NeQuick 2 and IRI Plas VTEC predictions for low latitude and South American sector

Ezquer, Rodolfo Gerardo; Scidá, L.A.; Migoya-Orué, Yenca; Nava, B.; Cabrera, Miguel A.; Brunini, C.

doi:10.1016/j.asr.2017.10.003

Cited by 43 publications

(36 citation statements)

References 55 publications

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“…NeQuick model depends on daily solar radio flux (NOAA-NGDC, 2017), so it is able to show day-to-day variations in the range ionospheric delay corrections. The findings of this study are in agreement with other recent studies (e.g., Ezquer et al (2018) and Okoh et al (2018)) where performance of NeQuick2 model is better for highest latitudes and worst for lowest latitudes. Also, the NeQuick2 underestimates the TEC values during the high solar activity years and overestimates it during local daytime for low and moderate solar activity years.…”

Section: Discussionsupporting

confidence: 94%

“…The model's topside adopted a new formulation of the shape parameter k. A number of recent studies investigated the behavior of the NeQuick2 model such as ), Okoh et al (2018 and Perna et al (2018). Ezquer et al (2018) analyzed the capability of the NeQuick 2 and IRI Plas models to predict VTEC over the low latitude and South American sector using vertical total electron content (VTEC) measurements obtained from GPS satellite signals. They concluded that performance of IRI Plas model for low latitude stations is better than that of NeQuick2 model and performance of NeQuick2 model is better than that of IRI Plas model for highest latitudes.…”

Section: Nequick2 Modelmentioning

confidence: 99%

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Nequick2 Model Behaviour for Global Ionospheric Delay Mitigation During Solar Cycle-24

Farah

2018

Artificial Satellites

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The ionospheric delay is the major current source of potential range delay for single-frequency GNSS users. Different ionospheric delay mitigation methods have been developed to mitigate the ionospheric delay effects for single-frequency users. The NeQuick is a quick-run ionospheric electron density model particularly designed for trans-ionospheric propagation applications developed at the Aeronomy and Radio propagation Laboratory of the Abdus Salam International Centre for Theoretical Physics (ICTP), Italy. NeQuick2 is the latest version of the NeQuick ionosphere electron density model. NeQuick model been used by the European Space Agency (ESA) European Geostationary Navigation Overlay Service (EGNOS) project for assessment analysis and has been adopted for single-frequency positioning applications in the frame work of the European satellite navigation system (Galileo). NeQuick2 model adopted modifications related to the modeling of the F1 layer peak electron density, height and thickness parameter. Also, a new formulation of the shape parameter k has been adopted. This paper presents a global study for the behavior of the modified NeQuick2 model. The zenith ionospheric range delay correction by the model has been assessed using the highly accurate IGS-Global Ionospheric Maps (IGS-GIMs) for two different-latitude stations (Aswan, Egypt) (low-latitude) (24.1 o N) and (Helsinki, Finland) (high-latitude) (60.2 o N). The study was carried out during current solar cycle-24 over three different months that each of them reflects a different state of solar activity. It can be concluded that NeQuick2 model globally presents overestimation for ionospheric delay for quiet and medium ionospheric activity states respectively, while the model presents underestimation for high activity state of the ionosphere layer.Key Words 1. NeQuick2 model 2. Single frequency 3. Ionospheric delay mitigation 4. Solar cycle-24 130 LOWER-LATITUDE GEOGRAPHIC REGION(ASWAN) station represents the lower-latitude geographic regions which characterized with the highest values of the peak-electron density. Figures (1 to 3) show the zenith range delay correction from NeQuick2 model and IGS-GIMs as a reference. The zenith range delay differences between the model and the IGS-GIMs are shown in Figures 4, 5 and 6. 131 132 133 134

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Section: Discussionsupporting

confidence: 94%

Section: Nequick2 Modelmentioning

confidence: 99%

Nequick2 Model Behaviour for Global Ionospheric Delay Mitigation During Solar Cycle-24

