM, NeQuick2, and NN are 2.25 (4.206) TECU, 3.988 (6.209) TECU, and 3.602 (4.696) TECU in their order. These results are comparable with the previous studies (Nigussie et al, 2012;Getahun and Nigussie, 2021;Tebabal et al,2018) for these models, except for differences resulting from the data used. In general, the models reproduced VTEC maps that capture the behavior of the scatter plots (experimental VTEC).…”

Section: Datasupporting

confidence: 89%

“…Studies showed that TEC ingestion into the NeQuick2 model improves its performance in reproducing spatial and temporal variations of the ionosphere TEC and electron density (Nava et al, 2006(Nava et al, , 2011Brunini et al, 2011;Nigussie et al, 2012;Ercha et al, 2018). Similarly, Getahun and Nigussie (2021) showed that adapting the NTCM model to quiet days TEC in the East-Africa ionosphere improved its performance substantially. Also, to enhance ionosphere characterization for scientific research, and mitigate its effect on positioning and communication, the Neural Network (NN) model has been suggested by various studies (Habarulema et al, 2007(Habarulema et al, , 2009Tebabal et al, 2018Tebabal et al, , 2019.…”

Section: Introductionmentioning

confidence: 90%

“…Local and instantaneous values of F10.7 defined for NeQuick2 effective ionization (symbol Az) and NTCM ionization driving index (symbol Az) are F10.7 values that minimize the difference between an experimental and the corresponding modeled VTEC. Many works of literature (Nigussie et al, 2012, Ercha et al, 2018, Nava et al, 2006, Getahun and Nigussie, 2021 reviewed that Id or Az is estimated by minimizing the root mean square error (RMSE) defined in equation 5.…”

Section: Data Ingestionmentioning

confidence: 99%

“…These models have shown different performance in their standard application in describing the observed TEC; for example, Nigussie et al (2013) have compared the performance of NeQuick2 and IRI-2007 models in describing the East-African ionospheric TEC and found, for both models, the modeled and experimental VTEC has shown significant discrepancy. The performance of NTCM in its standard applications has shown also a significant discrepancy to the experimental VTEC (Getahun and Nigussie, 2021). The weak performances of these models in their standard applications indicate limitations in reproducing and forecasting the local and instantaneous conditions, especially in regions of unique irregularities are common (Scherliess et al, 2004;Sojka et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

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Comparative assessment of the performances of GPS-TEC assisted NTCM, NeQuick2 and Neural Network models to describe the East-African equatorial Ionosphere

Gelaw

Nigussie

Tebabal³

2022

Preprint

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Different ionospheric climatological models such as NeQuick2 and NTCM have been developed to mitigate the ionosphere impact on the trans-ionosphere propagating radio wave. Moreover, the Neural Network (NN) is used to model and characterize the ionosphere. However, no one has compared the performances of NeQuick2, NTCM, and NN after adapting to GPS TEC.This study evaluates their performances in the East-African region in 2013 and 2015. It has been done by computing their drivers (effective ionization level, Az for NeQuick2 and ionization driving index, Id for NTCM) through least-square fitting to TEC observation sense. NN-algorithm has also been trained and tested with observed TEC used for NeQuick2 and NTCM adaptation. The annual performance test has shown that the correlation coefficient (R) values between observed and NTCM modeled TEC, after an adaption, are better than the corresponding values obtained from NeQuick2 and NN. It also shows that the standard deviations (STD) and root-mean-square errors (RMSE) obtained for NTCM are smaller than the STD and RMSE computed for NeQuick2 and NN. On the other hand, the daily performance test of now-casting and predicting showed that the NN performs the best, followed by NTCM. However, the 1-hour prediction test showed that NTCM performs the best among the models considered in this study. Hosted fileessoar.10512098.1.docx available at https://authorea.com/users/524617/articles/595516comparative-assessment-of-the-performances-of-gps-tec-assisted-ntcm-nequick2-and-neuralnetwork-models-to-describe-the-east-african-equatorial-ionosphere Comparative assessment of the performances of GPS-TEC assisted NTCM, NeQuick2 and Neural Network models to describe the East-African equatorial Ionosphere

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Section: Datasupporting

confidence: 89%

Section: Introductionmentioning

confidence: 90%

Section: Data Ingestionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Comparative assessment of the performances of GPS-TEC assisted NTCM, NeQuick2 and Neural Network models to describe the East-African equatorial Ionosphere

Gelaw

Nigussie

Tebabal³

2022

Preprint

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“…NTCM is an empirical model developed by Deutsches Zentrum für Luft- und Raumfahrt (DLR) [ 30 ]; it is a single layer TEC model similar to Klobuchar. Several modifications of the model have been proposed [ 19 , 20 , 31 , 32 ]; however, all the versions share a common approach: TECs are estimated as a function of geophysical parameters such as the geographic location, time and solar activity. The version used in this work is identified as NTCM-G and is the one described in [ 13 ]; the model has been implemented in Matlab and the module has been included in a customized navigation algorithm.…”

