The intensity of the main phase of geomagnetic storms in relation to the parameters of the solar wind

1976 ◽  
Vol 20 (4) ◽  
pp. 408-410 ◽  
Author(s):  
Petronela Ochabová ◽  
J. Halenka
Keyword(s):  
Solar Wind ◽  
Geomagnetic Storms ◽  
Main Phase

Energetics of Magnetosphere-Ionosphere system during Main Phase of Intense Geomagnetic Storms over three Solar Cycles

2018 ◽  
Vol 13 (S340) ◽  
pp. 69-70
Author(s):  
K. J. Suji ◽  
P. R. Prince
Keyword(s):  
Solar Wind ◽  
Kinetic Energy ◽  
Wind Energy ◽  
Joule Heating ◽  
Geomagnetic Storms ◽  
Main Phase ◽  
Relative Importance ◽  
Solar Cycles ◽  
Dynamo Action ◽  
Solar Wind Energy

AbstractSolar wind kinetic energy gets transferred into the Earth’s magnetosphere as a result of dynamo action between magnetosphere and solar wind. Energy is then dissipated among various dissipation channels in the MI system. In the present study, energetics of 59 intense geomagnetic storms are analyzed for the period between 1986 and 2015, which covers the three consecutive solar cycles SC 22, 23 and 24. The average solar wind energy impinging the MI system is estimated using Epsilon parameter, the coupling function. Moreover, the relative importance of different energy sinks in the MI system are quantified and is found that more than 60% of solar wind energy is dissipated in the form of ionospheric Joule heating.

scholarly journals Supersubstorms during strong magnetic storm on 7 September 2017

2019 ◽  
Vol 127 ◽  
pp. 01010
Author(s):  
Irina Despirak ◽  
Natalia Kleimenova ◽  
Liudmila Gromova ◽  
Sergey Gromov ◽  
Liudmila Malysheva
Keyword(s):  
Solar Wind ◽  
Magnetic Storm ◽  
Geomagnetic Storms ◽  
Magnetic Cloud ◽  
Global Scale ◽  
Main Phase ◽  
Front Edge ◽  
Strong Magnetic Storm ◽  
Night Side ◽  
Substorm Activity

We analyzed the appearance of two supersubstorms observed during storm on September 07, 2017. Supersubstorms (SSS) are called substorms with SML index < - 2500 nT. The storm on September 07, 2017 is famous event which was studied already in many papers. There were two several geomagnetic storms on 7 and 8 September 2017, which associated with two consecutive solar wind structures: SHEATH with EJECTA and SHEATH with magnetic cloud (MC). Because the first SHEATH have a positive IMF Bz on their front edge the substorm activity absent in this time. The main phase of the first magnetic storm began with arriving the second SHEATH with the strong negative IMF Bz. During this period the first night-side supersubstorm (up to ~ 3500 nT) developed. The second magnetic storm was caused by MC with the negative IMF Bz and the severe night-side supersubstorm (up to ~ 3500 nT) were registered in this time. Thus, during the 7-8 September 2017 storms, two supersubstorms were generated, these supersubstorms caused by the SHEATH and MC impact have demonstrated the global scale distribution.

scholarly journals Influence of Solar Wind Parameters on Equatorial Magnetic Observatories during Intense Geomagnetic Storms of the Year 2015

2020 ◽  
Vol 12 (3) ◽  
pp. 233-250
Author(s):  
S. Joshi ◽  
K. M. Rao
Keyword(s):  
Magnetic Field ◽  
Solar Wind ◽  
Geomagnetic Storms ◽  
Ionospheric Disturbance ◽  
Causative Factor ◽  
Main Phase ◽  
Magnetic Data ◽  
Solar Wind Parameters ◽  
Mass Ejection ◽  
Magnetic Observatories

Coronal mass ejection (CME) and Corotating interaction region (CIR), a dynamic phenomenon associated with the sun, is widely acknowledged as the main causative factor for the occurrence of the geomagnetic storms. In the present investigation, we studied the influence of solar wind parameters and interplanetary magnetic field (IMFBz) on two severe geomagnetic storms (Dst=<-200 nT) occurred during March and June 2015 using magnetic data recorded at four low latitude Indian magnetic observatories namely Jaipur (Rajasthan), Desalpar (Gujarat), Alibag (Maharastra) and Hyderabad (Telangana). Residual H-component of magnetic field distinctly distinguish the different phases of storms. Solar wind density and pressure are more influencive factors during main phase of the magnetic storm with observed high MS coherence (>0.8) with the H-comp. Dynamic spectrum of H-component of magnetic field at low latitudes and solar wind parameters reveals a burst-like nature during the main phase of these storms. During Ionospheric Disturbance Dynamo (Ddyn) for March Storm, it is observed that American sector has downward movement in H-component of magnetic field and prominent attenuation of EEJ in African and Indian sectors. Similarly, for June storm, downward H-component movement is observed in both the American and African sectors and attenuation of EEJ at Indian sector.

