Regression modeling method of space weather prediction

TL;DR

This paper introduces a regression-based method for space weather prediction that forecasts the Dst index up to 6 hours ahead with high accuracy and identifies new geoeffective parameters related to solar wind flow angles.

Contribution

It presents a novel regression modeling approach for space weather forecasting and uncovers two new geoeffective parameters influencing the Dst index.

Findings

Forecasts Dst index with about 90% correlation up to 6 hours ahead.

Identifies latitudinal and longitudinal flow angles as new geoeffective parameters.

Shows Dst index retains memory of past values for approximately 2000 hours.

Abstract

A regression modeling method of space weather prediction is proposed. It allows forecasting Dst index up to 6 hours ahead with about 90% correlation. It can also be used for constructing phenomenological models of interaction between the solar wind and the magnetosphere. With its help two new geoeffective parameters were found: latitudinal and longitudinal flow angles of the solar wind. It was shown that Dst index remembers its previous values for 2000 hours.