Variation on Solar Wind Parameters and Total Electron Content Over Middle‐ to Low‐Latitude Regions During Intense Geomagnetic Storms

Mishra, Roshan Kumar; Adhikari, Binod; Chapagain, Narayan P.; Baral, Rabin; Das, Priyanka Kumari; Klausner, Virginia; Sharma, Manisha

doi:10.1029/2020rs007129

Cited by 21 publications

(5 citation statements)

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“…The authors also reported a hemispherically asymmetric negative ionospheric storm effect which they explained in terms of the thermospheric wind patterns in the two hemispheres. Recently, Mishra et al (2020) studied vertical total electron content (vTEC) using GPS-derived TEC data in mid to low latitude regions over Indian, Australian, Brazilian, and South African sectors during three different geomagnetic storms including the 2015 St. Patrick's Day storm. They reported GMS-induced TEC perturbations in sectors they studied with higher variations in Brazil and India sectors than South African and Australian regions.…”

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confidence: 99%

Spatio‐Temporal Evolution of Global Ionospheric Storm Drivers and Hemispherical Asymmetry During 17–18 March 2015 Geomagnetic Storm

Terefe

Nigussie

2021

JGR Space Physics

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Excess energy and momentum deposition into the Earth's magnetosphere owing to the invasion of solar wind outbursts arising from active regions of the Sun causes major disturbances in the geomagnetic field referred to as geomagnetic storms (GMSs) (D'ujanga et al., 2013). The main drivers of GMSs are solar flares, coronal mass ejections (CMEs), and fast wind streams from coronal holes or their interplanetary remnants known as the interplanetary coronal mass ejections (Rawat et al., 2010;Richardson et al., 2006). The occurrence of GMSs is marked with a larger southward pointing interplanetary magnetic field (IMF), B z < −10 nT, that sustain for a period of time approximately longer than 3 h (Gonzalez & Tsurutani, 1987;Gonzalez et al., 1994). Southward B z is quite essential as it facilitates the transfer of energy, mass, and momentum from the solar wind into the Earth's magnetosphere through the magnetic reconnection with the northward directed geomagnetic field around Earth's magnetopause (Dungey, 1961).The state of the ionosphere at times of GMSs, commonly named ionospheric storms, describes variations of ionospheric parameters such as electron density, total electron content (TEC), or critical frequency (f o F 2 ) in response to geomagnetic storms. Based on the storm-induced alterations of the ionospheric parameters the ionospheric storms can be categorized as positive/negative corresponding to an increase/decrease in ionospheric parameters (

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confidence: 99%

Spatio‐Temporal Evolution of Global Ionospheric Storm Drivers and Hemispherical Asymmetry During 17–18 March 2015 Geomagnetic Storm

Terefe

Nigussie

2021

JGR Space Physics

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“…Several previous studies have shown that positive ionospheric disturbances in the low and mid‐latitudes are the result of PPEF and equatorward disturbance with the main factor of enhanced thermospheric compositions of

\mathrm{O}/{\mathrm{N}}_{2}

ratio (de Jesus et al., 2016; Lissa et al., 2020). However, it should be noted that generally, the ionospheric storm effects result from several disturbance mechanisms depending on the storm phase and the latitudinal position (Balan et al., 2009; Maruyama, 2004; Mishra et al., 2020). The ionospheric storm effects can also be observed in other ionospheric phenomena for example, equatorial plasma bubbles (Tulasi Ram et al., 2015) and low altitude ionospheric storm effects (Lastovicka, 1996), traveling ionospheric disturbances and traveling atmospheric disturbances (TAD) (Habarulema et al., 2015; Scherliess & Fejer, 1997).…”

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confidence: 99%

The Ionospheric Dynamics of the African Sector Responding to a Severe Geomagnetic Storm; the Storm of 3–5 November 2021

