Comparison of Dst Index Coupling Functions

Friday, 18 December 2015
Poster Hall (Moscone South)
Hamood Al Saadi1, Richard Boynton2 and Michael A Balikhin2, (1)University of Sheffield, Sheffield, United Kingdom, (2)University of Sheffield, Sheffield, S10, United Kingdom
Local linear filter and nonlinear autoregressive with eXogenous input based on neural network (NARX) were both used to mathematically model and forecast Dst index from input-output data. Several previously proposed solar wind magnetosphere coupling functions were used as input and Dst index was used as output producing two models for each of them. The correlation coefficient and prediction efficiency were used as a mean to validate the results. Results from both methods showed that the model employing the Boynton et al [2011] solar wind magnetosphere coupling function produced the best forecast for Dst.