scholarly journals Relation between Solar Wind Parameter and Geomagnetic Storm Condition during Cycle-23

2014 ◽  
Vol 05 (13) ◽  
pp. 1602-1608 ◽  
Author(s):  
Balveer S. Rathore ◽  
Dinesh C. Gupta ◽  
K. K. Parashar
Keyword(s):  
Solar Wind ◽  
Geomagnetic Storm ◽  
Solar Wind Parameter ◽  
Storm Condition

scholarly journals Calibration of QM-MOURA three-axis magnetometer and gradiometer

2015 ◽  
Vol 4 (1) ◽  
pp. 1-18 ◽  
Author(s):  
M. Díaz-Michelena ◽  
R. Sanz ◽  
M. F. Cerdán ◽  
A. B. Fernández
Keyword(s):  
Magnetic Field ◽  
Solar Wind ◽  
Geomagnetic Storm ◽  
Remanent Magnetization ◽  
Local Magnetic Field ◽  
Daily Variations ◽  
Scientific Goal ◽  
Solar Events

Abstract. MOURA instrument is a three-axis magnetometer and gradiometer designed and developed for Mars MetNet Precursor mission. The initial scientific goal of the instrument is to measure the local magnetic field in the surroundings of the lander i.e. to characterize the magnetic environment generated by the remanent magnetization of the crust and the superimposed daily variations of the field produced either by the solar wind incidence or by the thermomagnetic variations. Therefore, the qualification model (QM) will be tested in representative scenarios like magnetic surveys on terrestrial analogues of Mars and monitoring solar events, with the aim to achieve some experience prior to the arrival to Mars. In this work, we present a practical first approach for calibration of the instrument in the laboratory; a finer correction after the comparison of MOURA data with those of a reference magnetometer located in San Pablo de los Montes (SPT) INTERMAGNET Observatory; and a comparative recording of a geomagnetic storm as a demonstration of the compliance of the instrument capabilities with the scientific objectives.

scholarly journals Total electron content responses to HILDCAAs and geomagnetic storms over South America

2017 ◽  
Vol 35 (6) ◽  
pp. 1309-1326 ◽  
Author(s):  
Patricia Mara de Siqueira Negreti ◽  
Eurico Rodrigues de Paula ◽  
Claudia Maria Nicoli Candido
Keyword(s):  
Solar Wind ◽  
South America ◽  
Total Electron Content ◽  
Geomagnetic Storm ◽  
Electric Fields ◽  
Geomagnetic Storms ◽  
Time Variability ◽  
Electron Content ◽  
Quiet Time ◽  
Total Electron

Abstract. Total electron content (TEC) is extensively used to monitor the ionospheric behavior under geomagnetically quiet and disturbed conditions. This subject is of greatest importance for space weather applications. Under disturbed conditions the two main sources of electric fields, which are responsible for changes in the plasma drifts and for current perturbations, are the short-lived prompt penetration electric fields (PPEFs) and the longer-lasting ionospheric disturbance dynamo (DD) electric fields. Both mechanisms modulate the TEC around the globe and the equatorial ionization anomaly (EIA) at low latitudes. In this work we computed vertical absolute TEC over the low latitude of South America. The analysis was performed considering HILDCAA (high-intensity, long-duration, continuous auroral electrojet (AE) activity) events and geomagnetic storms. The characteristics of storm-time TEC and HILDCAA-associated TEC will be presented and discussed. For both case studies presented in this work (March and August 2013) the HILDCAA event follows a geomagnetic storm, and then a global scenario of geomagnetic disturbances will be discussed. Solar wind parameters, geomagnetic indices, O ∕ N2 ratios retrieved by GUVI instrument onboard the TIMED satellite and TEC observations will be analyzed and discussed. Data from the RBMC/IBGE (Brazil) and IGS GNSS networks were used to calculate TEC over South America. We show that a HILDCAA event may generate larger TEC differences compared to the TEC observed during the main phase of the precedent geomagnetic storm; thus, a HILDCAA event may be more effective for ionospheric response in comparison to moderate geomagnetic storms, considering the seasonal conditions. During the August HILDCAA event, TEC enhancements from  ∼  25 to 80 % (compared to quiet time) were observed. These enhancements are much higher than the quiet-time variability observed in the ionosphere. We show that ionosphere is quite sensitive to solar wind forcing and considering the events studied here, this was the most important source of ionospheric responses. Furthermore, the most important source of TEC changes were the long-lasting PPEFs observed on August 2013, during the HILDCAA event. The importance of this study relies on the peculiarity of the region analyzed characterized by high declination angle and ionospheric gradients which are responsible for creating a complex response during disturbed periods.