H-Cmes and Ii-Type Radio Bursts Related Intense Geomagnetic Storms in Relation With Interplanetary Magnetic Field and Solar Wind Disturbances

2012 ◽  
Vol 2 (10) ◽  
pp. 1-3 ◽  
Author(s):  
Praveen Kumar Gupta ◽  
◽  
Puspraj Singh Puspraj Singh ◽  
Puspraj Singh Puspraj Singh ◽  
P. K. Chamadia P. K. Chamadia
Keyword(s):  
Magnetic Field ◽  
Solar Wind ◽  
Interplanetary Magnetic Field ◽  
Geomagnetic Storms ◽  
Radio Bursts ◽  
Solar Wind Disturbances

scholarly journals Specific interplanetary conditions for CIR-, Sheath-, and ICME-induced geomagnetic storms obtained by double superposed epoch analysis

2010 ◽  
Vol 28 (12) ◽  
pp. 2177-2186 ◽  
Author(s):  
Yu. I. Yermolaev ◽  
N. S. Nikolaeva ◽  
I. G. Lodkina ◽  
M. Yu. Yermolaev
Keyword(s):  
Large Scale ◽  
Geomagnetic Storms ◽  
Magnetic Cloud ◽  
Magnetic Storms ◽  
Main Phase ◽  
Corotating Interaction Region ◽  
Superposed Epoch Analysis ◽  
Epoch Analysis ◽  
Archive Data ◽  
Scale Types

Abstract. A comparison of specific interplanetary conditions for 798 magnetic storms with Dst <−50 nT during 1976–2000 was made on the basis of the OMNI archive data. We categorized various large-scale types of solar wind as interplanetary drivers of storms: corotating interaction region (CIR), Sheath, interplanetary CME (ICME) including both magnetic cloud (MC) and Ejecta, separately MC and Ejecta, and "Indeterminate" type. The data processing was carried out by the method of double superposed epoch analysis which uses two reference times (onset of storm and minimum of Dst index) and makes a re-scaling of the main phase of the storm in a such way that all storms have equal durations of the main phase in the new time reference frame. This method reproduced some well-known results and allowed us to obtain some new results. Specifically, obtained results demonstrate that (1) in accordance with "output/input" criteria the highest efficiency in generation of magnetic storms is observed for Sheath and the lowest one for MC, and (2) there are significant differences in the properties of MC and Ejecta and in their efficiencies.

scholarly journals Study of Solar Wind and Interplanetary Magnetic Field Features Associated with Geomagnetic Storms: The Cross Wavelet Approach

2021 ◽  
Author(s):  
Sujan Prasad Gautam ◽  
Ashok Silwal ◽  
Prakash Poudel ◽  
Monika Karki ◽  
Binod Adhikari ◽  
...  
Keyword(s):  
Magnetic Field ◽  
Solar Wind ◽  
Interplanetary Magnetic Field ◽  
Geomagnetic Storms ◽  
The Cross ◽  
Cross Wavelet

Statistical relationship between solar wind conditions and geomagnetic storms in 1998–2008

2009 ◽  
Vol 57 (12) ◽  
pp. 1500-1513 ◽  
Author(s):  
D. Xu ◽  
T. Chen ◽  
X.X. Zhang ◽  
Z. Liu
Keyword(s):  
Solar Wind ◽  
Geomagnetic Storms ◽  
Statistical Relationship

scholarly journals Data derived NARMAX Dst model

2011 ◽  
Vol 29 (6) ◽  
pp. 965-971 ◽  
Author(s):  
R. J. Boynton ◽  
M. A. Balikhin ◽  
S. A. Billings ◽  
A. S. Sharma ◽  
O. A. Amariutei
Keyword(s):  
Mathematical Model ◽  
Solar Wind ◽  
System Identification ◽  
Nonlinear Systems ◽  
Wide Class ◽  
Geomagnetic Storms ◽  
Output Data ◽  
Dst Index ◽  
Geomagnetic Indices ◽  
Input And Output

Abstract. The NARMAX OLS-ERR methodology is applied to identify a mathematical model for the dynamics of the Dst index. The NARMAX OLS-ERR algorithm, which is widely used in the field of system identification, is able to identify a mathematical model for a wide class of nonlinear systems using input and output data. Solar wind-magnetosphere coupling functions, derived from analytical or data based methods, are employed as the inputs to such models and the outputs are geomagnetic indices. The newly deduced coupling function, p1/2V4/3BTsin6(θ/2), has been implemented as an input to model the Dst dynamics. It was shown that the identified model has a very good forecasting ability, especially with the geomagnetic storms.

A neural network-based mid-term prognosis of geomagnetic storms that uses pre-storm effects related to current sheets and magnetic islands

2021 ◽  
Author(s):  
Mikhail Fridman
Keyword(s):  
Solar Wind ◽  
Solar Minimum ◽  
High Speed ◽  
Geomagnetic Storms ◽  
Magnetic Islands ◽  
Solar Wind Density ◽  
Short Term ◽  
Storm Effects ◽  
Term Forecast ◽  
Advance Time

&lt;p&gt;Mid-term prognoses of geomagnetic storms require an improvement since the&amp;#1091; are known to have rather low accuracy which does not exceed 40% in solar minimum. We claim that the problem lies in the approach. Current mid-term forecasts are typically built using the same paradigm as short-term ones and suggest an analysis of the solar wind conditions typical for geomagnetic storms. According to this approach, there is a 20-60 minute delay between the arrival of a geoeffective flow/stream to L1 and the arrival of the signal from the spacecraft to Earth, which gives a necessary advance time for a short-term prognosis. For the mid-term forecast with an advance time from 3 hours to 3 days, this is not enough. Therefore, we have suggested finding precursors of geomagnetic storms observed in the solar wind. Such precursors are variations in the solar wind density and the interplanetary magnetic field in the ULF range associated with crossings of magnetic cavities in front of the arriving geoeffective high-speed streams and flows (Khabarova et al., 2015, 2016, 2018; Adhikari et al., 2019). Despite some preliminary studies have shown that this might be a perspective way to create a mid-term prognosis (Khabarova 2007; Khabarova &amp; Yermolaev, 2007), the problem of automatization of the prognosis remained unsolved.&lt;/p&gt;

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