An Empirical Model for the Ionospheric Global Electron Content Storm-Time Response

Li, Shuhui; Galas, R.; Ewert, Dietrich; Peng, Junhuan

doi:10.1515/acgeo-2015-0067

Cited by 15 publications

(9 citation statements)

References 29 publications

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“…Recently, EOF analysis was used for the first time to model TEC during geomagnetic storms, and it was found that modeling results agree with observed TEC quite well for storms with nonsignificant ionospheric response while positive and negative storm responses were not sufficiently captured (Uwamahoro & Habarulema, 2015). Multiregression analysis-based methods have been developed and applied to ionospheric modeling during geomagnetically quiet and disturbed conditions, and results agree with observations quite well (Feng et al, 2016;Hajra et al, 2016;Kutiev & Muhtarov, 2001, 2003Li et al, 2016;Mukhtarov et al, 2013). However, the failure to accurately capture positive and negative ionospheric responses was reported for some geomagnetic storms considered for validation (e.g., Mukhtarov et al, 2013).…”

Section: Introductionmentioning

confidence: 74%

“…Toward a better understanding of TEC response during geomagnetic storms, TEC modeling during storm conditions is of utmost importance for ionospheric studies and satellite applications. Different TEC models have long been developed and applied for ionospheric studies during both geomagnetically quiet and disturbed conditions (e.g., Chen et al, 2015;Dabbakuti & Ratnam, 2016Ercha et al, 2012Ercha et al, , 2015Feng et al, 2016;Habarulema et al, 2007Habarulema et al, , 2010Habarulema et al, , 2011Hajra et al, 2016;Kakinami et al, 2009;Li et al, 2016;Mao et al, 2005Mao et al, , 2008Mukhtarov et al, 2013;Uwamahoro & Habarulema, 2015;Watthanasangmechai et al, 2012). Findings about the performance of climatological models such as the International Reference Ionosphere (IRI) have shown that, on average, TEC is predicted quite well during geomagnetically quiet conditions although for some cases, IRI model underestimates or overestimates TEC magnitude (e.g., Adewale et al, 2011;Akala et al, 2013;Kenpankho et al, 2011;Rathore et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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Storm Time Total Electron Content Modeling Over African Low‐Latitude and Midlatitude Regions

2018

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This paper presents storm time total electron content (TEC) modeling results based on artificial neural networks, for both low‐latitude and midlatitude African regions. The developed storm time TEC models were based on Global Positioning System (GPS) observations from GPS receiver stations selected in low latitude, Northern and Southern hemisphere midlatitude regions of the African sector. GPS data selection was based on a storm criterion of Dst≤ −50 nT and storm data sets used to develop the models were within the periods 2001–2015, 2000–2015, and 1998–2015, for African low latitude, Northern and Southern Hemisphere midlatitude regions, respectively. For the first time in storm time TEC modeling, the meridional wind velocity was introduced as an additional input to the well‐known TEC modeling inputs (diurnal variation, seasonal variation, solar activity, and geomagnetic activity representations) to take into account the effect of neutral winds in moving ionization within the ionosphere along the magnetic field lines. Results showed that the use of meridional wind as an additional input leads to overall percentage improvements of about 5%, 10%, and 5% for the low‐latitude, Northern and Southern Hemisphere midlatitude regions, respectively. High‐latitude storm‐induced winds and the interhemispheric blows of the meridional winds from summer to winter hemisphere may be associated with these improvements.

