J. Space Weather Space Clim.
Volume 9, 2019
System Science: Application to Space Weather Analysis, Modelling, and Forecasting
|Number of page(s)||15|
|Published online||07 May 2019|
Forecasting of SYMH and ASYH indices for geomagnetic storms of solar cycle 24 including St. Patrick’s day, 2015 storm using NARX neural network
NASA/Goddard Space Flight Center, Greenbelt, MD, USA
2 University Corporation for Atmospheric Research, Boulder, CO, USA
3 Indian Institute of Geomagnetism, New Panvel, Navi Mumbai, India
* Corresponding author: email@example.com
Accepted: 20 February 2019
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.
Key words: space weather forecasting / artificial neural network / machine learning / geomagnetic storm / ring current
© A. Bhaskar & G. Vichare, Published by EDP Sciences 2015
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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