Variability of ionospheric parameters during solar minimum and maximum activity and assessment of IRI model

Sharma, Dinesh K.; Aggarwal, Malini; Bardhan, Ananna

doi:10.1016/j.asr.2016.11.027

Cited by 7 publications

(3 citation statements)

References 31 publications

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“…One of such efforts includes the development and continuous update of global ionospheric models like the NeQuick (Hochegger et al, 2000;Nava et al, 2008;Radicella & Leitinger, 2001) and the International Reference Ionosphere (IRI; Bilitza, 2001;Bilitza & Reinisch, 2008) model. Although these models have been adequately shown to be reliable in predicting the trends and patterns of variations in the ionosphere (e.g., Aggarwal, 2011;Sharma et al, 2017; and many more), a number of research results (e.g., Adewale et al, 2011;Akala et al, 2013Akala et al, , 2015Habarulema et al, 2007Habarulema et al, , 2009Okoh, McKinnel, et al, 2015;Okoh, Onwuneme, et al, 2018;Oyeyemi et al, 2018;Olwendo et al, 2012;Rabiu et al, 2011Rabiu et al, , 2014 have reported certain shortfalls in the African ionosphere. One often cited reason for this shortfall is that the volume of data from the African region used in the development of these models is relatively small, and this is owing to the paucity of ionospheric data available from the region (Bilitza & Reinisch, 2008;McKinnell, 2002;Okoh et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

A Neural Network‐Based Ionospheric Model Over Africa From Constellation Observing System for Meteorology, Ionosphere, and Climate and Ground Global Positioning System Observations

et al. 2019

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The first regional total electron content (TEC) model over the entire African region (known as AfriTEC model) using empirical observations is developed and presented. Artificial neural networks were used to train TEC observations obtained from Global Positioning System receivers, both on ground and onboard the Constellation Observing System for Meteorology, Ionosphere, and Climate satellites for the African region from years 2000 to 2017. The neural network training was implemented using inputs that enabled the networks to learn diurnal variations, seasonal variations, spatial variations, and variations that are connected with the level of solar activity, for quiet geomagnetic conditions (−20 nT ≤ Dst ≤ 20 nT). The effectiveness of three solar activity indices (sunspot number, solar radio flux at 10.7-cm wavelength [F10.7], and solar ultraviolet [UV] flux at 1 AU) for the neural network trainings was tested. The F10.7 and UV were more effective, and the F10.7 was used as it gave the least errors on the validation data set used. Equatorial anomaly simulations show a reduced occurrence during the June solstice season. The distance of separation between the anomaly crests is typically in the range from about 11.5 ± 1.0°to 16.0 ± 1.0°. The separation is observed to widen as solar activity levels increase. During the December solstice, the anomaly region shifts southwards of the equinox locations; in year 2012, the trough shifted by about 1.5°and the southern crest shifted by over 2.5°. Key Points:• The first regional TEC model over the entire African region using empirical observations is developed • The model offers opportunities to conduct high spatial resolution investigations over the African region • EIA occurrence is reduced during the June solstice, and the anomaly region shifts southwards during December solstice Data used in this work include GPS data, indices for solar and geomagnetic activities, and data from ionospheric models used to comparatively verify/validate the model developed. Figure 7. RMSE variations for predictions of the AfriTEC model using the test data set under conditions of varying (a) latitudes, (b) F10.7 values, (c) local times, and (d) days of the years.Figure 11. (a) Sample TEC profile for longitude 20°E illustrating the determination of anomaly crest and trough locations. The illustrated profile is for the March equinox day of year 2012. (b) to (d) are spatial simulations of TEC from the AfriTEC model for 13:00 UT of day number 79 of years 2009, 2012, and 2014, respectively. The F10.7 values are respectively 68, 101, and 150.