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Section: Introductionmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Storm Time Total Electron Content Modeling Over African Low‐Latitude and Midlatitude Regions

2018

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“…We analyzed the periods when IMF Bz component had the values less than -8 nTl. Taking into account the possible duration of ionospheric disturbances [19], ionospheric data were analyzed in a 3-day time window, each beginning from 00:00 UT. Thus, for the ionospheric data we processed the periods including the date of the event in IMF and the two following days.…”

Section: Results Of Methods Applicationmentioning

confidence: 99%

Analysis of cosmic ray dynamics and ionospheric parameters during increased solar activity and magnetic storms

2019

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Based on the ground station data, the paper investigates the dynamics of space rays and ionospheric parameters during strong and moderate magnetic storms 2018. The research was carried out applying new methods for modeling and data analysis suggested by the authors. In the course of investigation, anomalous changes in ionospheric parameters preceding and accompanying magnetic storms were detected. When they were compared with the data of cosmic rays and interplanetary magnetic field, correlation was observed. It confirms the solar nature of the detected ionospheric effects. Application of regression methods confirmed the assumption on statistically significant effect of interplanetary magnetic field parameters on ionospheric parameter dynamics during increased solar activity. The results are of interest in the tasks of space weather forecast.

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“…(2015) derive a statistical model for the lower band chorus distribution; S. Li et al. (2016) estimate the ionospheric global electron content storm‐time response; Boardsen et al. (2000) derive an empirical model of the high‐latitude magnetopause; and, Zhao et al.…”

Section: Introductionmentioning

confidence: 99%

Multi‐Hour‐Ahead Dst Index Prediction Using Multi‐Fidelity Boosted Neural Networks

Camporeale

Swiger

2023

Space Weather

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The Disturbance storm time (Dst) is a geomagnetic index related to the perturbation of the geomagnetic field at low latitudes (Burton et al., 1975;Rostoker, 1972). Currently, Dst is defined by using geomagnetic field measurements from four equatorial ground magnetometers: Hermanus, Honolulu, San Juan and Kakioka (Sugiura & Kamei, 1991). Dst has been widely used for monitoring geomagnetic storms, which pose one of the most severe space weather risks to our space-borne and ground-based electronic instruments, such as global navigation satellite systems and radio transmission systems (Z. Li et al., 2021;Wan et al., 2021).A short-term prediction of Dst is produced operationally at the NOAA Space Weather Prediction Center by means of a physics-based model (the Space Weather Modeling Framework developed at the University of Michigan, (Tóth et al., 2005)). A longer lead-time operational Dst forecast is provided by Space Environment Technologies, using the Anemomilos model (Tobiska et al., 2013). Dst is also used as an essential input for forecasting thermosphere mass density and ionospheric parameters, and to parameterize several empirical models. The following is a non-exhaustive list of models that use Dst as one of their inputs: O'Brien and Moldwin ( 2003) empirically estimate the location of the plasmapause; Agapitov et al. (2015) derive a statistical model for the lower band chorus distribution; S. Li et al. (2016) estimate the ionospheric global electron content storm-time response; Boardsen et al. (2000) derive an empirical model of the high-latitude magnetopause; and, Zhao et al. (2018) derive a model of radiation belt electron pitch-angle distributions.A large amount of literature has been devoted to Dst prediction, notably using empirical and machine learning techniques (Camporeale, 2019). Lundstedt et al. (2002) first attempted to implement a multi-layer perception neural network to forecast Dst 1 hour ahead using interplanetary magnetic field (IMF) data. Several researchers presented models to extend the Dst forecast up to 6 hr in advance (Bala & Reiff, 2012;Lazzús et al., 2017;Saiz et al., 2008). A Gaussian Process model was introduced by Chandorkar et al. (2017) and combined with a long short-term memory (LSTM) architecture in Gruet et al. (2018) to provide probabilistic predictions up to 6 hours in advance. An ensemble learning algorithm was applied by Xu et al. (2020) for the same purpose. Laperre et al. (2020) evaluated the performance of a LSTM model based on a Dynamical Time Warping metric as the cost function.

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An Empirical Model for the Ionospheric Global Electron Content Storm-Time Response

Cited by 15 publications

References 29 publications

Storm Time Total Electron Content Modeling Over African Low‐Latitude and Midlatitude Regions

Storm Time Total Electron Content Modeling Over African Low‐Latitude and Midlatitude Regions

Analysis of cosmic ray dynamics and ionospheric parameters during increased solar activity and magnetic storms

Multi‐Hour‐Ahead Dst Index Prediction Using Multi‐Fidelity Boosted Neural Networks

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