scholarly journals Suzaku Observation of Strong Solar-Wind Charge-Exchange Emission from the Terrestrial Exosphere during a Geomagnetic Storm

2013 ◽  
Vol 65 (3) ◽  
pp. 63 ◽  
Author(s):  
Kumi Ishikawa ◽  
Yuichiro Ezoe ◽  
Yoshizumi Miyoshi ◽  
Naoki Terada ◽  
Kazuhisa Mitsuda ◽  
...  
Keyword(s):  
Solar Wind ◽  
Geomagnetic Storm ◽  
Charge Exchange ◽  
Solar Wind Charge Exchange

Neural network applications in geomagnetic storm prognosis based on the pre-storm occurrence of magnetic islands in the solar wind

2020 ◽  
Author(s):  
Mikhail Fridman
Keyword(s):  
Neural Network ◽  
Solar Wind ◽  
Particle Acceleration ◽  
Geomagnetic Storm ◽  
High Speed ◽  
Geomagnetic Storms ◽  
Pattern Search ◽  
Small Scale ◽  
Magnetic Islands ◽  
Solar Wind Density

<p>So far, the problem of a short-term forecast of geomagnetic storms can be considered as solved. Meanwhile, mid-term prognoses of geomagnetic storms with an advance time from 3 hours to 3 days are still unsuccessful (see  https://www.swpc.noaa.gov/sites/default/files/images/u30/Max%20Kp%20and%20GPRA.pdf).</p><p> This fact suggests a necessity of looking for specific processes in the solar wind preceding geomagnetic storms. Knowing that magnetic cavities filled with magnetic islands and current sheets are formed in front of high-speed streams of any type (Khabarova et al., 2015, 2016, 2018; Adhikari et al., 2019), we have performed an analysis of the corresponding ULF variations in the solar wind density observed at the Earth's orbit from hours to days before the arrival of a geoeffective stream or flow. The fact of the occurrence of ULF-precursors of geomagnetic storms was noticed a long time ago (Khabarova 2007; Khabarova & Yermolaev, 2007) and related prognostic methods were recently developed (Kogai et al. 2019), while the problem of automatization of the prognosis remained unsolved.</p><p> A new geomagnetic storm forecast method, which employs a Recurrent Neural Network (RNN) for an automatic pattern search, is proposed. An ability of self-teaching and extracting deeply hidden non-linear patterns is the main advantage of Deep Neural Networks (DNNs) with multiple layers over traditional Machine Learning methods. We show a success of the RNN method, using either the unprocessed solar wind density data or Wavelet analysis coefficients as the input parameter for a DNN to perform an automatic mid-term prognosis of geomagnetic storms.  </p><p>Adhikari, L., et al. 2019, The Role of Magnetic Reconnection–associated Processes in Local Particle Acceleration in the Solar Wind, ApJ, 873, 1, 72, https://doi.org/10.3847/1538-4357/ab05c6<br>Kogai T.G. et al., Pre-storm ULF variations in the solar wind density and interplanetary magnetic field as key parameters to build a mid-term prognosis of geomagnetic storms. “GRINGAUZ 100: PLASMA IN THE SOLAR SYSTEM”, IKI RAS, Moscow, June 13–15, 2018, 140-143, ISBN 978-5-00015-043-6. https://www.researchgate.net/publication/327781146_Pre-storm_ULF_variations_in_the_solar_wind_density_and_interplanetary_magnetic_field_as_key_parameters_to_build_a_mid-term_prognosis_of_geomagnetic_storms<br> Khabarova O. V., et al. 2018,  Re-acceleration of energetic particles in large-scale heliospheric magnetic cavities, Proceedings of the IAU, 76-82, https://doi.org/10.1017/S1743921318000285 <br>Khabarova O.V., et al. Small-scale magnetic islands in the solar wind and their role in particle acceleration. II. Particle energization inside magnetically confined cavities. 2016, ApJ, 827, 122, http://iopscience.iop.org/article/10.3847/0004-637X/827/2/122<br>Khabarova O., et al. Small-scale magnetic islands in the solar wind and their role in particle acceleration. 1. Dynamics of magnetic islands near the heliospheric current sheet. 2015, ApJ, 808, 181, https://doi.org/10.1088/0004-637X/808/2/181</p><p>Khabarova O.V., Current Problems of Magnetic Storm Prediction and Possible Ways of Their Solving. Sun&Geosphere,  http://sg.shao.az/v2n1/SG_v2_No1_2007-pp-33-38.pdf , 2(1), 33-38, 2007</p><p>Khabarova O.V. & Yu.I.Yermolaev, Solar wind parameters' behavior before and after magnetic storms, JASTP, 70, 2-4, 2008, 384-390, http://dx.doi.org/10.1016/j.jastp.2007.08.024</p>

scholarly journals Magnetospheric chaos and dynamical complexity response during storm time disturbance

