Study of the development of geomagnetic storms in the magnetosphere using solar wind data of three different time resolutions

2022 ◽  
Vol 367 (1) ◽  
Author(s):  
B. Badruddin ◽  
O. P. M. Aslam ◽  
M. Derouich
Keyword(s):  
Solar Wind ◽  
Geomagnetic Storms ◽  
Wind Data

scholarly journals Ring current influence on auroral electrojet predictions

1999 ◽  
Vol 17 (10) ◽  
pp. 1268-1275 ◽  
Author(s):  
H. Gleisner ◽  
H. Lundstedt
Keyword(s):  
Solar Wind ◽  
Ring Current ◽  
Geomagnetic Storms ◽  
Wind Data ◽  
Common Source ◽  
Neural Net ◽  
Operational Forecasting ◽  
Storms And Substorms ◽  
Highly Correlated ◽  
Strong Control

Abstract. Geomagnetic storms and substorms develop under strong control of the solar wind. This is demonstrated by the fact that the geomagnetic activity indices Dst and AE can be predicted from the solar wind alone. A consequence of the strong control by a common source is that substorm and storm indices tend to be highly correlated. However, a part of this correlation is likely to be an effect of internal magnetospheric processes, such as a ring-current modulation of the solar wind-AE relation. The present work extends previous studies of nonlinear AE predictions from the solar wind. It is examined whether the AE predictions are modulated by the Dst index.This is accomplished by comparing neural network predictions from Dst and the solar wind, with predictions from the solar wind alone. Two conclusions are reached: (1) with an optimal set of solar-wind data available, the AE predictions are not markedly improved by the Dst input, but (2) the AE predictions are improved by Dst if less than, or other than, the optimum solar-wind data are available to the net. It appears that the solar wind-AE relation described by an optimized neural net is not significantly modified by the magnetosphere's Dst state. When the solar wind alone is used to predict AE, the correlation between predicted and observed AE is 0.86, while the prediction residual is nearly uncorrelated to Dst. Further, the finding that Dst can partly compensate for missing information on the solar wind, is of potential importance in operational forecasting where gaps in the stream of real time solar-wind data are a common occurrence.Key words. Magnetospheric physics (solar wind · magnetosphere interactions; storms and substorms)

scholarly journals Predicting geomagnetic storms from solar-wind data using time-delay neural networks

1996 ◽  
Vol 14 (7) ◽  
pp. 679-686 ◽  
Author(s):  
H. Gleisner ◽  
H. Lundstedt ◽  
P. Wintoft
Keyword(s):  
Neural Networks ◽  
Solar Wind ◽  
Time Delay ◽  
Input Data ◽  
Ring Current ◽  
Geomagnetic Storms ◽  
Time History ◽  
Recovery Phase ◽  
Wind Data ◽  
Data Sequence

Abstract. We have used time-delay feed-forward neural networks to compute the geomagnetic-activity index Dst one hour ahead from a temporal sequence of solar-wind data. The input data include solar-wind density n, velocity V and the southward component Bz of the interplanetary magnetic field. Dst is not included in the input data. The networks implement an explicit functional relationship between the solar wind and the geomagnetic disturbance, including both direct and time-delayed non-linear relations. In this study we especially consider the influence of varying the temporal size of the input-data sequence. The networks are trained on data covering 6600 h, and tested on data covering 2100 h. It is found that the initial and main phases of geomagnetic storms are well predicted, almost independent of the length of the input-data sequence. However, to predict the recovery phase, we have to use up to 20 h of solar-wind input data. The recovery phase is mainly governed by the ring-current loss processes, and is very much dependent on the ring-current history, and thus also the solar-wind history. With due consideration of the time history when optimizing the networks, we can reproduce 84% of the Dst variance.

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 Prediction of SYM-H index during large storms by NARX neural network from IMF and solar wind data

2010 ◽  
Vol 28 (2) ◽  
pp. 381-393 ◽  
Author(s):  
L. Cai ◽  
S. Y. Ma ◽  
Y. L. Zhou
Keyword(s):  
Neural Network ◽  
Solar Wind ◽  
Correlation Coefficient ◽  
Characteristic Time ◽  
Ring Current ◽  
Wind Data ◽  
Distinct Advantage ◽  
Elman Network ◽  
H Index ◽  
Narx Neural Network

Abstract. Similar to the Dst index, the SYM-H index may also serve as an indicator of magnetic storm intensity, but having distinct advantage of higher time-resolution. In this study the NARX neural network has been used for the first time to predict SYM-H index from solar wind (SW) and IMF parameters. In total 73 time intervals of great storm events with IMF/SW data available from ACE satellite during 1998 to 2006 are used to establish the ANN model. Out of them, 67 are used to train the network and the other 6 samples for test. Additionally, the NARX prediction model is also validated using IMF/SW data from WIND satellite for 7 great storms during 1995–1997 and 2005, as well as for the July 2000 Bastille day storm and November 2001 superstorm using Geotail and OMNI data at 1 AU, respectively. Five interplanetary parameters of IMF Bz, By and total B components along with proton density and velocity of solar wind are used as the original external inputs of the neural network to predict the SYM-H index about one hour ahead. For the 6 test storms registered by ACE including two super-storms of min. SYM-H<−200 nT, the correlation coefficient between observed and NARX network predicted SYM-H is 0.95 as a whole, even as high as 0.95 and 0.98 with average relative variance of 13.2% and 7.4%, respectively, for the two super-storms. The prediction for the 7 storms with WIND data is also satisfactory, showing averaged correlation coefficient about 0.91 and RMSE of 14.2 nT. The newly developed NARX model shows much better capability than Elman network for SYM-H prediction, which can partly be attributed to a key feedback to the input layer from the output neuron with a suitable length (about 120 min). This feedback means that nearly real information of the ring current status is effectively directed to take part in the prediction of SYM-H index by ANN. The proper history length of the output-feedback may mainly reflect on average the characteristic time of ring current decay which involves various decay mechanisms with ion lifetimes from tens of minutes to tens of hours. The Elman network makes feedback from hidden layer to input only one step, which is of 5 min for SYM-H index in this work and thus insufficient to catch the characteristic time length.

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

Study of the fractality of magnetized plasma using an MHD shell model driven by solar wind data

2018 ◽  
Vol 25 (9) ◽  
pp. 092302 ◽  
Author(s):  
Macarena Domínguez ◽  
Giuseppina Nigro ◽  
Víctor Muñoz ◽  
Vincenzo Carbone
Keyword(s):  
Solar Wind ◽  
Shell Model ◽  
Magnetized Plasma ◽  
Wind Data ◽  
Model Driven

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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