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Section: Introductionmentioning

confidence: 99%

A Neural Network‐Based Ionospheric Model Over Africa From Constellation Observing System for Meteorology, Ionosphere, and Climate and Ground Global Positioning System Observations

et al. 2019

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“…These simulations were done either for 1,000 or 2,000 different true input parameters ( x true ) uniformly selected between the following parameter ranges: electron density, 10 9 ≤ N e ≤ 10 12 m −3 , electron temperature, 300 ≤ T e ≤ 5000 ° K, ion temperature, 300 ≤ T i ≤ 3000 ° K, electron‐to‐ion temperature ratio, 0.1 ≤ T e / T i ≤ 5, ion drift velocity, −250 ≤ V i ≤ 250 m/s, and ion composition, 0 ≤ p ≤ 1. The ranges of N e , T e , and T i parameters selected resemble the maximum and minimum values of measurements obtained by SROSS‐C2 satellite and models above the ionospheric F 2 layer peak at low‐latitude regions with different solar conditions (Sharma et al, ). The ranges of T e / T i and V i used were larger than the typical measurements obtained at standard midlatitude ionospheric conditions shown in (Scherliess et al, ; Wand, ).…”

Section: Simulation Methodsmentioning

confidence: 99%

Determination of the Signal Fluctuation Threshold of the Temperature‐Ion Composition Ambiguity Problem Using Monte Carlo Simulations

Martínez-Ledesma

Quezada

2019

JGR Space Physics

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Unambiguously estimating the plasma parameters of the ionosphere at altitudes between 130 and 300 km presents a problem for the incoherent scatter radar (ISR). At these ranges, ISR is unable to distinguish between different mixtures of molecular ions (NO + and O 2 + ) and atomic oxygen ions (O + ). Common solutions to this problem are either to employ empirical or theoretical models of the ionosphere or to add a priori known plasma parameter information obtained from the plasma line of the ISR spectrum. Studies have demonstrated that plasma parameters can be unambiguously estimated in almost noiseless scenarios, not commonly feasible during routine monitoring. In this study, we define a theoretical framework to quantify the ambiguity problem and determine the maximum signal fluctuation levels of the ISR signal to unambiguously estimate plasma parameters. We conduct Monte Carlo simulations for different plasma parameters to evaluate the estimation performance of the most commonly used nonlinear least squares optimization algorithm. Results are shown as probability curves of valid convergence and correct estimation. We use simulations to quantify the estimation error when using ionospheric models as initial conditions of the optimization algorithm. We also determine the contribution to the estimation process of different combinations of parameters known from the plasma line, the particular contribution of each plasma parameter, and the effect of increasing the level of uncertainty of the parameters known a priori. Results suggest that knowing a priori both electron density and electron temperature parameters allows an unambiguous estimation even at high fluctuation levels.

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“…Different researchers have assessed the variability of vertical total electron content (VTEC) and performance of the models over different regions at different solar activities. The performance of the IRI model has been assessed over different equatorial and low-latitude regions in the globe at different solar activities (e.g., Asmare et al, 2014;Mengistu et al, 2018;Perna et al, 2018;Rao et al, 2018;Sharma et al, 2017;Tariku, 2015Tariku, , 2019. For example, Asmare et al (2014) and Tariku, (2015) showed that the IRI 2012 model generally tend to overestimate the VTEC variations over African equatorial anomaly regions at different solar activity phases.…”

Section: Introductionmentioning

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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Variability of ionospheric parameters during solar minimum and maximum activity and assessment of IRI model

Cited by 7 publications

References 31 publications

A Neural Network‐Based Ionospheric Model Over Africa From Constellation Observing System for Meteorology, Ionosphere, and Climate and Ground Global Positioning System Observations

A Neural Network‐Based Ionospheric Model Over Africa From Constellation Observing System for Meteorology, Ionosphere, and Climate and Ground Global Positioning System Observations

Determination of the Signal Fluctuation Threshold of the Temperature‐Ion Composition Ambiguity Problem Using Monte Carlo Simulations

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

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