2020 ◽  
Author(s):  
Irewola Aaron Oludehinwa ◽  
Olasunkanmi Isaac Olusola ◽  
Olawale Segun Bolaji ◽  
Olumide Olayinka Odeyemi ◽  
Abdullahi Ndzi Njah
Keyword(s):  
Time Series ◽  
Solar Wind ◽  
Geomagnetic Storm ◽  
Time Series Data ◽  
Geomagnetic Storms ◽  
Approximate Entropy ◽  
Series Data ◽  
Dynamical Complexity ◽  
Significant Difference ◽  
Dynamical Features

Abstract. In this study, we examine the magnetospheric chaos and dynamical complexity response in the disturbance storm time (Dst) and solar wind electric field (VBs) during different categories of geomagnetic storm (minor, moderate and major geomagnetic storm). The time series data of the Dst and VBs are analyzed for the period of nine years using nonlinear dynamics tools (Maximal Lyapunov Exponent, MLE, Approximate Entropy, ApEn and Delay Vector Variance, DVV). We found a significant trend between each nonlinear parameter and the categories of geomagnetic storm. The MLE and ApEn values of the Dst indicate that chaotic and dynamical complexity response are high during minor geomagnetic storms, reduce at moderate geomagnetic storms and declined further during major geomagnetic storms. However, the MLE and ApEn values obtained in VBs indicate that chaotic and dynamical complexity response are high with no significant difference between the periods that are associate with minor, moderate and major geomagnetic storms. The test for nonlinearity in the Dst time series during major geomagnetic storm reveals the strongest nonlinearity features. Based on these findings, the dynamical features obtained in the VBs as input and Dst as output of the magnetospheric system suggest that the magnetospheric dynamics is nonlinear and the solar wind dynamics is consistently stochastic in nature.

scholarly journals ESTIMASI BADAI GEOMAGNET BERDASARKAN PERILAKU PARAMETER ANGIN SURYA DAN MEDAN MAGNET ANTARPLANET SEBELUM BADAI GEOMAGNET (THE ESTIMATION OF GEOMAGNETIC STORM BASED ON SOLAR WIND PARAMETERS AND INTERPLANETARY MAGNETIC FIELD BEHAVIOR BEFORE GEOMAGNETIC STOR

2017 ◽  
Vol 14 (2) ◽  
pp. 17
Author(s):  
Anwar Santoso ◽  
Mamat Rahimat ◽  
Rasdewita Kesumaningrum ◽  
Siska Filawati
Keyword(s):  
Magnetic Field ◽  
Solar Wind ◽  
Geomagnetic Storm ◽  
Space Weather ◽  
Geomagnetic Storms ◽  
Storm Events ◽  
Science Center ◽  
Solar Wind Parameters ◽  
The Impact ◽  
The Imf

Space weather research is the principal activity at the Space Science Center, Lapan to learn characteristics and generator source of the space weather so that can mitigate its the impact on the Earth's environment as mandated in Law No. 21 Year 2013. One of them is the phenomenon of geomagnetic storms. Geomagnetic storms caused by the entry of solar wind together with the IMF Bz that leads to the south. The behavior of the solar wind parameters together with the IMF Bz before geomagnetic storms can determine the formation of geomagnetic storms that caused it. In spite that, by the solar wind parameters and IMF Bz behavior before geomagnetic storm can be estimated its intensity through the equation Dst * = 1.599 * Ptotal - 34.48. The result of this equation is obtained that the Dst minimum deviation between the raw data and the output of this equation to the geomagnetic storm events on March 17, 2013 is about of -2.51 nT or 1.9% and on the geomagnetic storm events on February 19, 2014 is about of 2.77 nT or 2, 5%. Thus, the equation Dst * = 1.599 * Ptotal - 34.48 is very good for the estimation of geomagnetic storms.

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