Open AccessA Nonlinear System Science Approach to Find the Robust Solar Wind Drivers of the Multivariate Magnetosphere
Author(s) -
Blunier S.,
Toledo B.,
Rogan J.,
Valdivia J. A.
Publication year - 2021
Publication title -
space weather
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.254
H-Index - 56
ISSN - 1542-7390
DOI - 10.1029/2020sw002634
Subject(s) - solar wind , earth's magnetic field , interplanetary spaceflight , nonlinear system , artificial neural network , geomagnetic storm , magnetosphere , computer science , iterated function , algorithm , control theory (sociology) , meteorology , mathematics , physics , magnetic field , artificial intelligence , mathematical analysis , control (management) , quantum mechanics
We propose a method, based on Neural Networks, that detects the nonlinear robust interplanetary solar wind variables, with varying delays, driving the coupled behavior of three geomagnetic indices ( Dst , AL, and AU). As opposed to minimizing a prediction error, the method is based on degrading the prediction by distorting the inputs of the trained Neural Networks in order to highlight the most sensible drivers. We show that the z component of the magnetic field, the duskward oriented electric field, and the speed of the particles of the interplanetary medium, at particular time delays, seem to be the most efficient drivers of the three coupled geomagnetic indices. Using only the sensible or robust drivers in the model, we demonstrate that iterated predictions during geomagnetic storm are significantly improved from models that only use one of the outstanding drivers with multiple time delays. The derived robust nonlinear Neural Network model is also a significant improvement over linear approximations, specially when used as iterated predictors.