Rukundo

2023

Space Weather

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A geomagnetic storm is a disturbance on the Earth's magnetic field resulting from enhanced energy transfer from interplanetary space to the Earth's environment through the magnetosphere. The cause of geomagnetic storms is variation in the solar wind parameters, that is enhancement in solar wind speed and solar wind particles that increase solar wind pressure, and at least >3 hr sustained southward interplanetary magnetic field (IMF) on the day side magnetosphere (Gonzalez et al., 1994;Kumar & Kumar, 2019). The major solar driver of this disturbance is the coronal mass ejections (CMEs) embedded with large plasma emissions from the sun, taking several hours or days to reach the Earth's atmosphere. It should be noted that other sun's emissions like high solar wind speed from coronal holes, and corotating interaction regions also contribute to geomagnetic storms (Richardson, 2018;Richardson et al., 2006). The geomagnetic storm effects induce ionospheric disturbances characterized

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“…e TEC values, which change over time and ought to be evaluated along with their location in space, are the principal factors for solar activity and ionosphere-magnetosphere-Sun interaction [3,[19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36]. is essay predicts the TEC values through an artificial neural network model (ANNm) [7,8,[37][38][39][40][41][42][43][44][45] over the superstorms of November 20, 2003 (Dst � -422 nT) and November 08, 2004 (Dst � -374 nT).…”

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confidence: 99%

Discussing Total Electron Content over the Solar Wind Parameters

Eroğlu

2022

Mathematical Problems in Engineering

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Modeling and forecasting of Total Electron Content (TEC) values by an Artificial Neural Network model (ANNm) have high agreement on November 2003, 2004 superstorms. The work discusses Solar Wind Parameters (SWp) from OMNI (Operating Missions as a Node on the Internet) and TEC (TECU) data (International Reference Ionosphere) IRI-2012, IRI-2016 on November 20, 2003 (Dst = –422 nT) and on November 08, 2004 (Dst = –374 nT) Geomagnetic Storms (GSs). The paper commences with a 120-hour GS exhibition of SWp and proceeds with the correlation data of the variables, their hierarchical tracks, and inner dispersions. The ANNm with SWp as the input and TEC data as the output are introduced. The performance of the ANNm for 2003 and 2004 superstorms is adequate. The Correlation Coefficient (R) and Root Mean Square Error (RMSE) of the ANNm are 97.5%, 1.17 TECU (IRI-2012), and 97.9%, 1.09 TECU (IRI-2016) for the 2003 GS and 97.0%, 0.89 TECU (IRI-2012), and 98.0%, 1.61 TECU (IRI-2016) for 2004 GS. Parameters effect of the R constant of TEC data points out to the dynamic pressure (nPa), the magnetic field Bz component (nT), the flow speed (km/s), and the proton density (1/cm3). Besides, the absolute total error and the variance of the predicted TEC data for November 2003 and November 2004 GSs are 0.06 (0.30%) with 0.013 variance (IRI-2012), 0.09 (0.49%) with 0.016 variance (IRI-2016) for 2003 storm and 0.13 (0.73%) with 0.033 variance (IRI-2012), and 0.11 (1.06%) with 0.035 variance (IRI-2016) for 2004. It means that the paper models TEC data with considerable consistency over the SWp.

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Variation on Solar Wind Parameters and Total Electron Content Over Middle‐ to Low‐Latitude Regions During Intense Geomagnetic Storms

Cited by 21 publications

References 82 publications

Spatio‐Temporal Evolution of Global Ionospheric Storm Drivers and Hemispherical Asymmetry During 17–18 March 2015 Geomagnetic Storm

Spatio‐Temporal Evolution of Global Ionospheric Storm Drivers and Hemispherical Asymmetry During 17–18 March 2015 Geomagnetic Storm

The Ionospheric Dynamics of the African Sector Responding to a Severe Geomagnetic Storm; the Storm of 3–5 November 2021

Discussing Total Electron Content over the Solar Wind Parameters

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