Time-scale dependence of solar wind-based regression models of ionospheric electrodynamics

TL;DR

This paper investigates how the effectiveness of solar wind-based regression models of ionospheric electrodynamics varies with different time scales, revealing the importance of time scale considerations in model accuracy and interpretation.

Contribution

It introduces a time scale-dependent correction factor for regression models and provides a method to estimate nightside reconnection rates from solar wind data.

Findings

Regression models differ significantly across time scales.

A correction factor improves model consistency across scales.

Method to estimate nightside reconnection rate from geomagnetic data.

Abstract

The solar wind influence on geospace can be described as the sum of a directly driven component, or dayside reconnection, and an unloading component, associated with the release of magnetic energy via nightside reconnection. The two processes are poorly correlated on short time scales, but exactly equal when averaged over long time windows. Because of this peculiar property, regression models of ionospheric electrodynamics that are based on solar wind data are time scale specific: Models derived from 1 min resolution data will be different from models derived from hourly, daily, or monthly data. We explain and quantify this effect on simple linear regression models of various geomagnetic indices. We also derive a time scale-dependent correction factor that can be used with the Average Magnetic field and Polar current System model. Finally, we show how absolute estimates of the nightside reconnection rate can be calculated from solar wind measurements and geomagnetic indices.