Farah

2018

Artificial Satellites

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“…For instance, Scida et al (2012), using the GPS data stored during the June and September equinox in 1999 (high solar activity year) ranging from 18.4 to 64.7 in geographic latitude noted that, the IRI 2007 model generally tends to underestimate and then overestimate the VTEC variations in moving from low to high latitudes. Similarly, Ezquer et al (2017) used the previous version of the models (IRI-Plas 2015) with previous measurement data stored in 1999 focusing in June and September. They noted that the IRI-Plas 2015 and NeQuick 2 models provide better forecasts for the equatorial and low latitude region in the June solstice with small underestimations being observed by both models.…”

Section: 1029/2019rs007047mentioning

confidence: 99%

Comparison of Performance of the IRI 2016, IRI‐Plas 2017, and NeQuick 2 Models During Different Solar Activity (2013–2018) Years Over South American Sector

Tariku

2020

Radio Science

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This study mainly focuses on the prediction of the variability of the vertical total electron content (VTEC) and validation of the new versions of the International Reference Ionosphere (IRI 2016), IRI extended to the plasmasphere (IRI‐Plas 2017), and NeQuick 2 models over the equatorial anomaly region during 2013–2018. The Global Positioning System (GPS)‐derived VTEC data inferred from the South American equatorial region during the relatively high (2013–2014), descending (2015–2016), and low (2017–2018) solar activity years have been considered as measurements to validate the models. The modeled (IRI 2016, IRI‐Plas 2017, and NeQuick 2) VTEC values are generally larger than the GPS‐derived VTEC values, with the highest overestimation being illustrated by the IRI‐Plas 2017 model. Small underestimations are also observed in employing the IRI 2016 and NeQuick 2 models, especially when the solar activity increases. The highest root‐mean‐square Deviations (RMSDs) reaching up to 10 TEC units (TECU) is observed in some hours while using the IRI‐Plas 2017 model during the high solar activity years. RMSDs less than 5 TECU are also observed on most of the hours while utilizing the model during all solar activity years. However, RMSD values less than 2 TECU (while using the NeQuick 2 model) and less than 1 TECU (while using the IRI 2016 model) are seen on most of the hours during 2013–2018. This shows that the performance of the IRI 2016 and NeQuick 2 is better than the IRI‐Plas 2017 with the IRI 2016 model consistently revealing the best in all solar activity years.

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“…The empirical model, such as the international reference ionosphere (IRI) Model and NeQuick ionospheric model (NeQuick) have been used by the practitioner to predict the ionosphere condition to monitor the space weather [3]. However, for the areas located in the equatorial and lowlatitude region, there is a large deviation of total electron content (TEC) prediction due to lack of observation data contributed from these areas and regions during the development of the model [4].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review

Salleh

Yuhaniz

Sabri

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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This paper studies the machine learning techniques that can be used to enhance the prediction method of the ionosphere for space weather monitoring. Previously, the empirical model is used. However, there is a large deviation of the total electron content of ionosphere data for the areas located in the equatorial and low-latitude regions due to the lack of observation data contributed by these areas during the development of the empirical model. The machine learning technique is an alternative method used to develop the predictive model. Thus, in this study, the machine learning techniques that can be applied are investigated. The aim is to improve the predictive model in terms of reducing the total electron content deviation, increasing the accuracy and minimizing the error. In this review, the techniques used in previous works will be compared. The artificial neural network is found to be a suitable technique and the most favorable from the review conducted. Also, this technique can provide an accurate model for time series data and fewer errors compared to other techniques. However, due to the size and complexity of the data, the deep neural network technique that is an improved artificial neural network technique is suggested. By using this technique, an accurate ionosphere predictive model in equatorial and low region area is expected. In the future, this study will analyze further by using computing tools and real-time data.

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NeQuick 2 and IRI Plas VTEC predictions for low latitude and South American sector

Cited by 43 publications

References 55 publications

Nequick2 Model Behaviour for Global Ionospheric Delay Mitigation During Solar Cycle-24

Nequick2 Model Behaviour for Global Ionospheric Delay Mitigation During Solar Cycle-24

Comparison of Performance of the IRI 2016, IRI‐Plas 2017, and NeQuick 2 Models During Different Solar Activity (2013–2018) Years Over South American Sector

Enhancing Prediction Method of Ionosphere for Space Weather Monitoring Using Machine Learning Approaches: A Review

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