Prediction of high latitude nightside Geomagnetic Field Disturbances (GGFDs) during the 9-March-2012 geomagnetic storm using Recurrent Neural Network (RNN)

Abstract

Geomagnetically Induced currents (GICs) pose a potentially catastrophic threat to the safety and security of large-scale power distribution systems. Using a recurrent neural network, we predict high (≥60°) magnetic latitude (MLAT) and night side (06-18) magnetic local time (MLT) geomagnetic field disturbances (surface dBs/dt and vertical dBz/dt), the trigger of GICs, during a geomagnetic storms occurred on March 09, 2012. We use the high latitude geomagnetic field data from SuperMAG and the solar wind particle and magnetic field data from NASA-OMNIWeb. We divide the high latitude night side of Earth’s globe into multiple sectors and obtain a time series of maximum geomagnetic disturbances observed in each sector. We use the Long Short Term Memory (LSTM) network technique sector by sector on both, the solar wind data and the sectored geomagnetic field disturbance (10° geomagnetic latitude, 1-hour MLT) time series obtained from SuperMAG. Each sector prediction is plotted on a polar map to reveal high latitude night side geomagnetic disturbance patterns. This high latitude nigh side model has great potential. With solar wind input at a Lagrangian-1 point, and geomagnetic field data from SuperMAG, it can predict the peak magnetic disturbance at any given sector for couple of hours in advance. Our machine learning techniques provide the best set of solar wind input that gives the best disturbance prediction. This paper presents a prototype of the global geomagnetic disturbance model with low-resolution spatial grids and a subset of potential solar wind input parameters. Once matured, the global magnetic disturbance model will be provided to the public.