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.

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

2009 ◽  
Vol 52 (10) ◽  
pp. 2877-2885 ◽  
Author(s):  
Lei Cai ◽  
ShuYing Ma ◽  
HongTao Cai ◽  
YunLiang Zhou ◽  
RuoSi Liu
Keyword(s):  
Neural Network ◽  
Solar Wind ◽  
Wind Data ◽  
H Index ◽  
Narx Neural Network

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 Influences on the radius of the auroral oval

2009 ◽  
Vol 27 (7) ◽  
pp. 2913-2924 ◽  
Author(s):  
S. E. Milan ◽  
J. Hutchinson ◽  
P. D. Boakes ◽  
B. Hubert
Keyword(s):  
Solar Wind ◽  
Ring Current ◽  
Geomagnetic Storms ◽  
Auroral Oval ◽  
Substorm Onset ◽  
Polar Cap ◽  
H Index ◽  
Magnetic Perturbation

Abstract. We examine the variation in the radius of the auroral oval, as measured from auroral images gathered by the Imager for Magnetopause-to-Aurora Global Exploration (IMAGE) spacecraft, in response to solar wind inputs measured by the Advanced Composition Explorer (ACE) spacecraft for the two year interval June 2000 to May 2002. Our main finding is that the oval radius increases when the ring current, as measured by the Sym-H index, is intensified during geomagnetic storms. We discuss our findings within the context of the expanding/contracting polar cap paradigm, in terms of a modification of substorm onset conditions by the magnetic perturbation associated with the ring current.

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.

Identification of the ground signatures of magnetospheric current systems as a function of latitude during intense magnetic storms

2021 ◽  
Author(s):  
Vasilis Pitsis ◽  
Georgios Balasis ◽  
Ioannis Daglis ◽  
Dimitris Vassiliadis
Keyword(s):  
Solar Wind ◽  
Magnetic Fields ◽  
Geomagnetic Field ◽  
Ring Current ◽  
Magnetic Storms ◽  
Quiet Time ◽  
Maximum Correlation ◽  
Current Form ◽  
H Index ◽  
Current Systems

&lt;p&gt;We show that changes in the magnetospheric ring current and auroral currents during the magnetic storms of March 2015 and June 2015, are recorded in several specific ways by ground magnetometers. The ring current changes are detected in geomagnetic field measurements of ground stations at magnetic mid-latitudes from -50 to +50 degrees. The auroral currents changes are detected at high magnetic latitudes from 50 to about 73 degrees. Finally, for stations between 73 and about 85 degrees the measurements of the ground magnetometers seem to be directly correlated with the convection electric field VB&lt;sub&gt;South&lt;/sub&gt; of the solar wind. Using the correlations among magnetic fields measured at stations ordered by latitude, a correlation diagram is obtained where the maximum correlation values for fields determined by the ring current form a distinct block. High-latitude magnetic fields from stations at higher latitudes, which are mainly determined by auroral currents, form a different block in the same diagram. This is in agreement with our earlier work using wavelet transforms on ground magnetic-field time series, where mid-latitude fields stations that are influenced mainly by the ring current, give a critical exponent greater than 2 while higher-latitude fields show a more complex dependence with two exponents. The maximum correlation values for mid-latitude fields correlated with the SYM-H index vary from 0.8 to 0.9, and, thus, we infer that those geomagnetic disturbances are mainly due to the ring current. The maximum correlations between the same fields and the solar wind VB&lt;sub&gt;South &lt;/sub&gt;vary from 0.5 to 0.7. Fields at magnetic latitudes between 50 and 73 degrees exhibit greater correlation values for the AL index rather than the SYM-H index. This is expected since in the auroral zone, the convection- and substorm-associated auroral electrojets contribute significantly to the deviation of the geomagnetic field from its quiet-time value. In this case, maximum correlations vary between 0.6 and 0.7 for auroral latitude stations when compared with AL, as opposed to 0.4&amp;#8211;0.5 when compared with SYM-H. Our results show how different measures of ground geomagnetic variations reflect the time evolution of several magnetospheric current systems and of the solar wind &amp;#8211; magnetosphere coupling.&lt;/p&gt;

scholarly journals Forecasting of SYMH and ASYH indices for geomagnetic storms of solar cycle 24 including St. Patrick’s day, 2015 storm using NARX neural network

2019 ◽  
Vol 9 ◽  
pp. A12
Author(s):  
Ankush Bhaskar ◽  
Geeta Vichare
Keyword(s):  
Neural Network ◽  
Solar Wind ◽  
Solar Cycle ◽  
Prediction Model ◽  
Ring Current ◽  
Geomagnetic Storms ◽  
Time Profile ◽  
Asymmetric Component ◽  
Solar Cycle 24 ◽  
Cycle 24

Artificial Neural Network (ANN) has proven to be very successful in forecasting a variety of irregular magnetospheric/ionospheric processes like geomagnetic storms and substorms. SYMH and ASYH indices represent longitudinal symmetric and the asymmetric component of the ring current. Here, an attempt is made to develop a prediction model for these indices using ANN. The ring current state depends on its past conditions therefore, it is necessary to consider its history for prediction. To account for this effect Nonlinear Autoregressive Network with exogenous inputs (NARX) is implemented. This network considers input history of 30 min and output feedback of 120 min. Solar wind parameters mainly velocity, density, and interplanetary magnetic field are used as inputs. SYMH and ASYH indices during geomagnetic storms of 1998–2013, having minimum SYMH < −85 nT are used as the target for training two independent networks. We present the prediction of SYMH and ASYH indices during nine geomagnetic storms of solar cycle 24 including the recent largest storm occurred on St. Patrick’s day, 2015. The present prediction model reproduces the entire time profile of SYMH and ASYH indices along with small variations of ∼10–30 min to the good extent within noise level, indicating a significant contribution of interplanetary sources and past state of the magnetosphere. Therefore, the developed networks can predict SYMH and ASYH indices about an hour before, provided, real-time upstream solar wind data are available. However, during the main phase of major storms, residuals (observed-modeled) are found to be large, suggesting the influence of internal factors such as magnetospheric processes.

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