Section: Absolute Positioning Computation and Ionospheric Modelsmentioning

confidence: 99%

Neustrelitz Total Electron Content Model for Galileo Performance: A Position Domain Analysis

Gioia¹,

Angrisano

Gaglione

2023

Sensors

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Ionospheric error is one of the largest errors affecting global navigation satellite system (GNSS) users in open-sky conditions. This error can be mitigated using different approaches including dual-frequency measurements and corrections from augmentation systems. Although the adoption of multi-frequency devices has increased in recent years, most GNSS devices are still single-frequency standalone receivers. For these devices, the most used approach to correct ionospheric delays is to rely on a model. Recently, the empirical model Neustrelitz Total Electron Content Model for Galileo (NTCM-G) has been proposed as an alternative to Klobuchar and NeQuick-G (currently adopted by GPS and Galileo, respectively). While the latter outperforms the Klobuchar model, it requires a significantly higher computational load, which can limit its exploitation in some market segments. NTCM-G has a performance close to that of NeQuick-G and it shares with Klobuchar the limited computation load; the adoption of this model is emerging as a trade-off between performance and complexity. The performance of the three algorithms is assessed in the position domain using data for different geomagnetic locations and different solar activities and their execution time is also analysed. From the test results, it has emerged that in low- and medium-solar-activity conditions, NTCM-G provides slightly better performance, while NeQuick-G has better performance with intense solar activity. The NTCM-G computational load is significantly lower with respect to that of NeQuick-G and is comparable with that of Klobuchar.

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Comparative Assessment of the Performances of GPS‐TEC Assisted NTCM, NeQuick2 and Neural Network Models to Describe the East‐African Equatorial Ionosphere

2023

Self Cite

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The ionosphere is part of the upper atmosphere of Earth where EUV, X-ray radiations, and energetic particles from the sun ionize the atoms and molecules. It is a region where free electrons occur in a high enough density to influence the propagation of radio waves with measurable effects. It is one of the highly variable regions of the Earth-upper atmosphere. Nowadays, society has become reliant on systems that work with communication of the satellites whose performance is dependent on the state of the ionosphere. The ionosphere causes a significant error in single-frequency GPS navigation (Davies, 1990;Tiwari et al., 2013). The temporal and spatial variation of electron density (total electron content, TEC) of the ionosphere affects the GPS signal propagation by introducing delay (slowing down and bending propagation) and scintillation (Jakowski et al., 2012;Opperman et al., 2007;Tulunay et al., 2004Tulunay et al., , 2006. Understanding the ionosphere dynamics and developing a model for nowcasting and forecasting its TEC might mitigate the ionosphere effect. In this regard, the interest in specifying the ionosphere TEC is not only for climatic-like situations but also for weather-like behavior.Global empirical models such as IRI (Bilitza, 2018), NeQuick2 (Nava et al., 2008), and NTCM (Jakowski et al., 2011) are good in capturing the median behavior of the ionosphere. These models have been designed for scientific study and radio communication applications. For example, NeQUick2 is adopted for TEC prediction to improve communication quality for International Telecommunication Union Radiocommunication Sector (ITU-R). It is also approved to be used for single frequency ionospheric error correction in the framework of GALILEO (Nigussie et al., 2012 and references therein). These models have shown different performance in their standard application in describing the observed TEC; for example, Nigussie et al. ( 2013) have compared the performance of NeQuick2 and IRI-2007 models in describing the East-African ionospheric TEC and found, for both models, the modeled and experimental VTEC has shown significant discrepancy. The performance of NTCM in its standard applications has shown also a significant discrepancy to the experimental VTEC (Getahun & Nigussie, 2021). The weak performances of these models in their standard applications indicate limitations in reproducing and forecasting the local and instantaneous conditions, especially in regions of unique irregularities are common (Scherliess et al., 2004;Sojka et al., 2007).

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Performance of GPS-TEC assisted NTCM-model to describe the East-African equatorial ionosphere

Cited by 3 publications

References 20 publications

Comparative assessment of the performances of GPS-TEC assisted NTCM, NeQuick2 and Neural Network models to describe the East-African equatorial Ionosphere

Comparative assessment of the performances of GPS-TEC assisted NTCM, NeQuick2 and Neural Network models to describe the East-African equatorial Ionosphere

Neustrelitz Total Electron Content Model for Galileo Performance: A Position Domain Analysis

Comparative Assessment of the Performances of GPS‐TEC Assisted NTCM, NeQuick2 and Neural Network Models to Describe the East‐African Equatorial Ionosphere

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