Prediction of SYMH and ASYH indices for geomagnetic storms of solar cycle 24 including recent St. Patrick's day, 2015 storm using NARX neural network

Abstract

Artificial Neural Network (ANN) has proven to be very successful in forecasting variety of irregular magnetospheric/ionospheric processes like geomagnetic storms and substorms. SYMH and ASYH indices represent longitudinal symmetric and 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 this effect Nonlinear Autoregressive Network with eXogenous inputs (NARX) is implemented. This network considers input history of 30 minutes and output feedback of 120 minutes. 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 9 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 minutes to good extent within noise level, indicating 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 is available. However, during the main phase of major storms, residuals (observed-modeled) are found to be large, suggesting influence of internal factors such as magnetospheric processes