Mathematical analysis of the 09 March 2012 intense storm

Köklü, Kevser

doi:10.1016/j.asr.2020.04.053

Cited by 16 publications

(7 citation statements)

References 30 publications

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“…If these variables are omitted from the ap forecasting, the model correlation constant diminishes by 8.91% and 8.40%, respectively. Physically, the magnetic eld polarizations indicate parallel effects with the dynamic pressure P (nPa), the ow speed v (km/s), and the proton density N (1/cm 3 ) while the ap index nonlinearly responses to the instabilities (Altadill et al 2001;Eroglu, 2018;2019;2021;Inyurt, 2020;Koklu, 2020). The noticeable relation between B z (nT) magnetic eld, the dynamic pressure P (nPa), the ow speed v (km/s), and proton density N (1/cm 3 ) and the ap (nT) index may be perceived in Table 2a.…”

Section: Modelingmentioning

confidence: 99%

“…Lastly, the geomagnetic storm nishes with the recovery phase when the uctuating in the magnetic eld ends and the Dst index indicates to the initial values. In moderate storms, following the B z parameter of the Dst index with a delay (Burton, 1975) of 5-6 hours is the response of the ring current to the SW. To better understand a geomagnetic storm (Mayaud 1980;2011;Eroglu, 2018;Eroglu, 2019;Inyurt and Sekertekin, 2019;Inyurt, 2020;Koklu, 2020;Eroglu, 2020), the author considers to models between the SW parameters and the ZG indices. This paper utilizes hourly versions of the SW parameters and the ZG indices.…”

Section: Introductionmentioning

confidence: 99%

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Analysis of the first four moderate geomagnetic storms of the year 2015

Eroğlu

2021

Arab J Geosci

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This essay investigates of rst four moderate geomagnetic activities (04 January storm, 07 January storm, 17 February storm, and 24 February storm) of 2015 in the 24 th solar cycle. It tries to understand these storms with the aid of the zonal geomagnetic indices. It predicts the zonal geomagnetic indices (Dst, ap, AE) of the storms by an arti cial neural network model. The phenomena that occurred in January and February are discussed on the solar wind parameters (B z , E, P, N, v, T) and the zonal geomagnetic indices obtained from NASA. In the study, after glancing at the 2015-year general appearance, binary correlations of the variables are indicated by the covariance matrix, and the hierarchical cluster of the variables are presented by the dendrogram.The arti cial neural network model is governed by the physical principles in the paper. The model uses the solar wind parameters as inputs and the zonal geomagnetic indices as outputs. The causality principle forms the models by cause-effect association. Back propagation algorithm is speci ed as Levenberg-Marquardt (trainlm) and 35 neural numbers are utilized in the arti cial neural network. The neural network model predicts the Dst, ap, and AE indices of January and February geomagnetic storms with an accuracy that deserves discussion. Estimating the geomagnetic activities may support interplanetary works. value of 12.36 nPa and the proton density N hits its maximum value of 29.4 1/cm 3 . After two hours, on 07 January at 9:00 UT the B z magnetic eld decreases to its minimum value (-17.04 nT), the electric eld E ranges to its highest value of 8.16 mV/m, the AE index shows its maximum value of 1327 nT, and the ap ZG index hits its maximum value of 94 nT.

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Section: Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analysis of the first four moderate geomagnetic storms of the year 2015

Eroğlu

2021

Arab J Geosci

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“…e predicted TEC values are criss cross checked with the values attained from the IRI-2012 and 2016 [31,[46][47][48] model. e IRI model, which has been incessantly improved after its first version was produced in 1978, was created by the collaboration of the International Union of Radio Science (URSI) and the Committee on Space Research (COSPAR).…”

Section: Introductionmentioning

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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“…In order to discuss the dynamic structure of the storm, it is necessary to establish models by looking at the relationships between the variables and to keep the consistency of these correlations. e mathematical models can guide researchers about data and their relationships whichever in the science area [15,20]. In addition, they may yield clues about the behavior of the variables under different terms [15,16,18,26,38,39].…”

Section: Introductionmentioning

confidence: 99%

Mathematical Analysis of the 08 May 2014 Weak Storm

Köklü

2021

Mathematical Problems in Engineering

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Since the time scale of weak storms is about half the time scale of intense storms, it is troublesome and important to examine the solar wind parameters/interplanetary magnetic field (IMF) (E, v , P , T, N, and Bz) to evolve and affect to zonal geomagnetic indices (Kp, Dst, AE, and ap). In a severe storm, which usually has two main phases, solar parameters have enough time to react, but weak storms cannot find this time. They have to yield their reaction in a short time. One can find a weak storm in order to reveal and discuss the consistency of models that have proven themselves in severe and moderate storms in this study. I discuss weak storm (Dst = −46) on May 8, 2014, via solar wind parameters and zonal geomagnetic indices. The goal of the work is to realize the models applicable to the moderate and the strong storms for a weak storm. Hereby, all possible correlations between solar parameters and zonal indices are discussed in depth. I tried to obey the cause-effect relationship while creating mathematical models while not ignoring the physical principles. Therefore, the physical principles govern the study. The results are visualized with tables and graphs for the understanding of the dynamic structure of the storm.

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Mathematical analysis of the 09 March 2012 intense storm

Cited by 16 publications

References 30 publications

Analysis of the first four moderate geomagnetic storms of the year 2015

Analysis of the first four moderate geomagnetic storms of the year 2015

Discussing Total Electron Content over the Solar Wind Parameters

Mathematical Analysis of the 08 May 2014 Weak Storm

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