Highlights about the performances of storm-time TEC modelling techniques for low/equatorial and mid-latitude locations

Uwamahoro, Jean; Habarulema, John Bosco; Burešová, Dalia

doi:10.1016/j.asr.2019.01.027

Cited by 10 publications

(6 citation statements)

References 75 publications

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“…To address this specific issue, we made use of the TEC derived from radio occultation (COSMIC) data, scaled it to GPS TEC based on the neural network approach which led to improved coverage. The model performs better over midlatitude compared to low-and equatorial-latitude regions, a result that is consistent with other related literature (Hunt et al, 2000;Okoh et al, 2018;Uwamahoro et al, 2019) due to the complex ionospheric structure in Space Weather low latitudes related (but not limited) to steep electron density gradients, prereversal enhancements, and the presence of equatorial ionization anomaly. It was however very interesting that the accuracy of the developed model was comparable to single station modeling efforts at similar latitudes.…”

Section: Discussionsupporting

confidence: 91%

“…The error plots in Figure 3 point to the reality that the errors are larger when the TEC magnitudes are correspondingly larger, and vice versa. The larger error values obtained in the equatorial region is also potentially linked to the difficulty associated with modeling the region due to large TEC gradients and peculiar phenomena like the equatorial anomaly (Hunt et al, 2000; Okoh et al, 2018; Uwamahoro et al, 2019).…”

Section: Methodsmentioning

confidence: 99%

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Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks

et al. 2020

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This paper presents the development of a storm-time total electron content (TEC) model over the African sector for the first time. The storm criterion used was |Dst| ≥ 50 nT and Kp ≥ 4. We have utilized Global Positioning System (GPS) observations from 2000 to 2018 from about 252 receivers over the African continent and surroundings within spatial coverage of 40°S-40°N latitude and 25°W-60°E longitude. To increase data coverage in areas devoid of ground-based instrumentation including oceans, we used the available radio occultation Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) TEC from 2008 to 2018. The model is based on artificial neural networks which are used to learn the relationship between TEC and the corresponding physical/geophysical input parameters representing factors which influence ionospheric variability. An important result from this effort was the inclusion of the time history of the geomagnetic activity indicators dKp dt and dDst dt which improved TEC modeling by about 5% and 12% in middle and low latitudes, respectively. Overall, the model performs comparatively well with, and sometimes better than, the earlier single station modeling efforts even during quiet conditions. Given that this is a storm-time model, this result is encouraging since it is challenging to model ionospheric parameters during geomagnetically disturbed conditions. Statistically, the average root-mean-square error (RMSE) between modeled and GPS TEC is 5.5 TECU (percentage error ¼ 30.3%) and 5.0 TECU (percentage error ¼ 30.4%) for the Southern and Northern Hemisphere midlatitudes respectively compared to 7.5 TECU (percentage error ¼ 22.0%) in low latitudes.

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Section: Discussionsupporting

confidence: 91%

Section: Methodsmentioning

confidence: 99%

Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks

et al. 2020

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“…The higher value of RMSE (and slightly lower correlation coefficient) obtained for the region implies that it is more difficult to model the region than the other two regions that are in the low latitudes and midlatitudes. The equatorial ionosphere is known to be more difficult to model owing to the large TEC gradients and peculiar phenomena like the equatorial anomaly (Hunt et al, ; Okoh, Onwuneme, et al, ; Uwamahoro et al, ). This is also evident from the detailed error analysis presented in Figure a.…”

Section: Resultsmentioning

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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“…Similarly, Uwamahoro et al. (2019) noted as the IRI model shows improvements for the estimation of storm time TEC variation in moving from the low to the mid latitude regions in different geomagnetic storms (such as 7 October 2012 to 14 October 2012 and 22 June 2015 to 29 June 2015). An investigation focusing on the storm time response of the TEC variation in the low and mid latitude regions of Africa during the storm of 7 November 2004 to 12 November 2004 by Habarulema et al.…”

Section: Resultsmentioning

confidence: 85%

“…In the same way, Uwamahoro et al. (2019) noted that the performance of different empirical models (such as the International Reference Ionospheric model, IRI) shows improvements for the estimation of storm time TEC variation in moving from the low to the mid latitude regions. The investigation of Habarulema et al.…”

Section: Introductionmentioning

confidence: 90%

The Geomagnetic Storm Time Response of the Mid Latitude Ionosphere During Solar Cycle 24

Tariku

2021

Radio Science

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This paper mainly assesses the geomagnetic storm time response of the Africa, America, Asia and Europe mid latitude ionosphere in relation to the variability of the total electron content during solar cycle 24. This is carried out by considering the vertical total electron content (VTEC) data derived from the Global Positioning System (GPS), the recent International Reference Ionosphere (IRI 2016) and IRI‐Extended to the Plasmasphere (IRI‐Plas 2017) models in different geomagnetic storm days during solar cycle 24 (1 June 2013, 17 March 2015, and 28 May 2017). The effects of the 1 June 2013 and 17 March 2015 (2015 St. Patrick's Day) geomagnetic storms which resulted in intensification and fluctuation of the GPS VTEC variations were significantly enhanced in the European sector. On the other hand, the effect of the storm that occurred on 28 May 2017 was mostly pronounced in the American and Asian sectors. Both models are not able to adequately respond to the effects resulting from the geomagnetic storm. The VTEC calculated by the IRI models also generally decrease on most of the hours when the storm on option is applied during the main and recovery phases. Nevertheless, at the Asian station, the IRI‐Plas 2017 VTEC values tend to increase on most of the hours during the 2015 St. Patrick's Day storm. Moreover, as a result of enhancement of the VTEC, the satellite signal propagation during the positive geomagnetic storms is largely affected by the VTEC fluctuation.

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Highlights about the performances of storm-time TEC modelling techniques for low/equatorial and mid-latitude locations

Cited by 10 publications

References 75 publications

Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks

Storm‐Time Modeling of the African Regional Ionospheric Total Electron Content Using Artificial Neural Networks

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

The Geomagnetic Storm Time Response of the Mid Latitude Ionosphere During Solar Cycle 24

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