Low-latitude ionospheric response from GPS, IRI and TIE-GCM TEC to Solar Cycle 24

Rao, S. S.; Chakraborty, Monti; Kumar, Sanjay; Singh, Ashutosh K.

doi:10.1007/s10509-019-3701-2

Cited by 24 publications

(14 citation statements)

References 81 publications

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“…Since sufficient and regular observations of ionosonde are not available, the IRI model predictions are not very precise at low latitude and equatorial ionization anomaly region. In addition, IRI model does not include O/N2 ration, neutral winds, and equatorial electrojet as the input parameters (Rao et al, ). In the difference map, we can see that the difference between DeepIRI TEC map and IGS TEC map is close to 0 TECU around ionospheric peak regions.…”

Section: Resultsmentioning

confidence: 99%

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Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks

Moon

Park

2020

Space Weather

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In this study, we make a model, which is called DeepIRI, to generate improved International Reference Ionosphere (IRI) total electron content (TEC) maps by deep learning based on conditional Generative Adversarial Networks. For this we consider 48,901 pairs of IRI TEC maps and International Global Navigation Satellite Systems (GNSS) Service (IGS) TEC maps from 2001 to 2011 for training the model. We evaluate the model by comparing IGS TEC maps and DeepIRI TEC ones from 2013 to 2017. The DeepIRI TEC maps that our model generated are much more consistent with the corresponding IGS TEC maps than the IRI TEC ones. Especially, ionospheric peak structures are successfully generated in DeepIRI TEC maps while they are not in IRI-2016 ones. From the average differences between IRI and IGS TEC maps, our model greatly improved the IRI TEC at low-latitude region around the equatorial anomaly. These results show that our model can improve the global TEC prediction ability of the IRI-2016. Our study suggests a sufficient possibility to generate DeepIRI global TEC maps in near real time if IRI is generated in time. Our approach can be applied to make improved versions of empirical models if more realistic observations are available with a time delay. Plain Language SummaryThe International Reference Ionosphere (IRI) is one of the most widely used standard models of the ionosphere. The IRI total electron content (TEC) is very important to monitor ionospheric disturbances. This paper makes a model to improve IRI TEC maps by deep learning based on conditional Generative Adversarial Networks, which is a general purpose solution to resolve image-to-image translation problems. We consider many pairs of IRI TEC maps and IGS TEC maps for training the model. Our model greatly improves the global IRI TEC maps, which are very consistent with the observations.

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Section: Resultsmentioning

confidence: 99%

“…Rao et al (2019) compared the TEC from GPS and IRI-2016 during Solar Cycle 24. They found that the IRI model overestimates the noontime TEC values at low latitude.…”

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confidence: 99%

Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks

Moon

Park

2020

Space Weather

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“…The solar flux dependency of the winter anomaly in GPS TEC has been studied by Rao et al (2019b). The result showed that, when the level of solar flux in winter month is greater than the corresponding summer month, the winter anomaly is observed irrespective of whether the phases of solar cycle are is high or low.…”

Section: Seasonal Variation In Tecmentioning

confidence: 99%

“…Dashora and Suresh (2015) analysed the characteristics of low latitude TEC data of solar cycles 23 and 24 over Indian sector using global ionospheric data. A double hump structure in the solar flux and in TEC was identified at the low latitude station of Varanasi, India, in the ionospheric response using the GPS TEC, IRI (International Reference Ionosphere) and TIE-GCM (Thermosphere-Ionosphere-Electrodynamics General Circulation Model) TEC of solar cycle 24 by Rao et al (2019a). Parwani et al (2019) studied the latitudinal variation in ionospheric TEC in the northern hemispheric region and found that the diurnal TEC has a higher value in low latitudes than in mid and high latitudes and in the seasonal variation maximum in spring and autumn than in summer and winter.…”

Section: Introductionmentioning

confidence: 99%

Climatology of ionosphere over Nepal based on GPS total electron content data from 2008 to 2018

et al. 2021

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Abstract. In this study, we analyse the climatology of ionosphere over Nepal based on GPS-derived vertical total electron content (VTEC) observed from four stations as defined in Table 1: KKN4 (27.80∘ N, 85.27∘ E), GRHI (27.95∘ N, 82.49∘ E), JMSM (28.80∘ N, 83.74∘ E) and DLPA (28.98∘ N, 82.81∘ E) during the years 2008 to 2018. The study illustrates the diurnal, monthly, annual, seasonal and solar cycle variations in VTEC during all times of solar cycle 24. The results clearly reveal the presence of equinoctial asymmetry in TEC, which is more pronounced in maximum phases of solar cycle in the year 2014 at KKN4 station, followed by descending, ascending and minimum phases. Diurnal variations in VTEC showed the short-lived day minimum which occurs between 05:00 to 06:00 LT (local time) at all the stations considered, with diurnal peaks between 12:00 and 15:00 LT. The maximum value of TEC is observed more often during the spring equinox than the autumn equinox, with a few asymmetries. Seasonal variation in TEC is observed to be a manifestation of variations in solar flux, particularly regarding the level of solar flux in consecutive solstices.

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“…Ionospheric system undergoes significant modifications during a solar cycle (J. Rao et al., 2019; S. S. Rao et al., 2019). Based on the optical dayglow OI emissions, which signifies the upper atmospheric variability and level of ionization, Laskar et al.…”

Section: Introductionmentioning

confidence: 99%

Ionospheric Response to Sudden Stratospheric Warming Events Across Longitudes During Solar Cycle 24

2021

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The ionized layer of the terrestrial atmosphere from ∼90 to 600 km above sea level called ionosphere affects radio propagation due to the presence of free electrons and ions (Hargreaves, 1992). With our ever-growing technological dependence on satellites for many applications related to navigation and communication, forecasting and nowcasting of ionosphere is becoming crucial for precise positioning and navigation. However, due to unsteady primary source of its ionization, which is Sun, ionosphere is variable and exhibits normal hour-to-hour, day-to-day, seasonal, 11-year, and transient variations. Apart from the well-established solar and geomagnetic influences under space weather, it has been more or less established during the last two decades that meteorological processes also perturb the ionosphere significantly, accounting for ∼20% of daytime ionospheric variations on average (Forbes et al., 2000). This percentage share becomes more

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Low-latitude ionospheric response from GPS, IRI and TIE-GCM TEC to Solar Cycle 24

Cited by 24 publications

References 81 publications

Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks

Improvement of IRI Global TEC Maps by Deep Learning Based on Conditional Generative Adversarial Networks

Climatology of ionosphere over Nepal based on GPS total electron content data from 2008 to 2018

Ionospheric Response to Sudden Stratospheric Warming Events Across Longitudes During Solar Cycle 24

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