Long-term prediction of solar and geomagnetic activity daily time series using singular spectrum analysis and fuzzy descriptor models

Masoud Mirmomeni,Masoud Shafiee,Caro Lucas,Elaheh Kamaliha

doi:10.1186/bf03352959

Abstract

Abstract Of the various conditions that affect space weather, Sun-driven phenomena are the most dominant. Cyclic solar activity has a significant effect on the Earth, its climate, satellites, and space missions. In recent years, space weather hazards have become a major area of investigation, especially due to the advent of satellite technology. As such, the design of reliable alerting and warning systems is of utmost importance, and international collaboration is needed to develop accurate short-term and long-term prediction methodologies. Several methods have been proposed and implemented for the prediction of solar and geomagnetic activity indices, but problems in predicting the exact time and magnitude of such catastrophic events still remain. There are, however, descriptor systems that describe a wider class of systems, including physical models and non-dynamic constraints. It is well known that the descriptor system is much tighter than the state-space expression for representing real independent parametric perturbations. In addition, the fuzzy descriptor models as a generalization of the locally linear neurofuzzy models are general forms that can be trained by constructive intuitive learning algorithms. Here, we propose a combined model based on fuzzy descriptor models and singular spectrum analysis (SSA) (FD/SSA) to forecast a number of geomagnetic activity indices in a manner that optimizes a fuzzy descriptor model for each of the principal components obtained from singular spectrum analysis and recombines the predicted values so as to transform the geomagnetic activity time series into natural chaotic phenomena. The method has been applied to predict two solar and geomagnetic activity indices: geomagnetic aa and solar wind speed (SWS) of the solar wind index. The results demonstrate the higher power of the proposed method—compared to other methods—for predicting solar activity.

Highlights

Most space weather phenomena are influenced by variations in solar activity (Vassiliadis et al, 2000)
All mentioned parameters should be measured after a violent solar explosion that triggers numerous physical processes, a part of which is responsible for the geomagnetic storm
Historical data for training the geomagnetic aa and solar wind speed (SWS) estimator should contain all of the mentioned parameters of solar flares initiating a significant or less significant geomagnetic storm (GMS)

Summary

Introduction

Most space weather phenomena are influenced by variations in solar activity (Vassiliadis et al, 2000). Many of the changes that occur in space weather originate from solar activity, which varies in an 11-year period, called the solar cycle. There are an increased number of flares during the solar maximum, and these cause a significant increase in solar cosmic ray intensity. The high-energy particles disturb communication systems and affect the lifetime of satellites. Coronal mass ejections and solar flares cause shocks in the solar wind and geomagnetic disturbances in Earth’s magnetosphere. A high rate of geomagnetic storms and substorms result in atmospheric heating and increased drag of low earth orbit (LEO) satellites (Mirmomeni et al, 2006). Accurate and reliable long-term solar activity forecasting is especially useful to space mission centers because the or-

Methods

Results

Conclusion